feat: metal formatting and pre-commit bump (#1038)

* feat: metal formatting and pre-commit bump

* add guards

* update

* more guards

* more guards

* smakk fix

* Refactor instantiation of ternary types in ternary.metal

* fix scan.metal

.pre-commit-config.yaml

@@ -1,11 +1,11 @@
 repos:
 -   repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v18.1.3
+    rev: v18.1.4
     hooks:
     -   id: clang-format
 # Using this mirror lets us use mypyc-compiled black, which is about 2x faster
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 24.3.0
+    rev: 24.4.2
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/isort

examples/extensions/axpby/axpby.h

@@ -33,7 +33,7 @@ array axpby(
 class Axpby : public Primitive {
  public:
   explicit Axpby(Stream stream, float alpha, float beta)
-      : Primitive(stream), alpha_(alpha), beta_(beta){};
+      : Primitive(stream), alpha_(alpha), beta_(beta) {};
 
   /**
    * A primitive must know how to evaluate itself on the CPU/GPU

examples/extensions/axpby/axpby.metal

@@ -36,8 +36,8 @@ template <typename T>
 }
 
 #define instantiate_axpby(type_name, type)                               \
-  template [[host_name("axpby_general_" #type_name)]] \
+  template [[host_name("axpby_general_" #type_name)]] [[kernel]] void    \
-  [[kernel]] void axpby_general<type>(                \
+  axpby_general<type>(                                                   \
       device const type* x [[buffer(0)]],                                \
       device const type* y [[buffer(1)]],                                \
       device type* out [[buffer(2)]],                                    \
@@ -48,8 +48,8 @@ template <typename T>
       constant const size_t* y_strides [[buffer(7)]],                    \
       constant const int& ndim [[buffer(8)]],                            \
       uint index [[thread_position_in_grid]]);                           \
-  template [[host_name("axpby_contiguous_" #type_name)]] \
+  template [[host_name("axpby_contiguous_" #type_name)]] [[kernel]] void \
-  [[kernel]] void axpby_contiguous<type>(                \
+  axpby_contiguous<type>(                                                \
       device const type* x [[buffer(0)]],                                \
       device const type* y [[buffer(1)]],                                \
       device type* out [[buffer(2)]],                                    \

mlx/allocator.h

@@ -14,7 +14,7 @@ class Buffer {
   void* ptr_;
 
  public:
-  Buffer(void* ptr) : ptr_(ptr){};
+  Buffer(void* ptr) : ptr_(ptr) {};
 
   // Get the raw data pointer from the buffer
   void* raw_ptr();

mlx/array.h

@@ -209,7 +209,7 @@ class array {
     allocator::Buffer buffer;
     deleter_t d;
     Data(allocator::Buffer buffer, deleter_t d = allocator::free)
-        : buffer(buffer), d(d){};
+        : buffer(buffer), d(d) {};
     // Not copyable
     Data(const Data& d) = delete;
     Data& operator=(const Data& d) = delete;

mlx/backend/metal/device.h

@@ -38,7 +38,7 @@ using MTLFCList =
 
 struct CommandEncoder {
   CommandEncoder(MTL::ComputeCommandEncoder* enc)
-      : enc(enc), concurrent(false){};
+      : enc(enc), concurrent(false) {};
   CommandEncoder(const CommandEncoder&) = delete;
   CommandEncoder& operator=(const CommandEncoder&) = delete;
 

mlx/backend/metal/kernels/arange.metal

@@ -12,13 +12,13 @@ template <typename T>
 }
 
 #define instantiate_arange(tname, type)                                 \
-  template [[host_name("arange" #tname)]] \
+  template [[host_name("arange" #tname)]] [[kernel]] void arange<type>( \
-  [[kernel]] void arange<type>( \
       constant const type& start,                                       \
       constant const type& step,                                        \
       device type* out,                                                 \
       uint index [[thread_position_in_grid]]);
 
+// clang-format off
 instantiate_arange(uint8, uint8_t) 
 instantiate_arange(uint16, uint16_t)
 instantiate_arange(uint32, uint32_t) 
@@ -29,4 +29,4 @@ instantiate_arange(int32, int32_t)
 instantiate_arange(int64, int64_t)
 instantiate_arange(float16, half)
 instantiate_arange(float32, float)
-instantiate_arange(bfloat16, bfloat16_t)
+instantiate_arange(bfloat16, bfloat16_t) // clang-format on

mlx/backend/metal/kernels/arg_reduce.metal

@@ -18,7 +18,8 @@ struct ArgMin {
   static constexpr constant U init = Limits<U>::max;
 
   IndexValPair<U> reduce(IndexValPair<U> best, IndexValPair<U> current) {
-    if (best.val > current.val || (best.val == current.val && best.index > current.index)) {
+    if (best.val > current.val ||
+        (best.val == current.val && best.index > current.index)) {
       return current;
     } else {
       return best;
@@ -26,11 +27,12 @@ struct ArgMin {
   }
 
   template <int N>
-  IndexValPair<U> reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
+  IndexValPair<U>
-    for (int i=0; i<N; i++) {
+  reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
+    for (int i = 0; i < N; i++) {
       if (vals[i] < best.val) {
         best.val = vals[i];
-        best.index = offset+i;
+        best.index = offset + i;
       }
     }
     return best;
@@ -42,7 +44,8 @@ struct ArgMax {
   static constexpr constant U init = Limits<U>::min;
 
   IndexValPair<U> reduce(IndexValPair<U> best, IndexValPair<U> current) {
-    if (best.val < current.val || (best.val == current.val && best.index > current.index)) {
+    if (best.val < current.val ||
+        (best.val == current.val && best.index > current.index)) {
       return current;
     } else {
       return best;
@@ -50,11 +53,12 @@ struct ArgMax {
   }
 
   template <int N>
-  IndexValPair<U> reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
+  IndexValPair<U>
-    for (int i=0; i<N; i++) {
+  reduce_many(IndexValPair<U> best, thread U* vals, uint32_t offset) {
+    for (int i = 0; i < N; i++) {
       if (vals[i] > best.val) {
         best.val = vals[i];
-        best.index = offset+i;
+        best.index = offset + i;
       }
     }
     return best;
@@ -64,19 +68,16 @@ struct ArgMax {
 template <typename U>
 IndexValPair<U> simd_shuffle_down(IndexValPair<U> data, uint16_t delta) {
   return IndexValPair<U>{
-    simd_shuffle_down(data.index, delta),
+      simd_shuffle_down(data.index, delta), simd_shuffle_down(data.val, delta)};
-    simd_shuffle_down(data.val, delta)
-  };
 }
 
-
 template <typename T, typename Op, int N_READS>
 [[kernel]] void arg_reduce_general(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device uint32_t *out [[buffer(1)]],
+    device uint32_t* out [[buffer(1)]],
-    const device int *shape [[buffer(2)]],
+    const device int* shape [[buffer(2)]],
-    const device size_t *in_strides [[buffer(3)]],
+    const device size_t* in_strides [[buffer(3)]],
-    const device size_t *out_strides [[buffer(4)]],
+    const device size_t* out_strides [[buffer(4)]],
     const device size_t& ndim [[buffer(5)]],
     const device size_t& axis_stride [[buffer(6)]],
     const device size_t& axis_size [[buffer(7)]],
@@ -86,7 +87,6 @@ template <typename T, typename Op, int N_READS>
     uint simd_size [[threads_per_simdgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-
   // Shapes and strides *do not* contain the reduction axis. The reduction size
   // and stride are provided in axis_stride and axis_size.
   //
@@ -113,13 +113,13 @@ template <typename T, typename Op, int N_READS>
   threadgroup IndexValPair<T> local_data[32];
 
   // Loop over the reduction axis in lsize*N_READS buckets
-  for (uint r=0; r < ceildiv(axis_size, N_READS*lsize); r++) {
+  for (uint r = 0; r < ceildiv(axis_size, N_READS * lsize); r++) {
     // Read the current value
-    uint32_t current_index = r*lsize*N_READS + lid*N_READS;
+    uint32_t current_index = r * lsize * N_READS + lid * N_READS;
     uint32_t offset = current_index;
-    const device T * current_in = in + in_idx + current_index * axis_stride;
+    const device T* current_in = in + in_idx + current_index * axis_stride;
     T vals[N_READS];
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
       vals[i] = (current_index < axis_size) ? *current_in : T(Op::init);
       current_index++;
       current_in += axis_stride;
@@ -130,7 +130,7 @@ template <typename T, typename Op, int N_READS>
   // need to reduce across the thread group.
 
   // First per simd reduction.
-  for (uint offset=simd_size/2; offset>0; offset/=2) {
+  for (uint offset = simd_size / 2; offset > 0; offset /= 2) {
     IndexValPair<T> neighbor = simd_shuffle_down(best, offset);
     best = op.reduce(best, neighbor);
   }
@@ -149,7 +149,7 @@ template <typename T, typename Op, int N_READS>
   if (simd_lane_id < simd_groups) {
     best = local_data[simd_lane_id];
   }
-  for (uint offset=simd_size/2; offset>0; offset/=2) {
+  for (uint offset = simd_size / 2; offset > 0; offset /= 2) {
     IndexValPair<T> neighbor = simd_shuffle_down(best, offset);
     best = op.reduce(best, neighbor);
   }
@@ -161,13 +161,13 @@ template <typename T, typename Op, int N_READS>
 }
 
 #define instantiate_arg_reduce_helper(name, itype, op) \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void         \
-  [[kernel]] void arg_reduce_general<itype, op<itype>, 4>( \
+  arg_reduce_general<itype, op<itype>, 4>(             \
-      const device itype *in [[buffer(0)]], \
+      const device itype* in [[buffer(0)]],            \
-      device uint32_t * out [[buffer(1)]], \
+      device uint32_t* out [[buffer(1)]],              \
-      const device int *shape [[buffer(2)]], \
+      const device int* shape [[buffer(2)]],           \
-      const device size_t *in_strides [[buffer(3)]], \
+      const device size_t* in_strides [[buffer(3)]],   \
-      const device size_t *out_strides [[buffer(4)]], \
+      const device size_t* out_strides [[buffer(4)]],  \
       const device size_t& ndim [[buffer(5)]],         \
       const device size_t& axis_stride [[buffer(6)]],  \
       const device size_t& axis_size [[buffer(7)]],    \
@@ -178,6 +178,7 @@ template <typename T, typename Op, int N_READS>
       uint simd_lane_id [[thread_index_in_simdgroup]], \
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
+// clang-format off
 #define instantiate_arg_reduce(name, itype)                      \
   instantiate_arg_reduce_helper("argmin_" #name , itype, ArgMin) \
   instantiate_arg_reduce_helper("argmax_" #name , itype, ArgMax)
@@ -193,4 +194,4 @@ instantiate_arg_reduce(int32, int32_t)
 instantiate_arg_reduce(int64, int64_t)
 instantiate_arg_reduce(float16, half)
 instantiate_arg_reduce(float32, float)
-instantiate_arg_reduce(bfloat16, bfloat16_t)
+instantiate_arg_reduce(bfloat16, bfloat16_t) // clang-format on

mlx/backend/metal/kernels/binary.metal

@@ -77,7 +77,8 @@ template <typename T, typename U, typename Op>
     uint3 grid_dim [[threads_per_grid]]) {
   auto a_idx = elem_to_loc_3(index, a_strides);
   auto b_idx = elem_to_loc_3(index, b_strides);
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   c[out_idx] = Op()(a[a_idx], b[b_idx]);
 }
 
@@ -92,7 +93,8 @@ template <typename T, typename U, typename Op, int DIM>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto idx = elem_to_loc_2_nd<DIM>(index, shape, a_strides, b_strides);
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   c[out_idx] = Op()(a[idx.x], b[idx.y]);
 }
 
@@ -113,16 +115,16 @@ template <typename T, typename U, typename Op>
 }
 
 #define instantiate_binary(name, itype, otype, op, bopt)                      \
-  template [[host_name(name)]] \
+  template                                                                    \
-  [[kernel]] void binary_op_##bopt<itype, otype, op>( \
+      [[host_name(name)]] [[kernel]] void binary_op_##bopt<itype, otype, op>( \
           device const itype* a,                                              \
           device const itype* b,                                              \
           device otype* c,                                                    \
           uint index [[thread_position_in_grid]]);
 
 #define instantiate_binary_g_dim(name, itype, otype, op, dims) \
-  template [[host_name(name "_" #dims)]] \
+  template [[host_name(name "_" #dims)]] [[kernel]] void       \
-  [[kernel]] void binary_op_g_nd<itype, otype, op, dims>( \
+  binary_op_g_nd<itype, otype, op, dims>(                      \
       device const itype* a,                                   \
       device const itype* b,                                   \
       device otype* c,                                         \
@@ -133,16 +135,16 @@ template <typename T, typename U, typename Op>
       uint3 grid_dim [[threads_per_grid]]);
 
 #define instantiate_binary_g_nd(name, itype, otype, op) \
-  template [[host_name(name "_1")]] \
+  template [[host_name(name "_1")]] [[kernel]] void     \
-  [[kernel]] void binary_op_g_nd1<itype, otype, op>( \
+  binary_op_g_nd1<itype, otype, op>(                    \
       device const itype* a,                            \
       device const itype* b,                            \
       device otype* c,                                  \
       constant const size_t& a_stride,                  \
       constant const size_t& b_stride,                  \
       uint index [[thread_position_in_grid]]);          \
-  template [[host_name(name "_2")]] \
+  template [[host_name(name "_2")]] [[kernel]] void     \
-  [[kernel]] void binary_op_g_nd2<itype, otype, op>( \
+  binary_op_g_nd2<itype, otype, op>(                    \
       device const itype* a,                            \
       device const itype* b,                            \
       device otype* c,                                  \
@@ -150,8 +152,8 @@ template <typename T, typename U, typename Op>
       constant const size_t b_strides[2],               \
       uint2 index [[thread_position_in_grid]],          \
       uint2 grid_dim [[threads_per_grid]]);             \
-  template [[host_name(name "_3")]] \
+  template [[host_name(name "_3")]] [[kernel]] void     \
-  [[kernel]] void binary_op_g_nd3<itype, otype, op>( \
+  binary_op_g_nd3<itype, otype, op>(                    \
       device const itype* a,                            \
       device const itype* b,                            \
       device otype* c,                                  \
@@ -162,10 +164,8 @@ template <typename T, typename U, typename Op>
   instantiate_binary_g_dim(name, itype, otype, op, 4)   \
       instantiate_binary_g_dim(name, itype, otype, op, 5)
 
-
 #define instantiate_binary_g(name, itype, otype, op)                          \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void binary_op_g<itype, otype, op>( \
-  [[kernel]] void binary_op_g<itype, otype, op>( \
       device const itype* a,                                                  \
       device const itype* b,                                                  \
       device otype* c,                                                        \
@@ -176,14 +176,16 @@ template <typename T, typename U, typename Op>
       uint3 index [[thread_position_in_grid]],                                \
       uint3 grid_dim [[threads_per_grid]]);
 
+// clang-format off
 #define instantiate_binary_all(name, tname, itype, otype, op) \
   instantiate_binary("ss" #name #tname, itype, otype, op, ss) \
   instantiate_binary("sv" #name #tname, itype, otype, op, sv) \
   instantiate_binary("vs" #name #tname, itype, otype, op, vs) \
   instantiate_binary("vv" #name #tname, itype, otype, op, vv) \
   instantiate_binary_g("g" #name #tname, itype, otype, op)    \
-  instantiate_binary_g_nd("g" #name #tname, itype, otype, op)
+  instantiate_binary_g_nd("g" #name #tname, itype, otype, op) // clang-format on
 
+// clang-format off
 #define instantiate_binary_integer(name, op)                   \
   instantiate_binary_all(name, uint8, uint8_t, uint8_t, op)    \
   instantiate_binary_all(name, uint16, uint16_t, uint16_t, op) \
@@ -192,19 +194,22 @@ template <typename T, typename U, typename Op>
   instantiate_binary_all(name, int8, int8_t, int8_t, op)       \
   instantiate_binary_all(name, int16, int16_t, int16_t, op)    \
   instantiate_binary_all(name, int32, int32_t, int32_t, op)    \
-  instantiate_binary_all(name, int64, int64_t, int64_t, op) \
+  instantiate_binary_all(name, int64, int64_t, int64_t, op) // clang-format on
 
+// clang-format off
 #define instantiate_binary_float(name, op)                \
   instantiate_binary_all(name, float16, half, half, op)   \
   instantiate_binary_all(name, float32, float, float, op) \
-  instantiate_binary_all(name, bfloat16, bfloat16_t, bfloat16_t, op)
+  instantiate_binary_all(name, bfloat16, bfloat16_t, bfloat16_t, op) // clang-format on
 
+// clang-format off
 #define instantiate_binary_types(name, op)                              \
   instantiate_binary_all(name, bool_, bool, bool, op)                   \
   instantiate_binary_integer(name, op)                                  \
   instantiate_binary_all(name, complex64, complex64_t, complex64_t, op) \
-  instantiate_binary_float(name, op)
+  instantiate_binary_float(name, op) // clang-format on
 
+// clang-format off
 #define instantiate_binary_types_bool(name, op)                \
   instantiate_binary_all(name, bool_, bool, bool, op)          \
   instantiate_binary_all(name, uint8, uint8_t, bool, op)       \
@@ -218,8 +223,9 @@ template <typename T, typename U, typename Op>
   instantiate_binary_all(name, float16, half, bool, op)        \
   instantiate_binary_all(name, float32, float, bool, op)       \
   instantiate_binary_all(name, bfloat16, bfloat16_t, bool, op) \
-  instantiate_binary_all(name, complex64, complex64_t, bool, op)
+  instantiate_binary_all(name, complex64, complex64_t, bool, op) // clang-format on
 
+// clang-format off
 instantiate_binary_types(add, Add)
 instantiate_binary_types(div, Divide)
 instantiate_binary_types_bool(eq, Equal)
@@ -253,4 +259,4 @@ instantiate_binary_all(bitwise_or, bool_, bool, bool, BitwiseOr)
 instantiate_binary_integer(bitwise_xor, BitwiseXor)
 instantiate_binary_all(bitwise_xor, bool_, bool, bool, BitwiseXor)
 instantiate_binary_integer(left_shift, LeftShift)
-instantiate_binary_integer(right_shift, RightShift)
+instantiate_binary_integer(right_shift, RightShift) // clang-format on

mlx/backend/metal/kernels/binary_two.metal

@@ -3,23 +3,37 @@
 #include <metal_integer>
 #include <metal_math>
 
-#include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/utils.h"
 
 struct FloorDivide {
-  template <typename T> T operator()(T x, T y) { return x / y; }
+  template <typename T>
-  template <> float operator()(float x, float y) { return trunc(x / y); }
+  T operator()(T x, T y) {
-  template <> half operator()(half x, half y) { return trunc(x / y); }
+    return x / y;
-  template <> bfloat16_t operator()(bfloat16_t x, bfloat16_t y) { return trunc(x / y); }
+  }
+  template <>
+  float operator()(float x, float y) {
+    return trunc(x / y);
+  }
+  template <>
+  half operator()(half x, half y) {
+    return trunc(x / y);
+  }
+  template <>
+  bfloat16_t operator()(bfloat16_t x, bfloat16_t y) {
+    return trunc(x / y);
+  }
 };
 
 struct Remainder {
   template <typename T>
-  metal::enable_if_t<metal::is_integral_v<T> & !metal::is_signed_v<T>, T> operator()(T x, T y) {
+  metal::enable_if_t<metal::is_integral_v<T> & !metal::is_signed_v<T>, T>
+  operator()(T x, T y) {
     return x % y;
   }
   template <typename T>
-  metal::enable_if_t<metal::is_integral_v<T> & metal::is_signed_v<T>, T> operator()(T x, T y) {
+  metal::enable_if_t<metal::is_integral_v<T> & metal::is_signed_v<T>, T>
+  operator()(T x, T y) {
     auto r = x % y;
     if (r != 0 && (r < 0 != y < 0)) {
       r += y;
@@ -34,7 +48,8 @@ struct Remainder {
     }
     return r;
   }
-  template <> complex64_t operator()(complex64_t x, complex64_t y) {
+  template <>
+  complex64_t operator()(complex64_t x, complex64_t y) {
     return x % y;
   }
 };
@@ -50,7 +65,6 @@ template <typename T, typename U, typename Op1, typename Op2>
   d[index] = Op2()(a[0], b[0]);
 }
 
-
 template <typename T, typename U, typename Op1, typename Op2>
 [[kernel]] void binary_op_ss(
     device const T* a,
@@ -139,7 +153,8 @@ template <typename T, typename U, typename Op1, typename Op2>
     uint3 grid_dim [[threads_per_grid]]) {
   auto a_idx = elem_to_loc_3(index, a_strides);
   auto b_idx = elem_to_loc_3(index, b_strides);
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   c[out_idx] = Op1()(a[a_idx], b[b_idx]);
   d[out_idx] = Op2()(a[a_idx], b[b_idx]);
 }
@@ -156,7 +171,8 @@ template <typename T, typename U, typename Op1, typename Op2, int DIM>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto idx = elem_to_loc_2_nd<DIM>(index, shape, a_strides, b_strides);
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   c[out_idx] = Op1()(a[idx.x], b[idx.y]);
   d[out_idx] = Op2()(a[idx.x], b[idx.y]);
 }
@@ -180,8 +196,8 @@ template <typename T, typename U, typename Op1, typename Op2>
 }
 
 #define instantiate_binary(name, itype, otype, op1, op2, bopt) \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void                 \
-  [[kernel]] void binary_op_##bopt<itype, otype, op1, op2>( \
+      binary_op_##bopt<itype, otype, op1, op2>(                \
           device const itype* a,                               \
           device const itype* b,                               \
           device otype* c,                                     \
@@ -189,8 +205,8 @@ template <typename T, typename U, typename Op1, typename Op2>
           uint index [[thread_position_in_grid]]);
 
 #define instantiate_binary_g_dim(name, itype, otype, op1, op2, dims) \
-  template [[host_name(name "_" #dims)]] \
+  template [[host_name(name "_" #dims)]] [[kernel]] void             \
-  [[kernel]] void binary_op_g_nd<itype, otype, op1, op2, dims>( \
+  binary_op_g_nd<itype, otype, op1, op2, dims>(                      \
       device const itype* a,                                         \
       device const itype* b,                                         \
       device otype* c,                                               \
@@ -201,9 +217,10 @@ template <typename T, typename U, typename Op1, typename Op2>
       uint3 index [[thread_position_in_grid]],                       \
       uint3 grid_dim [[threads_per_grid]]);
 
+// clang-format off
 #define instantiate_binary_g_nd(name, itype, otype, op1, op2) \
-  template [[host_name(name "_1")]] \
+  template [[host_name(name "_1")]] [[kernel]] void           \
-  [[kernel]] void binary_op_g_nd1<itype, otype, op1, op2>( \
+  binary_op_g_nd1<itype, otype, op1, op2>(                    \
       device const itype* a,                                  \
       device const itype* b,                                  \
       device otype* c,                                        \
@@ -211,8 +228,8 @@ template <typename T, typename U, typename Op1, typename Op2>
       constant const size_t& a_stride,                        \
       constant const size_t& b_stride,                        \
       uint index [[thread_position_in_grid]]);                \
-  template [[host_name(name "_2")]] \
+  template [[host_name(name "_2")]] [[kernel]] void           \
-  [[kernel]] void binary_op_g_nd2<itype, otype, op1, op2>( \
+  binary_op_g_nd2<itype, otype, op1, op2>(                    \
       device const itype* a,                                  \
       device const itype* b,                                  \
       device otype* c,                                        \
@@ -221,8 +238,8 @@ template <typename T, typename U, typename Op1, typename Op2>
       constant const size_t b_strides[2],                     \
       uint2 index [[thread_position_in_grid]],                \
       uint2 grid_dim [[threads_per_grid]]);                   \
-  template [[host_name(name "_3")]] \
+  template [[host_name(name "_3")]] [[kernel]] void           \
-  [[kernel]] void binary_op_g_nd3<itype, otype, op1, op2>( \
+  binary_op_g_nd3<itype, otype, op1, op2>(                    \
       device const itype* a,                                  \
       device const itype* b,                                  \
       device otype* c,                                        \
@@ -232,12 +249,11 @@ template <typename T, typename U, typename Op1, typename Op2>
       uint3 index [[thread_position_in_grid]],                \
       uint3 grid_dim [[threads_per_grid]]);                   \
   instantiate_binary_g_dim(name, itype, otype, op1, op2, 4)   \
-  instantiate_binary_g_dim(name, itype, otype, op1, op2, 5)
+  instantiate_binary_g_dim(name, itype, otype, op1, op2, 5) // clang-format on
-
 
 #define instantiate_binary_g(name, itype, otype, op1, op2) \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void             \
-  [[kernel]] void binary_op_g<itype, otype, op2, op2>( \
+  binary_op_g<itype, otype, op2, op2>(                     \
       device const itype* a,                               \
       device const itype* b,                               \
       device otype* c,                                     \
@@ -249,19 +265,22 @@ template <typename T, typename U, typename Op1, typename Op2>
       uint3 index [[thread_position_in_grid]],             \
       uint3 grid_dim [[threads_per_grid]]);
 
+// clang-format off
 #define instantiate_binary_all(name, tname, itype, otype, op1, op2) \
   instantiate_binary("ss" #name #tname, itype, otype, op1, op2, ss) \
   instantiate_binary("sv" #name #tname, itype, otype, op1, op2, sv) \
   instantiate_binary("vs" #name #tname, itype, otype, op1, op2, vs) \
   instantiate_binary("vv" #name #tname, itype, otype, op1, op2, vv) \
   instantiate_binary_g("g" #name #tname, itype, otype, op1, op2)    \
-  instantiate_binary_g_nd("g" #name #tname, itype, otype, op1, op2)
+  instantiate_binary_g_nd("g" #name #tname, itype, otype, op1, op2) // clang-format on
 
+// clang-format off
 #define instantiate_binary_float(name, op1, op2)                \
   instantiate_binary_all(name, float16, half, half, op1, op2)   \
   instantiate_binary_all(name, float32, float, float, op1, op2) \
-  instantiate_binary_all(name, bfloat16, bfloat16_t, bfloat16_t, op1, op2)
+  instantiate_binary_all(name, bfloat16, bfloat16_t, bfloat16_t, op1, op2) // clang-format on
 
+// clang-format off
 #define instantiate_binary_types(name, op1, op2)                              \
   instantiate_binary_all(name, bool_, bool, bool, op1, op2)                   \
   instantiate_binary_all(name, uint8, uint8_t, uint8_t, op1, op2)             \
@@ -275,4 +294,4 @@ template <typename T, typename U, typename Op1, typename Op2>
   instantiate_binary_all(name, complex64, complex64_t, complex64_t, op1, op2) \
   instantiate_binary_float(name, op1, op2)
 
-instantiate_binary_types(divmod, FloorDivide, Remainder)
+instantiate_binary_types(divmod, FloorDivide, Remainder) // clang-format on

mlx/backend/metal/kernels/complex.h

@@ -22,7 +22,7 @@ struct complex64_t {
   float imag;
 
   // Constructors
-  constexpr complex64_t(float real, float imag) : real(real), imag(imag){};
+  constexpr complex64_t(float real, float imag) : real(real), imag(imag) {};
 
   // Conversions to complex64_t
   template <

mlx/backend/metal/kernels/conv.metal

@@ -1,13 +1,11 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include <metal_stdlib>
 #include <metal_simdgroup>
 #include <metal_simdgroup_matrix>
 #include <metal_stdlib>
 
-
-#include "mlx/backend/metal/kernels/steel/conv/params.h"
 #include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/steel/conv/params.h"
 
 #define MLX_MTL_CONST static constant constexpr const
 
@@ -23,12 +21,13 @@ template <typename T, int N>
     device T* out [[buffer(1)]],
     const constant MLXConvParams<N>* params [[buffer(2)]],
     uint3 gid [[thread_position_in_grid]]) {
-
   int filter_size = params->C;
-  for(short i = 0; i < N; i++) filter_size *= params->wS[i];
+  for (short i = 0; i < N; i++)
+    filter_size *= params->wS[i];
 
   int out_pixels = 1;
-  for(short i = 0; i < N; i++) out_pixels *= params->oS[i];
+  for (short i = 0; i < N; i++)
+    out_pixels *= params->oS[i];
 
   // Set out
   out += gid.z * filter_size + gid.y * (params->C);
@@ -64,10 +63,10 @@ template <typename T, int N>
     wS /= params->wS[i];
   }
 
-  if(valid) {
+  if (valid) {
     size_t in_offset = n * params->in_strides[0];
 
-    for(int i = 0; i < N; ++i) {
+    for (int i = 0; i < N; ++i) {
       in_offset += is[i] * params->in_strides[i + 1];
     }
 
@@ -85,12 +84,13 @@ template <typename T, int N>
     device T* out [[buffer(1)]],
     const constant MLXConvParams<N>* params [[buffer(2)]],
     uint3 gid [[thread_position_in_grid]]) {
-
   int filter_size = params->C;
-  for(short i = 0; i < N; i++) filter_size *= params->wS[i];
+  for (short i = 0; i < N; i++)
+    filter_size *= params->wS[i];
 
   int out_pixels = 1;
-  for(short i = 0; i < N; i++) out_pixels *= params->oS[i];
+  for (short i = 0; i < N; i++)
+    out_pixels *= params->oS[i];
 
   // Set out
   out += gid.z * filter_size + gid.x * (filter_size / params->C);
@@ -128,10 +128,10 @@ template <typename T, int N>
     out += ws_ * params->str[i];
   }
 
-  if(valid) {
+  if (valid) {
     size_t in_offset = n * params->in_strides[0];
 
-    for(int i = 0; i < N; ++i) {
+    for (int i = 0; i < N; ++i) {
       in_offset += is[i] * params->in_strides[i + 1];
     }
 
@@ -142,23 +142,23 @@ template <typename T, int N>
 }
 
 #define instantiate_naive_unfold_nd(name, itype, n)                            \
-  template [[host_name("naive_unfold_nd_" #name "_" #n)]] \
+  template [[host_name("naive_unfold_nd_" #name "_" #n)]] [[kernel]] void      \
-  [[kernel]] void naive_unfold_Nd( \
+  naive_unfold_Nd(                                                             \
       const device itype* in [[buffer(0)]],                                    \
       device itype* out [[buffer(1)]],                                         \
       const constant MLXConvParams<n>* params [[buffer(2)]],                   \
       uint3 gid [[thread_position_in_grid]]);                                  \
-  template [[host_name("naive_unfold_transpose_nd_" #name "_" #n)]] \
+  template                                                                     \
-  [[kernel]] void naive_unfold_transpose_Nd( \
+      [[host_name("naive_unfold_transpose_nd_" #name "_" #n)]] [[kernel]] void \
+      naive_unfold_transpose_Nd(                                               \
           const device itype* in [[buffer(0)]],                                \
           device itype* out [[buffer(1)]],                                     \
           const constant MLXConvParams<n>* params [[buffer(2)]],               \
           uint3 gid [[thread_position_in_grid]]);
 
 #define instantiate_naive_unfold_nd_dims(name, itype)                      \
-  instantiate_naive_unfold_nd(name, itype, 1) \
+  instantiate_naive_unfold_nd(name, itype, 1) instantiate_naive_unfold_nd( \
-  instantiate_naive_unfold_nd(name, itype, 2) \
+      name, itype, 2) instantiate_naive_unfold_nd(name, itype, 3)
-  instantiate_naive_unfold_nd(name, itype, 3)
 
 instantiate_naive_unfold_nd_dims(float32, float);
 instantiate_naive_unfold_nd_dims(float16, half);
@@ -168,7 +168,8 @@ instantiate_naive_unfold_nd_dims(bfloat16, bfloat16_t);
 /// Slow and naive conv2d kernels
 ///////////////////////////////////////////////////////////////////////////////
 
-template <typename T, 
+template <
+    typename T,
     const int BM, /* Threadgroup rows (in threads) */
     const int BN, /* Threadgroup cols (in threads) */
     const int TM, /* Thread rows (in elements) */
@@ -183,7 +184,6 @@ template <typename T,
     uint3 lid [[thread_position_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-  
   (void)simd_gid;
   (void)simd_lid;
 
@@ -196,64 +196,66 @@ template <typename T,
   int out_h[TM];
   int out_w[TN];
 
-  for(int m = 0; m < TM; ++m) {
+  for (int m = 0; m < TM; ++m) {
     int mm = (out_hw + m);
     out_h[m] = mm / params.oS[1];
     out_w[m] = mm % params.oS[1];
   }
 
-
   T in_local[TM];
   T wt_local[TN];
   T out_local[TM * TN] = {T(0)};
 
-  for(int h = 0; h < params.wS[0]; ++h) {
+  for (int h = 0; h < params.wS[0]; ++h) {
-    for(int w = 0; w < params.wS[1]; ++w) {
+    for (int w = 0; w < params.wS[1]; ++w) {
-      for(int c = 0; c < params.C; ++c) {
+      for (int c = 0; c < params.C; ++c) {
-
         // Local in
-        for(int m = 0; m < TM; m++) {
+        for (int m = 0; m < TM; m++) {
           int i = out_h[m] * params.str[0] - params.pad[0] + h * params.kdil[0];
           int j = out_w[m] * params.str[1] - params.pad[1] + w * params.kdil[1];
 
           bool valid = i >= 0 && i < params.iS[0] && j >= 0 && j < params.iS[1];
-          in_local[m] = valid ? in[i * params.in_strides[1] + j * params.in_strides[2] + c] : T(0);
+          in_local[m] = valid
+              ? in[i * params.in_strides[1] + j * params.in_strides[2] + c]
+              : T(0);
         }
 
         // Load weight
         for (int n = 0; n < TN; ++n) {
           int o = out_o + n;
-          wt_local[n] = o < params.O ? wt[o * params.wt_strides[0] + 
+          wt_local[n] = o < params.O
-                                          h * params.wt_strides[1] + 
+              ? wt[o * params.wt_strides[0] + h * params.wt_strides[1] +
-                                          w * params.wt_strides[2] + c] : T(0);
+                   w * params.wt_strides[2] + c]
+              : T(0);
         }
 
         // Accumulate
-        for(int m = 0; m < TM; ++m) {
+        for (int m = 0; m < TM; ++m) {
-          for(int n = 0; n < TN; ++n) {
+          for (int n = 0; n < TN; ++n) {
             out_local[m * TN + n] += in_local[m] * wt_local[n];
           }
         }
-
       }
     }
   }
 
-  for(int m = 0; m < TM; ++m) {
+  for (int m = 0; m < TM; ++m) {
-    for(int n = 0; n < TN; ++n) {
+    for (int n = 0; n < TN; ++n) {
-      if(out_h[m] < params.oS[0] && out_w[m] < params.oS[1] && (out_o + n) < params.O)
+      if (out_h[m] < params.oS[0] && out_w[m] < params.oS[1] &&
+          (out_o + n) < params.O)
         out[out_h[m] * params.out_strides[1] +
-          out_w[m] * params.out_strides[2] + out_o + n] = out_local[m * TN + n];
+            out_w[m] * params.out_strides[2] + out_o + n] =
+            out_local[m * TN + n];
     }
   }
-
 }
 
 // Instantiations
 
 #define instantiate_naive_conv_2d(name, itype, bm, bn, tm, tn)              \
-  template [[host_name("naive_conv_2d_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn)]] \
+  template [[host_name("naive_conv_2d_" #name "_bm" #bm "_bn" #bn "_tm" #tm \
-  [[kernel]] void naive_conv_2d<itype, bm, bn, tm, tn>( \
+                       "_tn" #tn)]] [[kernel]] void                         \
+  naive_conv_2d<itype, bm, bn, tm, tn>(                                     \
       const device itype* in [[buffer(0)]],                                 \
       const device itype* wt [[buffer(1)]],                                 \
       device itype* out [[buffer(2)]],                                      \
@@ -276,9 +278,7 @@ instantiate_naive_conv_2d_blocks(bfloat16, bfloat16_t);
 ///////////////////////////////////////////////////////////////////////////////
 
 template <int M, int R, int S>
-struct WinogradTransforms {
+struct WinogradTransforms {};
-
-};
 
 template <>
 struct WinogradTransforms<6, 3, 8> {
@@ -287,36 +287,36 @@ struct WinogradTransforms<6, 3, 8> {
   MLX_MTL_CONST int IN_TILE_SIZE = OUT_TILE_SIZE + FILTER_SIZE - 1;
   MLX_MTL_CONST int SIMD_MATRIX_SIZE = 8;
   MLX_MTL_CONST float in_transform[SIMD_MATRIX_SIZE][SIMD_MATRIX_SIZE] = {
-    { 1.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f},
+      {1.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f},
-    { 0.00f,  1.00f, -1.00f,  0.50f, -0.50f,  2.00f, -2.00f, -1.00f},
+      {0.00f, 1.00f, -1.00f, 0.50f, -0.50f, 2.00f, -2.00f, -1.00f},
       {-5.25f, 1.00f, 1.00f, 0.25f, 0.25f, 4.00f, 4.00f, 0.00f},
-    { 0.00f, -4.25f,  4.25f, -2.50f,  2.50f, -2.50f,  2.50f,  5.25f},
+      {0.00f, -4.25f, 4.25f, -2.50f, 2.50f, -2.50f, 2.50f, 5.25f},
-    { 5.25f, -4.25f, -4.25f, -1.25f, -1.25f, -5.00f, -5.00f,  0.00f},
+      {5.25f, -4.25f, -4.25f, -1.25f, -1.25f, -5.00f, -5.00f, 0.00f},
-    { 0.00f,  1.00f, -1.00f,  2.00f, -2.00f,  0.50f, -0.50f, -5.25f},
+      {0.00f, 1.00f, -1.00f, 2.00f, -2.00f, 0.50f, -0.50f, -5.25f},
       {-1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 0.00f},
-    { 0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  0.00f,  1.00f},
+      {0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 1.00f},
   };
 
   MLX_MTL_CONST float out_transform[SIMD_MATRIX_SIZE][SIMD_MATRIX_SIZE] = {
-    { 1.00f,  0.00f,  0.00f,   0.00f,    0.00f,    0.00f},
+      {1.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f},
-    { 1.00f,  1.00f,  1.00f,   1.00f,    1.00f,    1.00f},
+      {1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f},
-    { 1.00f, -1.00f,  1.00f,  -1.00f,    1.00f,   -1.00f},
+      {1.00f, -1.00f, 1.00f, -1.00f, 1.00f, -1.00f},
-    { 1.00f,  2.00f,  4.00f,   8.00f,   16.00f,   32.00f},
+      {1.00f, 2.00f, 4.00f, 8.00f, 16.00f, 32.00f},
-    { 1.00f, -2.00f,  4.00f,  -8.00f,   16.00f,  -32.00f},
+      {1.00f, -2.00f, 4.00f, -8.00f, 16.00f, -32.00f},
-    { 1.00f,  0.50f,  0.25f,  0.125f,   0.0625f,   0.03125f},
+      {1.00f, 0.50f, 0.25f, 0.125f, 0.0625f, 0.03125f},
-    { 1.00f, -0.50f,  0.25f, -0.125f,   0.0625f,  -0.03125f},
+      {1.00f, -0.50f, 0.25f, -0.125f, 0.0625f, -0.03125f},
-    { 0.00f,  0.00f,  0.00f,  0.00f,      0.00f,   1.00f},
+      {0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 1.00f},
   };
 
   MLX_MTL_CONST float wt_transform[SIMD_MATRIX_SIZE][SIMD_MATRIX_SIZE] = {
-    {      1.00,       0.00,       0.00},
+      {1.00, 0.00, 0.00},
-    { -2.0/9.00,  -2.0/9.00,  -2.0/9.00},
+      {-2.0 / 9.00, -2.0 / 9.00, -2.0 / 9.00},
-    { -2.0/9.00,   2.0/9.00,  -2.0/9.00},
+      {-2.0 / 9.00, 2.0 / 9.00, -2.0 / 9.00},
-    {  1.0/90.0,   1.0/45.0,   2.0/45.0},
+      {1.0 / 90.0, 1.0 / 45.0, 2.0 / 45.0},
-    {  1.0/90.0,  -1.0/45.0,   2.0/45.0},
+      {1.0 / 90.0, -1.0 / 45.0, 2.0 / 45.0},
-    { 32.0/45.0,  16.0/45.0,   8.0/45.0},
+      {32.0 / 45.0, 16.0 / 45.0, 8.0 / 45.0},
-    { 32.0/45.0, -16.0/45.0,   8.0/45.0},
+      {32.0 / 45.0, -16.0 / 45.0, 8.0 / 45.0},
-    {      0.00,       0.00,       1.00},
+      {0.00, 0.00, 1.00},
   };
 };
 
@@ -324,12 +324,9 @@ constant constexpr const float WinogradTransforms<6, 3, 8>::wt_transform[8][8];
 constant constexpr const float WinogradTransforms<6, 3, 8>::in_transform[8][8];
 constant constexpr const float WinogradTransforms<6, 3, 8>::out_transform[8][8];
 
-template <typename T,
+template <typename T, int BC = 32, int BO = 4, int M = 6, int R = 3>
-          int BC = 32,
+[[kernel, max_total_threads_per_threadgroup(BO * 32)]] void
-          int BO = 4,
+winograd_conv_2d_weight_transform(
-          int M = 6,
-          int R = 3>
-[[kernel, max_total_threads_per_threadgroup(BO * 32)]] void winograd_conv_2d_weight_transform(
     const device T* wt_in [[buffer(0)]],
     device T* wt_out [[buffer(1)]],
     const constant int& C [[buffer(2)]],
@@ -337,7 +334,6 @@ template <typename T,
     uint tid [[threadgroup_position_in_grid]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]]) {
-
   using WGT = WinogradTransforms<M, R, 8>;
 
   // Get lane position in simdgroup
@@ -369,12 +365,12 @@ template <typename T,
   threadgroup T Ws[BO][R][R][BC];
 
   // Loop over C
-  for(int bc = 0; bc < C; bc += BC) {
+  for (int bc = 0; bc < C; bc += BC) {
     threadgroup_barrier(mem_flags::mem_threadgroup);
     // Read into shared memory
-    for(int kh = 0; kh < R; ++kh) {
+    for (int kh = 0; kh < R; ++kh) {
-      for(int kw = 0; kw < R; ++kw) {
+      for (int kw = 0; kw < R; ++kw) {
-        for(int kc = simd_lane_id; kc < BC; kc += 32) {
+        for (int kc = simd_lane_id; kc < BC; kc += 32) {
           Ws[simd_group_id][kh][kw][kc] = wt_in[kh * R * C + kw * C + kc];
         }
       }
@@ -382,10 +378,12 @@ template <typename T,
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
     // Do transform and store the result
-    for(int c = 0; c < BC; ++c) {
+    for (int c = 0; c < BC; ++c) {
       simdgroup_matrix<T, 8, 8> g;
-      g.thread_elements()[0] = sm < R && sn < R ? Ws[simd_group_id][sm][sn][c] : T(0);
+      g.thread_elements()[0] =
-      g.thread_elements()[1] = sm < R && sn + 1 < R ? Ws[simd_group_id][sm][sn + 1][c] : T(0);
+          sm < R && sn < R ? Ws[simd_group_id][sm][sn][c] : T(0);
+      g.thread_elements()[1] =
+          sm < R && sn + 1 < R ? Ws[simd_group_id][sm][sn + 1][c] : T(0);
 
       simdgroup_matrix<T, 8, 8> g_out = (G * g) * Gt;
       wt_out_0[c * O] = g_out.thread_elements()[0];
@@ -396,27 +394,23 @@ template <typename T,
     wt_out_0 += BC * O;
     wt_out_1 += BC * O;
   }
-
 }
 
 #define instantiate_winograd_conv_2d_weight_transform_base(name, itype, bc) \
-  template [[host_name("winograd_conv_2d_weight_transform_" #name "_bc" #bc)]]\
+  template [[host_name("winograd_conv_2d_weight_transform_" #name           \
-  [[kernel]] void winograd_conv_2d_weight_transform<itype, bc>(\
+                       "_bc" #bc)]] [[kernel]] void                         \
-      const device itype* wt_in [[buffer(0)]],\
+  winograd_conv_2d_weight_transform<itype, bc>(                             \
-      device itype* wt_out [[buffer(1)]],\
+      const device itype* wt_in [[buffer(0)]],                              \
-      const constant int& C [[buffer(2)]],\
+      device itype* wt_out [[buffer(1)]],                                   \
-      const constant int& O [[buffer(3)]],\
+      const constant int& C [[buffer(2)]],                                  \
-      uint tid [[threadgroup_position_in_grid]],\
+      const constant int& O [[buffer(3)]],                                  \
-      uint simd_group_id [[simdgroup_index_in_threadgroup]],\
+      uint tid [[threadgroup_position_in_grid]],                            \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],                \
       uint simd_lane_id [[thread_index_in_simdgroup]]);
 
-template <typename T,
+template <typename T, int BC, int WM, int WN, int M = 6, int R = 3>
-          int BC,
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void
-          int WM,
+winograd_conv_2d_input_transform(
-          int WN,
-          int M = 6,
-          int R = 3>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void winograd_conv_2d_input_transform(
     const device T* inp_in [[buffer(0)]],
     device T* inp_out [[buffer(1)]],
     const constant MLXConvParams<2>& params [[buffer(2)]],
@@ -425,7 +419,6 @@ template <typename T,
     uint3 tgp_per_grid [[threadgroups_per_grid]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]]) {
-
   (void)lid;
 
   using WGT = WinogradTransforms<M, R, 8>;
@@ -456,38 +449,40 @@ template <typename T,
   int bw = M * tid.x + kw;
 
   // Move to the correct input tile
-  inp_in += tid.z * params.in_strides[0] 
+  inp_in += tid.z * params.in_strides[0] + bh * params.in_strides[1] +
-             + bh * params.in_strides[1]
+      bw * params.in_strides[2];
-             + bw * params.in_strides[2];
 
   // Pre compute strides
   int jump_in[TH][TW];
 
-  for(int h = 0; h < TH; h++) {
+  for (int h = 0; h < TH; h++) {
-    for(int w = 0; w < TW; w++) {
+    for (int w = 0; w < TW; w++) {
       jump_in[h][w] = h * params.in_strides[1] + w * params.in_strides[2];
     }
   }
 
   // inp_out is stored interleaved (A x A x tiles x C)
   size_t N_TILES = tgp_per_grid.x * tgp_per_grid.y * tgp_per_grid.z;
-  size_t tile_id = tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
+  size_t tile_id =
+      tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
   size_t ohw_0 = sm * 8 + sn;
   size_t ohw_1 = sm * 8 + sn + 1;
-  device T* inp_out_0 = inp_out + ohw_0 * N_TILES * params.C + tile_id * params.C;
+  device T* inp_out_0 =
-  device T* inp_out_1 = inp_out + ohw_1 * N_TILES * params.C + tile_id * params.C;
+      inp_out + ohw_0 * N_TILES * params.C + tile_id * params.C;
+  device T* inp_out_1 =
+      inp_out + ohw_1 * N_TILES * params.C + tile_id * params.C;
 
   // Prepare shared memory
   threadgroup T Is[A][A][BC];
 
   // Loop over C
-  for(int bc = 0; bc < params.C; bc += BC) {
+  for (int bc = 0; bc < params.C; bc += BC) {
     threadgroup_barrier(mem_flags::mem_threadgroup);
     // Read into shared memory
-    for(int h = 0; h < TH; h++) {
+    for (int h = 0; h < TH; h++) {
-      for(int w = 0; w < TW; w++) {
+      for (int w = 0; w < TW; w++) {
         const device T* in_ptr = inp_in + jump_in[h][w];
-        for(int c = simd_lane_id; c < BC; c += 32) {
+        for (int c = simd_lane_id; c < BC; c += 32) {
           Is[kh + h][kw + w][c] = in_ptr[c];
         }
       }
@@ -495,7 +490,7 @@ template <typename T,
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
     // Do transform and store the result
-    for(int c = simd_group_id; c < BC; c += N_SIMD_GROUPS) {
+    for (int c = simd_group_id; c < BC; c += N_SIMD_GROUPS) {
       simdgroup_matrix<T, 8, 8> I;
       I.thread_elements()[0] = Is[sm][sn][c];
       I.thread_elements()[1] = Is[sm][sn + 1][c];
@@ -509,28 +504,24 @@ template <typename T,
     inp_out_0 += BC;
     inp_out_1 += BC;
   }
-
 }
 
 #define instantiate_winograd_conv_2d_input_transform(name, itype, bc) \
-  template [[host_name("winograd_conv_2d_input_transform_" #name "_bc" #bc)]]\
+  template [[host_name("winograd_conv_2d_input_transform_" #name      \
-  [[kernel]] void winograd_conv_2d_input_transform<itype, bc, 2, 2>(\
+                       "_bc" #bc)]] [[kernel]] void                   \
-      const device itype* inp_in [[buffer(0)]],\
+  winograd_conv_2d_input_transform<itype, bc, 2, 2>(                  \
-      device itype* inp_out [[buffer(1)]],\
+      const device itype* inp_in [[buffer(0)]],                       \
-      const constant MLXConvParams<2>& params [[buffer(2)]],\
+      device itype* inp_out [[buffer(1)]],                            \
-      uint3 tid [[threadgroup_position_in_grid]],\
+      const constant MLXConvParams<2>& params [[buffer(2)]],          \
-      uint3 lid [[thread_position_in_threadgroup]],\
+      uint3 tid [[threadgroup_position_in_grid]],                     \
-      uint3 tgp_per_grid [[threadgroups_per_grid]],\
+      uint3 lid [[thread_position_in_threadgroup]],                   \
-      uint simd_group_id [[simdgroup_index_in_threadgroup]],\
+      uint3 tgp_per_grid [[threadgroups_per_grid]],                   \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],          \
       uint simd_lane_id [[thread_index_in_simdgroup]]);
 
-template <typename T,
+template <typename T, int BO, int WM, int WN, int M = 6, int R = 3>
-          int BO,
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void
-          int WM,
+winograd_conv_2d_output_transform(
-          int WN,
-          int M = 6,
-          int R = 3>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void winograd_conv_2d_output_transform(
     const device T* out_in [[buffer(0)]],
     device T* out_out [[buffer(1)]],
     const constant MLXConvParams<2>& params [[buffer(2)]],
@@ -539,7 +530,6 @@ template <typename T,
     uint3 tgp_per_grid [[threadgroups_per_grid]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]]) {
-
   (void)lid;
 
   using WGT = WinogradTransforms<M, R, 8>;
@@ -572,57 +562,59 @@ template <typename T,
   int bw = M * tid.x + kw;
 
   // Move to the correct input tile
-  out_out += tid.z * params.out_strides[0] 
+  out_out += tid.z * params.out_strides[0] + bh * params.out_strides[1] +
-              + bh * params.out_strides[1]
+      bw * params.out_strides[2];
-              + bw * params.out_strides[2];
 
   // Pre compute strides
   int jump_in[TH][TW];
 
-  for(int h = 0; h < TH; h++) {
+  for (int h = 0; h < TH; h++) {
-    for(int w = 0; w < TW; w++) {
+    for (int w = 0; w < TW; w++) {
       bool valid = ((bh + h) < params.oS[0]) && ((bw + w) < params.oS[1]);
-      jump_in[h][w] = valid ? h * params.out_strides[1] + w * params.out_strides[2] : -1;
+      jump_in[h][w] =
+          valid ? h * params.out_strides[1] + w * params.out_strides[2] : -1;
     }
   }
 
   // out_in is stored interleaved (A x A x tiles x O)
   size_t N_TILES = tgp_per_grid.x * tgp_per_grid.y * tgp_per_grid.z;
-  size_t tile_id = tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
+  size_t tile_id =
+      tid.z * tgp_per_grid.x * tgp_per_grid.y + tid.y * tgp_per_grid.x + tid.x;
   size_t ohw_0 = sm * 8 + sn;
   size_t ohw_1 = sm * 8 + sn + 1;
-  const device T* out_in_0 = out_in + ohw_0 * N_TILES * params.O + tile_id * params.O;
+  const device T* out_in_0 =
-  const device T* out_in_1 = out_in + ohw_1 * N_TILES * params.O + tile_id * params.O;
+      out_in + ohw_0 * N_TILES * params.O + tile_id * params.O;
+  const device T* out_in_1 =
+      out_in + ohw_1 * N_TILES * params.O + tile_id * params.O;
 
   // Prepare shared memory
   threadgroup T Os[M][M][BO];
 
   // Loop over O
-  for(int bo = 0; bo < params.O; bo += BO) {
+  for (int bo = 0; bo < params.O; bo += BO) {
-
     threadgroup_barrier(mem_flags::mem_threadgroup);
     // Do transform and store the result
-    for(int c = simd_group_id; c < BO; c += N_SIMD_GROUPS) {
+    for (int c = simd_group_id; c < BO; c += N_SIMD_GROUPS) {
       simdgroup_matrix<T, 8, 8> O_mat;
       O_mat.thread_elements()[0] = out_in_0[c];
       O_mat.thread_elements()[1] = out_in_1[c];
 
       simdgroup_matrix<T, 8, 8> O_out = (Bt * (O_mat * B));
-      if((sm < M) && (sn < M)) {
+      if ((sm < M) && (sn < M)) {
         Os[sm][sn][c] = O_out.thread_elements()[0];
       }
-      if((sm < M) && ((sn + 1) < M)) {
+      if ((sm < M) && ((sn + 1) < M)) {
         Os[sm][sn + 1][c] = O_out.thread_elements()[1];
       }
     }
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
     // Read out from shared memory
-    for(int h = 0; h < TH; h++) {
+    for (int h = 0; h < TH; h++) {
-      for(int w = 0; w < TW; w++) {
+      for (int w = 0; w < TW; w++) {
-        if(jump_in[h][w] >= 0) {
+        if (jump_in[h][w] >= 0) {
           device T* out_ptr = out_out + jump_in[h][w];
-          for(int c = simd_lane_id; c < BO; c += 32) {
+          for (int c = simd_lane_id; c < BO; c += 32) {
             out_ptr[c] = Os[kh + h][kw + w][c];
           }
         }
@@ -633,25 +625,27 @@ template <typename T,
     out_in_0 += BO;
     out_in_1 += BO;
   }
-
 }
 
 #define instantiate_winograd_conv_2d_output_transform(name, itype, bo) \
-  template [[host_name("winograd_conv_2d_output_transform_" #name "_bo" #bo)]]\
+  template [[host_name("winograd_conv_2d_output_transform_" #name      \
-  [[kernel]] void winograd_conv_2d_output_transform<itype, bo, 2, 2>(\
+                       "_bo" #bo)]] [[kernel]] void                    \
-      const device itype* out_in [[buffer(0)]],\
+  winograd_conv_2d_output_transform<itype, bo, 2, 2>(                  \
-      device itype* out_out [[buffer(1)]],\
+      const device itype* out_in [[buffer(0)]],                        \
-      const constant MLXConvParams<2>& params [[buffer(2)]],\
+      device itype* out_out [[buffer(1)]],                             \
-      uint3 tid [[threadgroup_position_in_grid]],\
+      const constant MLXConvParams<2>& params [[buffer(2)]],           \
-      uint3 lid [[thread_position_in_threadgroup]],\
+      uint3 tid [[threadgroup_position_in_grid]],                      \
-      uint3 tgp_per_grid [[threadgroups_per_grid]],\
+      uint3 lid [[thread_position_in_threadgroup]],                    \
-      uint simd_group_id [[simdgroup_index_in_threadgroup]],\
+      uint3 tgp_per_grid [[threadgroups_per_grid]],                    \
+      uint simd_group_id [[simdgroup_index_in_threadgroup]],           \
       uint simd_lane_id [[thread_index_in_simdgroup]]);
 
+// clang-format off
 #define instantiate_winograd_conv_2d(name, itype)                     \
   instantiate_winograd_conv_2d_weight_transform_base(name, itype, 32) \
   instantiate_winograd_conv_2d_input_transform(name, itype, 32)       \
-  instantiate_winograd_conv_2d_output_transform(name, itype, 32)
+  instantiate_winograd_conv_2d_output_transform(name, itype, 32) // clang-format on
 
+// clang-format off
 instantiate_winograd_conv_2d(float32, float);
-instantiate_winograd_conv_2d(float16, half);
+instantiate_winograd_conv_2d(float16, half); // clang-format on

mlx/backend/metal/kernels/copy.metal

@@ -49,7 +49,8 @@ template <typename T, typename U>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto src_idx = elem_to_loc_3(index, src_strides);
-  int64_t dst_idx = index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
+  int64_t dst_idx =
+      index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
@@ -62,7 +63,8 @@ template <typename T, typename U, int DIM>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto src_idx = elem_to_loc_nd<DIM>(index, src_shape, src_strides);
-  int64_t dst_idx = index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
+  int64_t dst_idx =
+      index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
@@ -76,7 +78,8 @@ template <typename T, typename U>
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
   auto src_idx = elem_to_loc(index, src_shape, src_strides, ndim);
-  int64_t dst_idx = index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
+  int64_t dst_idx =
+      index.x + (int64_t)grid_dim.x * (index.y + (int64_t)grid_dim.y * index.z);
   dst[dst_idx] = static_cast<U>(src[src_idx]);
 }
 
@@ -144,23 +147,22 @@ template <typename T, typename U>
 }
 
 #define instantiate_copy(name, itype, otype, ctype)                        \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void copy_##ctype<itype, otype>( \
-  [[kernel]] void copy_##ctype<itype, otype>( \
       device const itype* src [[buffer(0)]],                               \
       device otype* dst [[buffer(1)]],                                     \
       uint index [[thread_position_in_grid]]);
 
 #define instantiate_copy_g_dim(name, itype, otype, dims)     \
-  template [[host_name(name "_" #dims)]] \
+  template [[host_name(name "_" #dims)]] [[kernel]] void     \
-  [[kernel]] void copy_g_nd<itype, otype, dims>( \
+  copy_g_nd<itype, otype, dims>(                             \
       device const itype* src [[buffer(0)]],                 \
       device otype* dst [[buffer(1)]],                       \
       constant const int* src_shape [[buffer(2)]],           \
       constant const int64_t* src_strides [[buffer(3)]],     \
       uint3 index [[thread_position_in_grid]],               \
       uint3 grid_dim [[threads_per_grid]]);                  \
-  template [[host_name("g" name "_" #dims)]] \
+  template [[host_name("g" name "_" #dims)]] [[kernel]] void \
-  [[kernel]] void copy_gg_nd<itype, otype, dims>( \
+  copy_gg_nd<itype, otype, dims>(                            \
       device const itype* src [[buffer(0)]],                 \
       device otype* dst [[buffer(1)]],                       \
       constant const int* src_shape [[buffer(2)]],           \
@@ -168,44 +170,40 @@ template <typename T, typename U>
       constant const int64_t* dst_strides [[buffer(4)]],     \
       uint3 index [[thread_position_in_grid]]);
 
-
 #define instantiate_copy_g_nd(name, itype, otype)                             \
-  template [[host_name(name "_1")]] \
+  template [[host_name(name "_1")]] [[kernel]] void copy_g_nd1<itype, otype>( \
-  [[kernel]] void copy_g_nd1<itype, otype>( \
       device const itype* src [[buffer(0)]],                                  \
       device otype* dst [[buffer(1)]],                                        \
       constant const int64_t& src_stride [[buffer(3)]],                       \
       uint index [[thread_position_in_grid]]);                                \
-  template [[host_name(name "_2")]] \
+  template [[host_name(name "_2")]] [[kernel]] void copy_g_nd2<itype, otype>( \
-  [[kernel]] void copy_g_nd2<itype, otype>( \
       device const itype* src [[buffer(0)]],                                  \
       device otype* dst [[buffer(1)]],                                        \
       constant const int64_t* src_strides [[buffer(3)]],                      \
       uint2 index [[thread_position_in_grid]],                                \
       uint2 grid_dim [[threads_per_grid]]);                                   \
-  template [[host_name(name "_3")]] \
+  template [[host_name(name "_3")]] [[kernel]] void copy_g_nd3<itype, otype>( \
-  [[kernel]] void copy_g_nd3<itype, otype>( \
       device const itype* src [[buffer(0)]],                                  \
       device otype* dst [[buffer(1)]],                                        \
       constant const int64_t* src_strides [[buffer(3)]],                      \
       uint3 index [[thread_position_in_grid]],                                \
       uint3 grid_dim [[threads_per_grid]]);                                   \
-  template [[host_name("g" name "_1")]] \
+  template [[host_name("g" name "_1")]] [[kernel]] void                       \
-  [[kernel]] void copy_gg_nd1<itype, otype>( \
+  copy_gg_nd1<itype, otype>(                                                  \
       device const itype* src [[buffer(0)]],                                  \
       device otype* dst [[buffer(1)]],                                        \
       constant const int64_t& src_stride [[buffer(3)]],                       \
       constant const int64_t& dst_stride [[buffer(4)]],                       \
       uint index [[thread_position_in_grid]]);                                \
-  template [[host_name("g" name "_2")]] \
+  template [[host_name("g" name "_2")]] [[kernel]] void                       \
-  [[kernel]] void copy_gg_nd2<itype, otype>( \
+  copy_gg_nd2<itype, otype>(                                                  \
       device const itype* src [[buffer(0)]],                                  \
       device otype* dst [[buffer(1)]],                                        \
       constant const int64_t* src_strides [[buffer(3)]],                      \
       constant const int64_t* dst_strides [[buffer(4)]],                      \
       uint2 index [[thread_position_in_grid]]);                               \
-  template [[host_name("g" name "_3")]] \
+  template [[host_name("g" name "_3")]] [[kernel]] void                       \
-  [[kernel]] void copy_gg_nd3<itype, otype>( \
+  copy_gg_nd3<itype, otype>(                                                  \
       device const itype* src [[buffer(0)]],                                  \
       device otype* dst [[buffer(1)]],                                        \
       constant const int64_t* src_strides [[buffer(3)]],                      \
@@ -214,10 +212,8 @@ template <typename T, typename U>
   instantiate_copy_g_dim(name, itype, otype, 4)                               \
       instantiate_copy_g_dim(name, itype, otype, 5)
 
-
 #define instantiate_copy_g(name, itype, otype)                            \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void copy_g<itype, otype>(      \
-  [[kernel]] void copy_g<itype, otype>( \
       device const itype* src [[buffer(0)]],                              \
       device otype* dst [[buffer(1)]],                                    \
       constant const int* src_shape [[buffer(2)]],                        \
@@ -225,8 +221,7 @@ template <typename T, typename U>
       constant const int& ndim [[buffer(5)]],                             \
       uint3 index [[thread_position_in_grid]],                            \
       uint3 grid_dim [[threads_per_grid]]);                               \
-  template [[host_name("g" name)]] \
+  template [[host_name("g" name)]] [[kernel]] void copy_gg<itype, otype>( \
-  [[kernel]] void copy_gg<itype, otype>( \
       device const itype* src [[buffer(0)]],                              \
       device otype* dst [[buffer(1)]],                                    \
       constant const int* src_shape [[buffer(2)]],                        \
@@ -235,12 +230,14 @@ template <typename T, typename U>
       constant const int& ndim [[buffer(5)]],                             \
       uint3 index [[thread_position_in_grid]]);
 
+// clang-format off
 #define instantiate_copy_all(tname, itype, otype)   \
   instantiate_copy("scopy" #tname, itype, otype, s) \
   instantiate_copy("vcopy" #tname, itype, otype, v) \
   instantiate_copy_g("gcopy" #tname, itype, otype)  \
-  instantiate_copy_g_nd("gcopy" #tname, itype, otype)
+  instantiate_copy_g_nd("gcopy" #tname, itype, otype) // clang-format on
 
+// clang-format off
 #define instantiate_copy_itype(itname, itype)                \
   instantiate_copy_all(itname ##bool_, itype, bool)          \
   instantiate_copy_all(itname ##uint8, itype, uint8_t)       \
@@ -268,4 +265,4 @@ instantiate_copy_itype(int64, int64_t)
 instantiate_copy_itype(float16, half)
 instantiate_copy_itype(float32, float)
 instantiate_copy_itype(bfloat16, bfloat16_t)
-instantiate_copy_itype(complex64, complex64_t)
+instantiate_copy_itype(complex64, complex64_t) // clang-format on

mlx/backend/metal/kernels/fft.metal

@@ -6,9 +6,8 @@
 // - VkFFT (https://github.com/DTolm/VkFFT)
 // - Eric Bainville's excellent page (http://www.bealto.com/gpu-fft.html)
 
-#include <metal_math>
 #include <metal_common>
-
+#include <metal_math>
 
 #include "mlx/backend/metal/kernels/defines.h"
 #include "mlx/backend/metal/kernels/utils.h"
@@ -23,7 +22,7 @@ float2 complex_mul(float2 a, float2 b) {
 }
 
 float2 get_twiddle(int k, int p) {
-  float theta = -1.0f * k * M_PI_F / (2*p);
+  float theta = -1.0f * k * M_PI_F / (2 * p);
 
   float2 twiddle;
   twiddle.x = metal::fast::cos(theta);
@@ -32,7 +31,12 @@ float2 get_twiddle(int k, int p) {
 }
 
 // single threaded radix2 implemetation
-void radix2(int i, int p, int m, threadgroup float2* read_buf, threadgroup float2* write_buf) {
+void radix2(
+    int i,
+    int p,
+    int m,
+    threadgroup float2* read_buf,
+    threadgroup float2* write_buf) {
   float2 x_0 = read_buf[i];
   float2 x_1 = read_buf[i + m];
 
@@ -53,11 +57,16 @@ void radix2(int i, int p, int m, threadgroup float2* read_buf, threadgroup float
 }
 
 // single threaded radix4 implemetation
-void radix4(int i, int p, int m, threadgroup float2* read_buf, threadgroup float2* write_buf) {
+void radix4(
+    int i,
+    int p,
+    int m,
+    threadgroup float2* read_buf,
+    threadgroup float2* write_buf) {
   float2 x_0 = read_buf[i];
   float2 x_1 = read_buf[i + m];
-  float2 x_2 = read_buf[i + 2*m];
+  float2 x_2 = read_buf[i + 2 * m];
-  float2 x_3 = read_buf[i + 3*m];
+  float2 x_3 = read_buf[i + 3 * m];
 
   // The index within this sub-DFT
   int k = i & (p - 1);
@@ -90,11 +99,10 @@ void radix4(int i, int p, int m, threadgroup float2* read_buf, threadgroup float
 
   write_buf[j] = y_0;
   write_buf[j + p] = y_1;
-  write_buf[j + 2*p] = y_2;
+  write_buf[j + 2 * p] = y_2;
-  write_buf[j + 3*p] = y_3;
+  write_buf[j + 3 * p] = y_3;
 }
 
-
 // Each FFT is computed entirely in shared GPU memory.
 //
 // N is decomposed into radix-2 and radix-4 DFTs:
@@ -107,11 +115,10 @@ void radix4(int i, int p, int m, threadgroup float2* read_buf, threadgroup float
 // steps at compile time for a ~20% performance boost.
 template <size_t n, size_t radix_2_steps, size_t radix_4_steps>
 [[kernel]] void fft(
-    const device float2 *in [[buffer(0)]],
+    const device float2* in [[buffer(0)]],
-    device float2 * out [[buffer(1)]],
+    device float2* out [[buffer(1)]],
     uint3 thread_position_in_grid [[thread_position_in_grid]],
     uint3 threads_per_grid [[threads_per_grid]]) {
-
   // Index of the DFT in batch
   int batch_idx = thread_position_in_grid.x * n;
   // The index in the DFT we're working on
@@ -132,16 +139,16 @@ template <size_t n, size_t radix_2_steps, size_t radix_4_steps>
   // Copy input into shared memory
   shared_in[i] = in[batch_idx + i];
   shared_in[i + m] = in[batch_idx + i + m];
-  shared_in[i + 2*m] = in[batch_idx + i + 2*m];
+  shared_in[i + 2 * m] = in[batch_idx + i + 2 * m];
-  shared_in[i + 3*m] = in[batch_idx + i + 3*m];
+  shared_in[i + 3 * m] = in[batch_idx + i + 3 * m];
 
   threadgroup_barrier(mem_flags::mem_threadgroup);
 
   int p = 1;
 
   for (size_t r = 0; r < radix_2_steps; r++) {
-    radix2(i, p, m*2, read_buf, write_buf);
+    radix2(i, p, m * 2, read_buf, write_buf);
-    radix2(i + m, p, m*2, read_buf, write_buf);
+    radix2(i + m, p, m * 2, read_buf, write_buf);
     p *= 2;
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
@@ -167,29 +174,26 @@ template <size_t n, size_t radix_2_steps, size_t radix_4_steps>
   // Copy shared memory to output
   out[batch_idx + i] = read_buf[i];
   out[batch_idx + i + m] = read_buf[i + m];
-  out[batch_idx + i + 2*m] = read_buf[i + 2*m];
+  out[batch_idx + i + 2 * m] = read_buf[i + 2 * m];
-  out[batch_idx + i + 3*m] = read_buf[i + 3*m];
+  out[batch_idx + i + 3 * m] = read_buf[i + 3 * m];
 }
 
 #define instantiate_fft(name, n, radix_2_steps, radix_4_steps)   \
-  template [[host_name("fft_" #name)]] \
+  template [[host_name("fft_" #name)]] [[kernel]] void           \
-  [[kernel]] void fft<n, radix_2_steps, radix_4_steps>( \
+  fft<n, radix_2_steps, radix_4_steps>(                          \
       const device float2* in [[buffer(0)]],                     \
       device float2* out [[buffer(1)]],                          \
       uint3 thread_position_in_grid [[thread_position_in_grid]], \
       uint3 threads_per_grid [[threads_per_grid]]);
 
-
 // Explicitly define kernels for each power of 2.
+// clang-format off
 instantiate_fft(4, /* n= */ 4, /* radix_2_steps= */ 0, /* radix_4_steps= */ 1)
-instantiate_fft(8, 8, 1, 1)
+instantiate_fft(8, 8, 1, 1) instantiate_fft(16, 16, 0, 2)
-instantiate_fft(16, 16, 0, 2)
+instantiate_fft(32, 32, 1, 2) instantiate_fft(64, 64, 0, 3)
-instantiate_fft(32, 32, 1, 2)
+instantiate_fft(128, 128, 1, 3) instantiate_fft(256, 256, 0, 4)
-instantiate_fft(64, 64, 0, 3)
-instantiate_fft(128, 128, 1, 3)
-instantiate_fft(256, 256, 0, 4)
 instantiate_fft(512, 512, 1, 4)
 instantiate_fft(1024, 1024, 0, 5)
 // 2048 is the max that will fit into 32KB of threadgroup memory.
 // TODO: implement 4 step FFT for larger n.
-instantiate_fft(2048, 2048, 1, 5)
+instantiate_fft(2048, 2048, 1, 5) // clang-format on

mlx/backend/metal/kernels/gather.metal

@@ -14,17 +14,16 @@ using namespace metal;
 
 template <typename T, typename IdxT, int NIDX, int IDX_NDIM>
 METAL_FUNC void gather_impl(
-    const device T *src [[buffer(0)]],
+    const device T* src [[buffer(0)]],
-    device T *out [[buffer(1)]],
+    device T* out [[buffer(1)]],
-    const constant int *src_shape [[buffer(2)]],
+    const constant int* src_shape [[buffer(2)]],
-    const constant size_t *src_strides [[buffer(3)]],
+    const constant size_t* src_strides [[buffer(3)]],
     const constant size_t& src_ndim [[buffer(4)]],
-    const constant int *slice_sizes [[buffer(5)]],
+    const constant int* slice_sizes [[buffer(5)]],
-    const constant int *axes [[buffer(6)]],
+    const constant int* axes [[buffer(6)]],
     const thread Indices<IdxT, NIDX>& indices,
     uint2 index [[thread_position_in_grid]],
     uint2 grid_dim [[threads_per_grid]]) {
-
   auto ind_idx = index.x;
   auto ind_offset = index.y;
 
@@ -43,41 +42,33 @@ METAL_FUNC void gather_impl(
           indices.ndim);
     }
     auto ax = axes[i];
-    auto idx_val = offset_neg_idx(
+    auto idx_val = offset_neg_idx(indices.buffers[i][idx_loc], src_shape[ax]);
-        indices.buffers[i][idx_loc], src_shape[ax]);
     src_idx += idx_val * src_strides[ax];
   }
 
-  auto src_offset = elem_to_loc(
+  auto src_offset = elem_to_loc(ind_offset, slice_sizes, src_strides, src_ndim);
-      ind_offset, slice_sizes, src_strides, src_ndim);
 
   size_t out_idx = index.y + static_cast<size_t>(grid_dim.y) * index.x;
   out[out_idx] = src[src_offset + src_idx];
-
 }
 
 #define make_gather_impl(IDX_ARG, IDX_ARR)                     \
-template <typename T, typename IdxT, int NIDX, int IDX_NDIM>  \
+  template <typename T, typename IdxT, int NIDX, int IDX_NDIM> \
-[[kernel]] void gather( \
+  [[kernel]] void gather(                                      \
-    const device T *src [[buffer(0)]], \
+      const device T* src [[buffer(0)]],                       \
-    device T *out [[buffer(1)]], \
+      device T* out [[buffer(1)]],                             \
-    const constant int *src_shape [[buffer(2)]], \
+      const constant int* src_shape [[buffer(2)]],             \
-    const constant size_t *src_strides [[buffer(3)]], \
+      const constant size_t* src_strides [[buffer(3)]],        \
       const constant size_t& src_ndim [[buffer(4)]],           \
-    const constant int *slice_sizes [[buffer(5)]], \
+      const constant int* slice_sizes [[buffer(5)]],           \
-    const constant int *axes [[buffer(6)]], \
+      const constant int* axes [[buffer(6)]],                  \
-    const constant int *idx_shapes [[buffer(7)]], \
+      const constant int* idx_shapes [[buffer(7)]],            \
-    const constant size_t *idx_strides [[buffer(8)]], \
+      const constant size_t* idx_strides [[buffer(8)]],        \
       const constant int& idx_ndim [[buffer(9)]],              \
-    IDX_ARG(IdxT) \
+      IDX_ARG(IdxT) uint2 index [[thread_position_in_grid]],   \
-    uint2 index [[thread_position_in_grid]], \
       uint2 grid_dim [[threads_per_grid]]) {                   \
- \
     Indices<IdxT, NIDX> idxs{                                  \
-      {{IDX_ARR()}}, \
+        {{IDX_ARR()}}, idx_shapes, idx_strides, idx_ndim};     \
-      idx_shapes, \
-      idx_strides, \
-      idx_ndim}; \
                                                                \
     return gather_impl<T, IdxT, NIDX, IDX_NDIM>(               \
         src,                                                   \
@@ -90,46 +81,39 @@ template <typename T, typename IdxT, int NIDX, int IDX_NDIM>  \
         idxs,                                                  \
         index,                                                 \
         grid_dim);                                             \
-} 
+  }
 
-#define make_gather(n) make_gather_impl(IDX_ARG_ ##n, IDX_ARR_ ##n)
+#define make_gather(n) make_gather_impl(IDX_ARG_##n, IDX_ARR_##n)
 
-make_gather(0)
+make_gather(0) make_gather(1) make_gather(2) make_gather(3) make_gather(4)
-make_gather(1)
+    make_gather(5) make_gather(6) make_gather(7) make_gather(8) make_gather(9)
-make_gather(2)
+        make_gather(10)
-make_gather(3)
-make_gather(4)
-make_gather(5)
-make_gather(6)
-make_gather(7)
-make_gather(8)
-make_gather(9)
-make_gather(10)
 
 /////////////////////////////////////////////////////////////////////
 // Gather instantiations
 /////////////////////////////////////////////////////////////////////
 
 #define instantiate_gather6(name, src_t, idx_t, nidx, IDX_ARG, nd, nd_name)   \
-template [[host_name("gather" name "_" #nidx "" #nd_name)]] \
+  template [[host_name("gather" name "_" #nidx "" #nd_name)]] [[kernel]] void \
-[[kernel]] void gather<src_t, idx_t, nidx, nd>( \
+  gather<src_t, idx_t, nidx, nd>(                                             \
-    const device src_t *src [[buffer(0)]], \
+      const device src_t* src [[buffer(0)]],                                  \
-    device src_t *out [[buffer(1)]], \
+      device src_t* out [[buffer(1)]],                                        \
-    const constant int *src_shape [[buffer(2)]], \
+      const constant int* src_shape [[buffer(2)]],                            \
-    const constant size_t *src_strides [[buffer(3)]], \
+      const constant size_t* src_strides [[buffer(3)]],                       \
       const constant size_t& src_ndim [[buffer(4)]],                          \
-    const constant int *slice_sizes [[buffer(5)]], \
+      const constant int* slice_sizes [[buffer(5)]],                          \
-    const constant int *axes [[buffer(6)]], \
+      const constant int* axes [[buffer(6)]],                                 \
-    const constant int *idx_shapes [[buffer(7)]], \
+      const constant int* idx_shapes [[buffer(7)]],                           \
-    const constant size_t *idx_strides [[buffer(8)]], \
+      const constant size_t* idx_strides [[buffer(8)]],                       \
       const constant int& idx_ndim [[buffer(9)]],                             \
-    IDX_ARG(idx_t) \
+      IDX_ARG(idx_t) uint2 index [[thread_position_in_grid]],                 \
-    uint2 index [[thread_position_in_grid]], \
       uint2 grid_dim [[threads_per_grid]]);
 
+// clang-format off
 #define instantiate_gather5(name, src_t, idx_t, nidx, nd, nd_name) \
-  instantiate_gather6(name, src_t, idx_t, nidx, IDX_ARG_ ##nidx, nd, nd_name)
+  instantiate_gather6(name, src_t, idx_t, nidx, IDX_ARG_ ##nidx, nd, nd_name) // clang-format on
 
+// clang-format off
 #define instantiate_gather4(name, src_t, idx_t, nidx) \
   instantiate_gather5(name, src_t, idx_t, nidx, 0, _0) \
   instantiate_gather5(name, src_t, idx_t, nidx, 1, _1) \
@@ -148,8 +132,9 @@ instantiate_gather4("int32", int32_t, bool, 0)
 instantiate_gather4("int64", int64_t, bool, 0)
 instantiate_gather4("float16", half, bool, 0)
 instantiate_gather4("float32", float, bool, 0)
-instantiate_gather4("bfloat16", bfloat16_t, bool, 0)
+instantiate_gather4("bfloat16", bfloat16_t, bool, 0) // clang-format on
 
+// clang-format off
 #define instantiate_gather3(name, src_type, ind_type) \
   instantiate_gather4(name, src_type, ind_type, 1)    \
   instantiate_gather4(name, src_type, ind_type, 2)    \
@@ -160,8 +145,9 @@ instantiate_gather4("bfloat16", bfloat16_t, bool, 0)
   instantiate_gather4(name, src_type, ind_type, 7)    \
   instantiate_gather4(name, src_type, ind_type, 8)    \
   instantiate_gather4(name, src_type, ind_type, 9)    \
-  instantiate_gather4(name, src_type, ind_type, 10)
+  instantiate_gather4(name, src_type, ind_type, 10) // clang-format on
 
+// clang-format off
 #define instantiate_gather(name, src_type)                \
   instantiate_gather3(#name "bool_", src_type, bool)      \
   instantiate_gather3(#name "uint8", src_type, uint8_t)   \
@@ -184,4 +170,4 @@ instantiate_gather(int32, int32_t)
 instantiate_gather(int64, int64_t)
 instantiate_gather(float16, half)
 instantiate_gather(float32, float)
-instantiate_gather(bfloat16, bfloat16_t)
+instantiate_gather(bfloat16, bfloat16_t) // clang-format on

mlx/backend/metal/kernels/gemv.metal

@@ -1,7 +1,7 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include <metal_stdlib>
 #include <metal_simdgroup>
+#include <metal_stdlib>
 
 #include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/defines.h"
@@ -22,10 +22,9 @@ template <
     const int BM, /* Threadgroup rows (in threads) */
     const int BN, /* Threadgroup cols (in threads) */
     const int TM, /* Thread rows (in elements) */
-  const int TN , /* Thread cols (in elements) */ 
+    const int TN, /* Thread cols (in elements) */
     const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
 struct GEMVKernel {
-
   static_assert(BN == SIMD_SIZE, "gemv block must have a width of SIMD_SIZE");
 
   // - The matrix of size (M = out_vec_size, N = in_vec_size) is divided up
@@ -35,15 +34,20 @@ struct GEMVKernel {
   //
   // 1. A thread loads TN elements each from mat along TM contiguous rows
   //      and the corresponding scalar from the vector
-  // 2. The thread then multiplies and adds to accumulate its local result for the block
+  // 2. The thread then multiplies and adds to accumulate its local result for
-  // 3. At the end, each thread has accumulated results over all blocks across the rows
+  // the block
+  // 3. At the end, each thread has accumulated results over all blocks across
+  // the rows
   //      These are then summed up across the threadgroup
   // 4. Each threadgroup writes its accumulated BN * TN outputs
   //
   // Edge case handling:
-  // - The threadgroup with the largest tid will have blocks that exceed the matrix
+  // - The threadgroup with the largest tid will have blocks that exceed the
-  //   * The blocks that start outside the matrix are never read (thread results remain zero)
+  // matrix
-  //   * The last thread that partially overlaps with the matrix is shifted inwards 
+  //   * The blocks that start outside the matrix are never read (thread results
+  //   remain zero)
+  //   * The last thread that partially overlaps with the matrix is shifted
+  //   inwards
   //     such that the thread block fits exactly in the matrix
 
   MLX_MTL_CONST short tgp_mem_size = BN * TN * 2;
@@ -64,7 +68,6 @@ struct GEMVKernel {
       uint3 lid [[thread_position_in_threadgroup]],
       uint simd_gid [[simdgroup_index_in_threadgroup]],
       uint simd_lid [[thread_index_in_simdgroup]]) {
-
     // Appease compiler
     (void)lid;
 
@@ -80,7 +83,7 @@ struct GEMVKernel {
     int out_row = (tid.x * BM + simd_gid) * TM;
 
     // Exit simdgroup if rows out of bound
-    if(out_row >= out_vec_size) 
+    if (out_row >= out_vec_size)
       return;
 
     // Adjust tail simdgroup to ensure in bound reads
@@ -90,89 +93,80 @@ struct GEMVKernel {
     mat += out_row * marix_ld;
 
     // Loop over in_vec in blocks of BN * TN
-    for(int bn = simd_lid * TN; bn < in_vec_size; bn += BN * TN) {
+    for (int bn = simd_lid * TN; bn < in_vec_size; bn += BN * TN) {
-
       threadgroup_barrier(mem_flags::mem_threadgroup);
 
       // Prefetch in_vector for threadgroup use
-      if(simd_gid == 0) {
+      if (simd_gid == 0) {
         // Main load loop
-        if(bn + TN <= in_vec_size) {
+        if (bn + TN <= in_vec_size) {
-
+#pragma clang loop unroll(full)
-          #pragma clang loop unroll(full)
+          for (int tn = 0; tn < TN; tn++) {
-          for(int tn = 0; tn < TN; tn++) {
             in_vec_block[tn] = in_vec[bn + tn];
           }
 
         } else { // Edgecase
 
-          #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-          for(int tn = 0; tn < TN; tn++) {
+          for (int tn = 0; tn < TN; tn++) {
             in_vec_block[tn] = bn + tn < in_vec_size ? in_vec[bn + tn] : 0;
           }
-
         }
       }
 
       threadgroup_barrier(mem_flags::mem_threadgroup);
 
-      // Load for all rows
+// Load for all rows
-      #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-      for(int tn = 0; tn < TN; tn++) {
+      for (int tn = 0; tn < TN; tn++) {
         v_coeff[tn] = in_vec_block[tn];
       }
 
-      // Per thread work loop
+// Per thread work loop
-      #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-      for(int tm = 0; tm < TM; tm++) {
+      for (int tm = 0; tm < TM; tm++) {
-
         // Load for the row
-        if(bn + TN <= in_vec_size) {
+        if (bn + TN <= in_vec_size) {
-          #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-          for(int tn = 0; tn < TN; tn++) {
+          for (int tn = 0; tn < TN; tn++) {
             inter[tn] = mat[tm * marix_ld + bn + tn];
           }
 
         } else { // Edgecase
-          #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-          for(int tn = 0; tn < TN; tn++) {
+          for (int tn = 0; tn < TN; tn++) {
-            int col_idx = (bn + tn) < in_vec_size ? (bn + tn) : (in_vec_size - 1);
+            int col_idx =
+                (bn + tn) < in_vec_size ? (bn + tn) : (in_vec_size - 1);
             inter[tn] = mat[tm * marix_ld + col_idx];
           }
         }
 
         // Accumulate results
-        for(int tn = 0; tn < TN; tn++) {
+        for (int tn = 0; tn < TN; tn++) {
           result[tm] += inter[tn] * v_coeff[tn];
         }
-
       }
     }
 
-    // Simdgroup accumulations
+// Simdgroup accumulations
-    #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-    for(int tm = 0; tm < TM; tm++) {
+    for (int tm = 0; tm < TM; tm++) {
       result[tm] = simd_sum(result[tm]);
     }
 
     // Write outputs
-    if(simd_lid == 0) {
+    if (simd_lid == 0) {
-
+#pragma clang loop unroll(full)
-      #pragma clang loop unroll(full)
+      for (int tm = 0; tm < TM; tm++) {
-      for(int tm = 0; tm < TM; tm++) {
+        if (kDoAxpby) {
-        if(kDoAxpby) {
+          out_vec[out_row + tm] = static_cast<T>(alpha) * result[tm] +
-          out_vec[out_row + tm] = 
-              static_cast<T>(alpha) * result[tm] + 
               static_cast<T>(beta) * bias[(out_row + tm) * bias_stride];
         } else {
           out_vec[out_row + tm] = result[tm];
         }
       }
-
     }
-
   }
-
 };
 
 ///////////////////////////////////////////////////////////////////////////////
@@ -187,7 +181,6 @@ template <
     const int TN, /* Thread cols (in elements) */
     const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
 struct GEMVTKernel {
-
   // - The matrix of size (M = in_vec_size, N = out_vec_size) is divided up
   //   into blocks of (BM * TM, BN * TN) divided among threadgroups
   // - Every thread works on a block of (TM, TN)
@@ -195,18 +188,22 @@ struct GEMVTKernel {
   //
   // 1. A thread loads TN elements each from mat along TM contiguous rows
   //      and the corresponding scalar from the vector
-  // 2. The thread then multiplies and adds to accumulate its local result for the block
+  // 2. The thread then multiplies and adds to accumulate its local result for
-  // 3. At the end, each thread has accumulated results over all blocks across the rows
+  // the block
+  // 3. At the end, each thread has accumulated results over all blocks across
+  // the rows
   //      These are then summed up across the threadgroup
   // 4. Each threadgroup writes its accumulated BN * TN outputs
   //
   // Edge case handling:
-  // - The threadgroup with the largest tid will have blocks that exceed the matrix
+  // - The threadgroup with the largest tid will have blocks that exceed the
-  //   * The blocks that start outside the matrix are never read (thread results remain zero)
+  // matrix
-  //   * The last thread that partially overlaps with the matrix is shifted inwards 
+  //   * The blocks that start outside the matrix are never read (thread results
+  //   remain zero)
+  //   * The last thread that partially overlaps with the matrix is shifted
+  //   inwards
   //     such that the thread block fits exactly in the matrix
 
-
   MLX_MTL_CONST short tgp_mem_size = BN * BM * TN;
 
   static METAL_FUNC void run(
@@ -225,7 +222,6 @@ struct GEMVTKernel {
       uint3 lid [[thread_position_in_threadgroup]],
       uint simd_gid [[simdgroup_index_in_threadgroup]],
       uint simd_lid [[thread_index_in_simdgroup]]) {
-
     // Appease compiler
     (void)simd_gid;
     (void)simd_lid;
@@ -243,77 +239,69 @@ struct GEMVTKernel {
 
     // Edgecase handling
     if (out_col < out_vec_size) {
-
       out_col = out_col + TN < out_vec_size ? out_col : out_vec_size - TN;
 
       // Per thread accumulation main loop
       int bm = in_row;
-      for(; bm < in_vec_size; bm += BM * TM) {
+      for (; bm < in_vec_size; bm += BM * TM) {
         // Adding a threadgroup_barrier improves performance slightly
         // This is possibly it may help exploit cache better
         threadgroup_barrier(mem_flags::mem_none);
 
-        if(bm + TM <= in_vec_size) {
+        if (bm + TM <= in_vec_size) {
-
+#pragma clang loop unroll(full)
-          #pragma clang loop unroll(full)
+          for (int tm = 0; tm < TM; tm++) {
-          for(int tm = 0; tm < TM; tm++) {
             v_coeff[tm] = in_vec[bm + tm];
           }
 
-          #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-          for(int tm = 0; tm < TM; tm++) {
+          for (int tm = 0; tm < TM; tm++) {
-            for(int tn = 0; tn < TN; tn++) {
+            for (int tn = 0; tn < TN; tn++) {
               inter[tn] = mat[(bm + tm) * marix_ld + out_col + tn];
             }
-            for(int tn = 0; tn < TN; tn++) {
+            for (int tn = 0; tn < TN; tn++) {
               result[tn] += v_coeff[tm] * inter[tn];
             }
           }
 
         } else { // Edgecase handling
-          for(int tm = 0; bm + tm < in_vec_size; tm++) {
+          for (int tm = 0; bm + tm < in_vec_size; tm++) {
             v_coeff[tm] = in_vec[bm + tm];
 
-            for(int tn = 0; tn < TN; tn++) {
+            for (int tn = 0; tn < TN; tn++) {
               inter[tn] = mat[(bm + tm) * marix_ld + out_col + tn];
             }
-            for(int tn = 0; tn < TN; tn++) {
+            for (int tn = 0; tn < TN; tn++) {
               result[tn] += v_coeff[tm] * inter[tn];
             }
-
           }
         }
       }
-
     }
 
     // Threadgroup collection
 
-    #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-    for(int i = 0; i < TN; i++) {
+    for (int i = 0; i < TN; i++) {
       tgp_results[lid.y * TN + i] = result[i];
     }
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
     // Threadgroup accumulation and writing out results
-    if(lid.y == 0 && out_col < out_vec_size) {
+    if (lid.y == 0 && out_col < out_vec_size) {
-      
+#pragma clang loop unroll(full)
-      #pragma clang loop unroll(full)
+      for (int i = 1; i < BM; i++) {
-      for(int i = 1; i < BM; i++) {
+#pragma clang loop unroll(full)
-
+        for (int j = 0; j < TN; j++) {
-        #pragma clang loop unroll(full)
-        for(int j = 0; j < TN; j++) {
           result[j] += tgp_results[i * TN + j];
         }
       }
 
-      #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-      for(int j = 0; j < TN; j++) {
+      for (int j = 0; j < TN; j++) {
-
+        if (kDoAxpby) {
-        if(kDoAxpby) {
+          out_vec[out_col + j] = static_cast<T>(alpha) * result[j] +
-          out_vec[out_col + j] = 
-              static_cast<T>(alpha) * result[j] + 
               static_cast<T>(beta) * bias[(out_col + j) * bias_stride];
         } else {
           out_vec[out_col + j] = result[j];
@@ -335,7 +323,7 @@ template <
     const int TN, /* Thread cols (in elements) */
     const bool kDoNCBatch, /* Batch ndim > 1 */
     const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
-[[kernel, max_total_threads_per_threadgroup(BM * BN)]] void gemv(
+[[kernel, max_total_threads_per_threadgroup(BM* BN)]] void gemv(
     const device T* mat [[buffer(0)]],
     const device T* in_vec [[buffer(1)]],
     const device T* bias [[buffer(2)]],
@@ -355,16 +343,15 @@ template <
     uint3 lid [[thread_position_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   using gemv_kernel = GEMVKernel<T, BM, BN, TM, TN, kDoAxpby>;
   threadgroup T tgp_memory[gemv_kernel::tgp_mem_size];
 
   // Update batch offsets
-  if(kDoNCBatch) {
+  if (kDoNCBatch) {
     in_vec += elem_to_loc(tid.z, batch_shape, vector_batch_stride, batch_ndim);
     mat += elem_to_loc(tid.z, batch_shape, matrix_batch_stride, batch_ndim);
 
-    if(kDoAxpby) {
+    if (kDoAxpby) {
       bias += elem_to_loc(tid.z, batch_shape, bias_batch_stride, batch_ndim);
     }
 
@@ -372,7 +359,7 @@ template <
     in_vec += tid.z * vector_batch_stride[0];
     mat += tid.z * matrix_batch_stride[0];
 
-    if(kDoAxpby) {
+    if (kDoAxpby) {
       bias += tid.z * bias_batch_stride[0];
     }
   }
@@ -394,15 +381,13 @@ template <
       tid,
       lid,
       simd_gid,
-    simd_lid
+      simd_lid);
-  );
-
 }
 
-
 #define instantiate_gemv_helper(name, itype, bm, bn, tm, tn, nc, axpby)      \
-  template [[host_name("gemv_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn "_nc" #nc "_axpby" #axpby)]] \
+  template [[host_name("gemv_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn \
-  [[kernel]] void gemv<itype, bm, bn, tm, tn, nc, axpby>( \
+                       "_nc" #nc "_axpby" #axpby)]] [[kernel]] void          \
+  gemv<itype, bm, bn, tm, tn, nc, axpby>(                                    \
       const device itype* mat [[buffer(0)]],                                 \
       const device itype* in_vec [[buffer(1)]],                              \
       const device itype* bias [[buffer(2)]],                                \
@@ -430,9 +415,8 @@ template <
               instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 1, 1)
 
 #define instantiate_gemv_blocks(name, itype)                   \
-  instantiate_gemv(name, itype, 4, 32, 1, 4) \
+  instantiate_gemv(name, itype, 4, 32, 1, 4) instantiate_gemv( \
-  instantiate_gemv(name, itype, 4, 32, 4, 4) \
+      name, itype, 4, 32, 4, 4) instantiate_gemv(name, itype, 8, 32, 4, 4)
-  instantiate_gemv(name, itype, 8, 32, 4, 4)
 
 instantiate_gemv_blocks(float32, float);
 instantiate_gemv_blocks(float16, half);
@@ -450,7 +434,7 @@ template <
     const int TN, /* Thread cols (in elements) */
     const bool kDoNCBatch, /* Batch ndim > 1 */
     const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
-[[kernel, max_total_threads_per_threadgroup(BM * BN)]] void gemv_t(
+[[kernel, max_total_threads_per_threadgroup(BM* BN)]] void gemv_t(
     const device T* mat [[buffer(0)]],
     const device T* in_vec [[buffer(1)]],
     const device T* bias [[buffer(2)]],
@@ -470,16 +454,15 @@ template <
     uint3 lid [[thread_position_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   using gemv_kernel = GEMVTKernel<T, BM, BN, TM, TN, kDoAxpby>;
   threadgroup T tgp_memory[gemv_kernel::tgp_mem_size];
 
   // Update batch offsets
-  if(kDoNCBatch) {
+  if (kDoNCBatch) {
     in_vec += elem_to_loc(tid.z, batch_shape, vector_batch_stride, batch_ndim);
     mat += elem_to_loc(tid.z, batch_shape, matrix_batch_stride, batch_ndim);
 
-    if(kDoAxpby) {
+    if (kDoAxpby) {
       bias += elem_to_loc(tid.z, batch_shape, bias_batch_stride, batch_ndim);
     }
 
@@ -487,7 +470,7 @@ template <
     in_vec += tid.z * vector_batch_stride[0];
     mat += tid.z * matrix_batch_stride[0];
 
-    if(kDoAxpby) {
+    if (kDoAxpby) {
       bias += tid.z * bias_batch_stride[0];
     }
   }
@@ -509,14 +492,13 @@ template <
       tid,
       lid,
       simd_gid,
-    simd_lid
+      simd_lid);
-  );
-
 }
 
 #define instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, nc, axpby)      \
-  template [[host_name("gemv_t_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn "_nc" #nc "_axpby" #axpby)]] \
+  template [[host_name("gemv_t_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn \
-  [[kernel]] void gemv_t<itype, bm, bn, tm, tn, nc, axpby>( \
+                       "_nc" #nc "_axpby" #axpby)]] [[kernel]] void            \
+  gemv_t<itype, bm, bn, tm, tn, nc, axpby>(                                    \
       const device itype* mat [[buffer(0)]],                                   \
       const device itype* in_vec [[buffer(1)]],                                \
       const device itype* bias [[buffer(2)]],                                  \
@@ -537,20 +519,23 @@ template <
       uint simd_gid [[simdgroup_index_in_threadgroup]],                        \
       uint simd_lid [[thread_index_in_simdgroup]]);
 
+// clang-format off
 #define instantiate_gemv_t(name, itype, bm, bn, tm, tn)        \
   instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 0, 0) \
   instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 0, 1) \
   instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 1, 0) \
-  instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 1, 1)
+  instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 1, 1) // clang-format on
 
+// clang-format off
 #define instantiate_gemv_t_blocks(name, itype) \
   instantiate_gemv_t(name, itype, 8, 8, 4, 1)  \
   instantiate_gemv_t(name, itype, 8, 8, 4, 4)  \
   instantiate_gemv_t(name, itype, 8, 16, 4, 4) \
   instantiate_gemv_t(name, itype, 8, 32, 4, 4) \
   instantiate_gemv_t(name, itype, 8, 64, 4, 4) \
-  instantiate_gemv_t(name, itype, 8, 128, 4, 4)
+  instantiate_gemv_t(name, itype, 8, 128, 4, 4) // clang-format on
 
+// clang-format off
 instantiate_gemv_t_blocks(float32, float);
 instantiate_gemv_t_blocks(float16, half);
-instantiate_gemv_t_blocks(bfloat16, bfloat16_t);
+instantiate_gemv_t_blocks(bfloat16, bfloat16_t); // clang-format on

mlx/backend/metal/kernels/layer_norm.metal

@@ -99,7 +99,8 @@ template <typename T, int N_READS = RMS_N_READS>
     for (int i = 0; i < N_READS; i++) {
       if ((lid * N_READS + i) < axis_size) {
         thread_x[i] = (thread_x[i] - mean) * normalizer;
-        out[i] = w[w_stride * i] * static_cast<T>(thread_x[i]) + b[b_stride * i];
+        out[i] =
+            w[w_stride * i] * static_cast<T>(thread_x[i]) + b[b_stride * i];
       }
     }
   }
@@ -192,13 +193,15 @@ template <typename T, int N_READS = RMS_N_READS>
     if (r + lid * N_READS + N_READS <= axis_size) {
       for (int i = 0; i < N_READS; i++) {
         float xi = (x[r + i] - mean) * normalizer;
-        out[r + i] = w[w_stride * (i + r)] * static_cast<T>(xi) + b[b_stride * (i + r)];
+        out[r + i] =
+            w[w_stride * (i + r)] * static_cast<T>(xi) + b[b_stride * (i + r)];
       }
     } else {
       for (int i = 0; i < N_READS; i++) {
         if ((r + lid * N_READS + i) < axis_size) {
           float xi = (x[r + i] - mean) * normalizer;
-          out[r + i] = w[w_stride * (i + r)] * static_cast<T>(xi) + b[b_stride * (i + r)];
+          out[r + i] = w[w_stride * (i + r)] * static_cast<T>(xi) +
+              b[b_stride * (i + r)];
         }
       }
     }
@@ -323,7 +326,8 @@ template <typename T, int N_READS = RMS_N_READS>
   if (lid * N_READS + N_READS <= axis_size) {
     for (int i = 0; i < N_READS; i++) {
       thread_x[i] = (thread_x[i] - mean) * normalizer;
-      gx[i] = static_cast<T>(normalizer * (thread_w[i] * thread_g[i] - meanwg) -
+      gx[i] = static_cast<T>(
+          normalizer * (thread_w[i] * thread_g[i] - meanwg) -
           thread_x[i] * meanwgxc * normalizer2);
       gw[i] = static_cast<T>(thread_g[i] * thread_x[i]);
     }
@@ -331,7 +335,8 @@ template <typename T, int N_READS = RMS_N_READS>
     for (int i = 0; i < N_READS; i++) {
       if ((lid * N_READS + i) < axis_size) {
         thread_x[i] = (thread_x[i] - mean) * normalizer;
-        gx[i] = static_cast<T>(normalizer * (thread_w[i] * thread_g[i] - meanwg) -
+        gx[i] = static_cast<T>(
+            normalizer * (thread_w[i] * thread_g[i] - meanwg) -
             thread_x[i] * meanwgxc * normalizer2);
         gw[i] = static_cast<T>(thread_g[i] * thread_x[i]);
       }
@@ -460,8 +465,8 @@ template <typename T, int N_READS = RMS_N_READS>
         float xi = (x[i + r] - mean) * normalizer;
         float wi = w[(i + r) * w_stride];
         float gi = g[i + r];
-        gx[i + r] = static_cast<T>(normalizer * (wi * gi - meanwg) -
+        gx[i + r] = static_cast<T>(
-                                   xi * meanwgxc * normalizer2);
+            normalizer * (wi * gi - meanwg) - xi * meanwgxc * normalizer2);
         gw[i + r] = static_cast<T>(gi * xi);
       }
     } else {
@@ -470,8 +475,8 @@ template <typename T, int N_READS = RMS_N_READS>
           float xi = (x[i + r] - mean) * normalizer;
           float wi = w[(i + r) * w_stride];
           float gi = g[i + r];
-          gx[i + r] = static_cast<T>(normalizer * (wi * gi - meanwg) -
+          gx[i + r] = static_cast<T>(
-                                     xi * meanwgxc * normalizer2);
+              normalizer * (wi * gi - meanwg) - xi * meanwgxc * normalizer2);
           gw[i + r] = static_cast<T>(gi * xi);
         }
       }
@@ -548,6 +553,4 @@ template <typename T, int N_READS = RMS_N_READS>
 
 instantiate_layer_norm(float32, float)
 instantiate_layer_norm(float16, half)
-instantiate_layer_norm(bfloat16, bfloat16_t)
+instantiate_layer_norm(bfloat16, bfloat16_t) // clang-format on
-    // clang-format on
-

mlx/backend/metal/kernels/quantized.metal

@@ -1,7 +1,7 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include <metal_stdlib>
 #include <metal_simdgroup>
+#include <metal_stdlib>
 
 #include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/defines.h"
@@ -15,30 +15,31 @@ using namespace metal;
 
 MLX_MTL_CONST int SIMD_SIZE = 32;
 
-
 template <typename T, typename U, int values_per_thread, int bits>
-inline U load_vector(const device T *x, thread U *x_thread) {
+inline U load_vector(const device T* x, thread U* x_thread) {
-  static_assert(bits == 2 || bits == 4 || bits == 8, "Template undefined for bits not in {2, 4, 8}");
+  static_assert(
+      bits == 2 || bits == 4 || bits == 8,
+      "Template undefined for bits not in {2, 4, 8}");
 
   U sum = 0;
 
   if (bits == 2) {
     for (int i = 0; i < values_per_thread; i += 4) {
-      sum += x[i] + x[i+1] + x[i+2] + x[i+3];
+      sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
       x_thread[i] = x[i];
-      x_thread[i+1] = x[i+1] / 4.0f;
+      x_thread[i + 1] = x[i + 1] / 4.0f;
-      x_thread[i+2] = x[i+2] / 16.0f;
+      x_thread[i + 2] = x[i + 2] / 16.0f;
-      x_thread[i+3] = x[i+3] / 64.0f;
+      x_thread[i + 3] = x[i + 3] / 64.0f;
     }
   }
 
   else if (bits == 4) {
     for (int i = 0; i < values_per_thread; i += 4) {
-      sum += x[i] + x[i+1] + x[i+2] + x[i+3];
+      sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
       x_thread[i] = x[i];
-      x_thread[i+1] = x[i+1] / 16.0f;
+      x_thread[i + 1] = x[i + 1] / 16.0f;
-      x_thread[i+2] = x[i+2] / 256.0f;
+      x_thread[i + 2] = x[i + 2] / 256.0f;
-      x_thread[i+3] = x[i+3] / 4096.0f;
+      x_thread[i + 3] = x[i + 3] / 4096.0f;
     }
   }
 
@@ -53,33 +54,35 @@ inline U load_vector(const device T *x, thread U *x_thread) {
 }
 
 template <typename T, typename U, int values_per_thread, int bits>
-inline U load_vector_safe(const device T *x, thread U *x_thread, int N) {
+inline U load_vector_safe(const device T* x, thread U* x_thread, int N) {
-  static_assert(bits == 2 || bits == 4 || bits == 8, "Template undefined for bits not in {2, 4, 8}");
+  static_assert(
+      bits == 2 || bits == 4 || bits == 8,
+      "Template undefined for bits not in {2, 4, 8}");
 
   U sum = 0;
 
   if (bits == 2) {
     for (int i = 0; i < N; i += 4) {
-      sum += x[i] + x[i+1] + x[i+2] + x[i+3];
+      sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
       x_thread[i] = x[i];
-      x_thread[i+1] = x[i+1] / 4.0f;
+      x_thread[i + 1] = x[i + 1] / 4.0f;
-      x_thread[i+2] = x[i+2] / 16.0f;
+      x_thread[i + 2] = x[i + 2] / 16.0f;
-      x_thread[i+3] = x[i+3] / 64.0f;
+      x_thread[i + 3] = x[i + 3] / 64.0f;
     }
-    for (int i=N; i<values_per_thread; i++) {
+    for (int i = N; i < values_per_thread; i++) {
       x_thread[i] = 0;
     }
   }
 
   else if (bits == 4) {
     for (int i = 0; i < N; i += 4) {
-      sum += x[i] + x[i+1] + x[i+2] + x[i+3];
+      sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
       x_thread[i] = x[i];
-      x_thread[i+1] = x[i+1] / 16.0f;
+      x_thread[i + 1] = x[i + 1] / 16.0f;
-      x_thread[i+2] = x[i+2] / 256.0f;
+      x_thread[i + 2] = x[i + 2] / 256.0f;
-      x_thread[i+3] = x[i+3] / 4096.0f;
+      x_thread[i + 3] = x[i + 3] / 4096.0f;
     }
-    for (int i=N; i<values_per_thread; i++) {
+    for (int i = N; i < values_per_thread; i++) {
       x_thread[i] = 0;
     }
   }
@@ -89,7 +92,7 @@ inline U load_vector_safe(const device T *x, thread U *x_thread, int N) {
       sum += x[i];
       x_thread[i] = x[i];
     }
-    for (int i=N; i<values_per_thread; i++) {
+    for (int i = N; i < values_per_thread; i++) {
       x_thread[i] = 0;
     }
   }
@@ -98,29 +101,36 @@ inline U load_vector_safe(const device T *x, thread U *x_thread, int N) {
 }
 
 template <typename U, int values_per_thread, int bits>
-inline U qdot(const device uint8_t* w, const thread U *x_thread, U scale, U bias, U sum) {
+inline U qdot(
-  static_assert(bits == 2 || bits == 4 || bits == 8, "Template undefined for bits not in {2, 4, 8}");
+    const device uint8_t* w,
+    const thread U* x_thread,
+    U scale,
+    U bias,
+    U sum) {
+  static_assert(
+      bits == 2 || bits == 4 || bits == 8,
+      "Template undefined for bits not in {2, 4, 8}");
 
   U accum = 0;
 
   if (bits == 2) {
     for (int i = 0; i < (values_per_thread / 4); i++) {
-      accum += (
+      accum +=
-          x_thread[4*i] * (w[i] & 0x03)
+          (x_thread[4 * i] * (w[i] & 0x03) +
-          + x_thread[4*i+1] * (w[i] & 0x0c)
+           x_thread[4 * i + 1] * (w[i] & 0x0c) +
-          + x_thread[4*i+2] * (w[i] & 0x30)
+           x_thread[4 * i + 2] * (w[i] & 0x30) +
-          + x_thread[4*i+3] * (w[i] & 0xc0));
+           x_thread[4 * i + 3] * (w[i] & 0xc0));
     }
   }
 
   else if (bits == 4) {
     const device uint16_t* ws = (const device uint16_t*)w;
     for (int i = 0; i < (values_per_thread / 4); i++) {
-      accum += (
+      accum +=
-          x_thread[4*i] * (ws[i] & 0x000f)
+          (x_thread[4 * i] * (ws[i] & 0x000f) +
-          + x_thread[4*i+1] * (ws[i] & 0x00f0)
+           x_thread[4 * i + 1] * (ws[i] & 0x00f0) +
-          + x_thread[4*i+2] * (ws[i] & 0x0f00)
+           x_thread[4 * i + 2] * (ws[i] & 0x0f00) +
-          + x_thread[4*i+3] * (ws[i] & 0xf000));
+           x_thread[4 * i + 3] * (ws[i] & 0xf000));
     }
   }
 
@@ -134,29 +144,37 @@ inline U qdot(const device uint8_t* w, const thread U *x_thread, U scale, U bias
 }
 
 template <typename U, int values_per_thread, int bits>
-inline U qdot_safe(const device uint8_t* w, const thread U *x_thread, U scale, U bias, U sum, int N) {
+inline U qdot_safe(
-  static_assert(bits == 2 || bits == 4 || bits == 8, "Template undefined for bits not in {2, 4, 8}");
+    const device uint8_t* w,
+    const thread U* x_thread,
+    U scale,
+    U bias,
+    U sum,
+    int N) {
+  static_assert(
+      bits == 2 || bits == 4 || bits == 8,
+      "Template undefined for bits not in {2, 4, 8}");
 
   U accum = 0;
 
   if (bits == 2) {
     for (int i = 0; i < (N / 4); i++) {
-      accum += (
+      accum +=
-          x_thread[4*i] * (w[i] & 0x03)
+          (x_thread[4 * i] * (w[i] & 0x03) +
-          + x_thread[4*i+1] * (w[i] & 0x0c)
+           x_thread[4 * i + 1] * (w[i] & 0x0c) +
-          + x_thread[4*i+2] * (w[i] & 0x30)
+           x_thread[4 * i + 2] * (w[i] & 0x30) +
-          + x_thread[4*i+3] * (w[i] & 0xc0));
+           x_thread[4 * i + 3] * (w[i] & 0xc0));
     }
   }
 
   else if (bits == 4) {
     const device uint16_t* ws = (const device uint16_t*)w;
     for (int i = 0; i < (N / 4); i++) {
-      accum += (
+      accum +=
-          x_thread[4*i] * (ws[i] & 0x000f)
+          (x_thread[4 * i] * (ws[i] & 0x000f) +
-          + x_thread[4*i+1] * (ws[i] & 0x00f0)
+           x_thread[4 * i + 1] * (ws[i] & 0x00f0) +
-          + x_thread[4*i+2] * (ws[i] & 0x0f00)
+           x_thread[4 * i + 2] * (ws[i] & 0x0f00) +
-          + x_thread[4*i+3] * (ws[i] & 0xf000));
+           x_thread[4 * i + 3] * (ws[i] & 0xf000));
     }
   }
 
@@ -170,16 +188,19 @@ inline U qdot_safe(const device uint8_t* w, const thread U *x_thread, U scale, U
 }
 
 template <typename U, int values_per_thread, int bits>
-inline void qouter(const thread uint8_t* w, U x, U scale, U bias, thread U* result) {
+inline void
-  static_assert(bits == 2 || bits == 4 || bits == 8, "Template undefined for bits not in {2, 4, 8}");
+qouter(const thread uint8_t* w, U x, U scale, U bias, thread U* result) {
+  static_assert(
+      bits == 2 || bits == 4 || bits == 8,
+      "Template undefined for bits not in {2, 4, 8}");
 
   if (bits == 2) {
     U s[4] = {scale, scale / 4.0f, scale / 16.0f, scale / 64.0f};
     for (int i = 0; i < (values_per_thread / 4); i++) {
-      result[4*i] += x * (s[0] * (w[i] & 0x03) + bias);
+      result[4 * i] += x * (s[0] * (w[i] & 0x03) + bias);
-      result[4*i+1] += x * (s[1] * (w[i] & 0x0c) + bias);
+      result[4 * i + 1] += x * (s[1] * (w[i] & 0x0c) + bias);
-      result[4*i+2] += x * (s[2] * (w[i] & 0x30) + bias);
+      result[4 * i + 2] += x * (s[2] * (w[i] & 0x30) + bias);
-      result[4*i+3] += x * (s[3] * (w[i] & 0xc0) + bias);
+      result[4 * i + 3] += x * (s[3] * (w[i] & 0xc0) + bias);
     }
   }
 
@@ -187,10 +208,10 @@ inline void qouter(const thread uint8_t* w, U x, U scale, U bias, thread U* resu
     const thread uint16_t* ws = (const thread uint16_t*)w;
     U s[4] = {scale, scale / 16.0f, scale / 256.0f, scale / 4096.0f};
     for (int i = 0; i < (values_per_thread / 4); i++) {
-      result[4*i] += x * (s[0] * (ws[i] & 0x000f) + bias);
+      result[4 * i] += x * (s[0] * (ws[i] & 0x000f) + bias);
-      result[4*i+1] += x * (s[1] * (ws[i] & 0x00f0) + bias);
+      result[4 * i + 1] += x * (s[1] * (ws[i] & 0x00f0) + bias);
-      result[4*i+2] += x * (s[2] * (ws[i] & 0x0f00) + bias);
+      result[4 * i + 2] += x * (s[2] * (ws[i] & 0x0f00) + bias);
-      result[4*i+3] += x * (s[3] * (ws[i] & 0xf000) + bias);
+      result[4 * i + 3] += x * (s[3] * (ws[i] & 0xf000) + bias);
     }
   }
 
@@ -202,27 +223,38 @@ inline void qouter(const thread uint8_t* w, U x, U scale, U bias, thread U* resu
 }
 
 template <typename U, int N, int bits>
-inline void dequantize(const device uint8_t* w, U scale, U bias, threadgroup U* w_local) {
+inline void
-  static_assert(bits == 2 || bits == 4 || bits == 8, "Template undefined for bits not in {2, 4, 8}");
+dequantize(const device uint8_t* w, U scale, U bias, threadgroup U* w_local) {
+  static_assert(
+      bits == 2 || bits == 4 || bits == 8,
+      "Template undefined for bits not in {2, 4, 8}");
 
   if (bits == 2) {
-    U s[4] = {scale, scale / static_cast<U>(4.0f), scale / static_cast<U>(16.0f), scale / static_cast<U>(64.0f)};
+    U s[4] = {
+        scale,
+        scale / static_cast<U>(4.0f),
+        scale / static_cast<U>(16.0f),
+        scale / static_cast<U>(64.0f)};
     for (int i = 0; i < (N / 4); i++) {
-      w_local[4*i] = s[0] * (w[i] & 0x03) + bias;
+      w_local[4 * i] = s[0] * (w[i] & 0x03) + bias;
-      w_local[4*i+1] = s[1] * (w[i] & 0x0c) + bias;
+      w_local[4 * i + 1] = s[1] * (w[i] & 0x0c) + bias;
-      w_local[4*i+2] = s[2] * (w[i] & 0x30) + bias;
+      w_local[4 * i + 2] = s[2] * (w[i] & 0x30) + bias;
-      w_local[4*i+3] = s[3] * (w[i] & 0xc0) + bias;
+      w_local[4 * i + 3] = s[3] * (w[i] & 0xc0) + bias;
     }
   }
 
   else if (bits == 4) {
     const device uint16_t* ws = (const device uint16_t*)w;
-    U s[4] = {scale, scale / static_cast<U>(16.0f), scale / static_cast<U>(256.0f), scale / static_cast<U>(4096.0f)};
+    U s[4] = {
+        scale,
+        scale / static_cast<U>(16.0f),
+        scale / static_cast<U>(256.0f),
+        scale / static_cast<U>(4096.0f)};
     for (int i = 0; i < (N / 4); i++) {
-      w_local[4*i] = s[0] * (ws[i] & 0x000f) + bias;
+      w_local[4 * i] = s[0] * (ws[i] & 0x000f) + bias;
-      w_local[4*i+1] = s[1] * (ws[i] & 0x00f0) + bias;
+      w_local[4 * i + 1] = s[1] * (ws[i] & 0x00f0) + bias;
-      w_local[4*i+2] = s[2] * (ws[i] & 0x0f00) + bias;
+      w_local[4 * i + 2] = s[2] * (ws[i] & 0x0f00) + bias;
-      w_local[4*i+3] = s[3] * (ws[i] & 0xf000) + bias;
+      w_local[4 * i + 3] = s[3] * (ws[i] & 0xf000) + bias;
     }
   }
 
@@ -243,13 +275,20 @@ template <
     short group_size,
     short bits>
 struct QuantizedBlockLoader {
-  static_assert(BCOLS <= group_size, "The group size should be larger than the columns");
+  static_assert(
-  static_assert(group_size % BCOLS == 0, "The group size should be divisible by the columns");
+      BCOLS <= group_size,
-  static_assert(bits == 2 || bits == 4 || bits == 8, "Template undefined for bits not in {2, 4, 8}");
+      "The group size should be larger than the columns");
+  static_assert(
+      group_size % BCOLS == 0,
+      "The group size should be divisible by the columns");
+  static_assert(
+      bits == 2 || bits == 4 || bits == 8,
+      "Template undefined for bits not in {2, 4, 8}");
 
   MLX_MTL_CONST short pack_factor = 32 / bits;
   MLX_MTL_CONST short BCOLS_PACKED = BCOLS / pack_factor;
-  MLX_MTL_CONST short n_reads = (BCOLS_PACKED * BROWS < tgp_size) ? 1 : (BCOLS_PACKED * BROWS) / tgp_size;
+  MLX_MTL_CONST short n_reads =
+      (BCOLS_PACKED * BROWS < tgp_size) ? 1 : (BCOLS_PACKED * BROWS) / tgp_size;
   MLX_MTL_CONST short group_steps = group_size / BCOLS;
 
   const int src_ld;
@@ -275,7 +314,8 @@ struct QuantizedBlockLoader {
       ushort simd_group_id [[simdgroup_index_in_threadgroup]],
       ushort simd_lane_id [[thread_index_in_simdgroup]])
       : src_ld(src_ld_),
-        tile_stride(reduction_dim ? BCOLS_PACKED : BROWS * src_ld / pack_factor),
+        tile_stride(
+            reduction_dim ? BCOLS_PACKED : BROWS * src_ld / pack_factor),
         group_step_cnt(0),
         group_stride(BROWS * src_ld / group_size),
         thread_idx(simd_group_id * 32 + simd_lane_id),
@@ -293,8 +333,9 @@ struct QuantizedBlockLoader {
 
     T scale = *scales;
     T bias = *biases;
-    for (int i=0; i<n_reads; i++) {
+    for (int i = 0; i < n_reads; i++) {
-      dequantize<T, pack_factor, bits>((device uint8_t*)(src + i), scale, bias, dst + i * pack_factor);
+      dequantize<T, pack_factor, bits>(
+          (device uint8_t*)(src + i), scale, bias, dst + i * pack_factor);
     }
   }
 
@@ -304,14 +345,14 @@ struct QuantizedBlockLoader {
     }
 
     if (reduction_dim == 1 && bi >= src_tile_dim.y) {
-      for (int i=0; i<n_reads*pack_factor; i++) {
+      for (int i = 0; i < n_reads * pack_factor; i++) {
         dst[i] = T(0);
       }
       return;
     }
 
     if (reduction_dim == 0 && bi >= src_tile_dim.x) {
-      for (int i=0; i<n_reads*pack_factor; i++) {
+      for (int i = 0; i < n_reads * pack_factor; i++) {
         dst[i] = T(0);
       }
       return;
@@ -319,8 +360,9 @@ struct QuantizedBlockLoader {
 
     T scale = *scales;
     T bias = *biases;
-    for (int i=0; i<n_reads; i++) {
+    for (int i = 0; i < n_reads; i++) {
-      dequantize<T, pack_factor, bits>((device uint8_t*)(src + i), scale, bias, dst + i * pack_factor);
+      dequantize<T, pack_factor, bits>(
+          (device uint8_t*)(src + i), scale, bias, dst + i * pack_factor);
     }
   }
 
@@ -357,7 +399,6 @@ template <typename T, int group_size, int bits, int packs_per_thread>
     uint3 tid [[threadgroup_position_in_grid]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   constexpr int num_simdgroups = 2;
   constexpr int results_per_simdgroup = 4;
   constexpr int pack_factor = 32 / bits;
@@ -373,7 +414,8 @@ template <typename T, int group_size, int bits, int packs_per_thread>
   // Adjust positions
   const int in_vec_size_w = in_vec_size / pack_factor;
   const int in_vec_size_g = in_vec_size / group_size;
-  const int out_row = tid.y * (num_simdgroups * results_per_simdgroup) + simd_gid * results_per_simdgroup;
+  const int out_row = tid.y * (num_simdgroups * results_per_simdgroup) +
+      simd_gid * results_per_simdgroup;
   w += out_row * in_vec_size_w + simd_lid * packs_per_thread;
   scales += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
   biases += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
@@ -384,7 +426,8 @@ template <typename T, int group_size, int bits, int packs_per_thread>
     U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
 
     for (int row = 0; row < results_per_simdgroup; row++) {
-      const device uint8_t* wl = (const device uint8_t *)(w + row * in_vec_size_w);
+      const device uint8_t* wl =
+          (const device uint8_t*)(w + row * in_vec_size_w);
       const device T* sl = scales + row * in_vec_size_g;
       const device T* bl = biases + row * in_vec_size_g;
 
@@ -407,7 +450,6 @@ template <typename T, int group_size, int bits, int packs_per_thread>
   }
 }
 
-
 template <typename T, const int group_size, const int bits>
 [[kernel]] void qmv(
     const device uint32_t* w [[buffer(0)]],
@@ -420,7 +462,6 @@ template <typename T, const int group_size, const int bits>
     uint3 tid [[threadgroup_position_in_grid]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   constexpr int num_simdgroups = 2;
   constexpr int results_per_simdgroup = 4;
   constexpr int packs_per_thread = 1;
@@ -437,7 +478,8 @@ template <typename T, const int group_size, const int bits>
   // Adjust positions
   const int in_vec_size_w = in_vec_size / pack_factor;
   const int in_vec_size_g = in_vec_size / group_size;
-  const int out_row = tid.y * (num_simdgroups * results_per_simdgroup) + simd_gid * results_per_simdgroup;
+  const int out_row = tid.y * (num_simdgroups * results_per_simdgroup) +
+      simd_gid * results_per_simdgroup;
   const int used_out_row = min(out_vec_size - results_per_simdgroup, out_row);
 
   if (out_row >= out_vec_size) {
@@ -454,17 +496,19 @@ template <typename T, const int group_size, const int bits>
     y += tid.z * out_vec_size + out_row;
 
     int k = 0;
-    for (; k < in_vec_size-block_size; k += block_size) {
+    for (; k < in_vec_size - block_size; k += block_size) {
       U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
 
       for (int row = 0; out_row + row < out_vec_size; row++) {
-        const device uint8_t* wl = (const device uint8_t *)(w + row * in_vec_size_w);
+        const device uint8_t* wl =
+            (const device uint8_t*)(w + row * in_vec_size_w);
         const device T* sl = scales + row * in_vec_size_g;
         const device T* bl = biases + row * in_vec_size_g;
 
         U s = sl[0];
         U b = bl[0];
-        result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
+        result[row] +=
+            qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
       }
 
       w += block_size / pack_factor;
@@ -472,11 +516,16 @@ template <typename T, const int group_size, const int bits>
       biases += block_size / group_size;
       x += block_size;
     }
-    const int remaining = clamp(static_cast<int>(in_vec_size - k - simd_lid * values_per_thread), 0, values_per_thread);
+    const int remaining = clamp(
-    U sum = load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);
+        static_cast<int>(in_vec_size - k - simd_lid * values_per_thread),
+        0,
+        values_per_thread);
+    U sum =
+        load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);
 
     for (int row = 0; out_row + row < out_vec_size; row++) {
-      const device uint8_t* wl = (const device uint8_t *)(w + row * in_vec_size_w);
+      const device uint8_t* wl =
+          (const device uint8_t*)(w + row * in_vec_size_w);
       const device T* sl = scales + row * in_vec_size_g;
       const device T* bl = biases + row * in_vec_size_g;
 
@@ -502,17 +551,19 @@ template <typename T, const int group_size, const int bits>
     y += tid.z * out_vec_size + used_out_row;
 
     int k = 0;
-    for (; k < in_vec_size-block_size; k += block_size) {
+    for (; k < in_vec_size - block_size; k += block_size) {
       U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
 
       for (int row = 0; row < results_per_simdgroup; row++) {
-        const device uint8_t* wl = (const device uint8_t *)(w + row * in_vec_size_w);
+        const device uint8_t* wl =
+            (const device uint8_t*)(w + row * in_vec_size_w);
         const device T* sl = scales + row * in_vec_size_g;
         const device T* bl = biases + row * in_vec_size_g;
 
         U s = sl[0];
         U b = bl[0];
-        result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
+        result[row] +=
+            qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
       }
 
       w += block_size / pack_factor;
@@ -520,17 +571,23 @@ template <typename T, const int group_size, const int bits>
       biases += block_size / group_size;
       x += block_size;
     }
-    const int remaining = clamp(static_cast<int>(in_vec_size - k - simd_lid * values_per_thread), 0, values_per_thread);
+    const int remaining = clamp(
-    U sum = load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);
+        static_cast<int>(in_vec_size - k - simd_lid * values_per_thread),
+        0,
+        values_per_thread);
+    U sum =
+        load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);
 
     for (int row = 0; row < results_per_simdgroup; row++) {
-      const device uint8_t* wl = (const device uint8_t *)(w + row * in_vec_size_w);
+      const device uint8_t* wl =
+          (const device uint8_t*)(w + row * in_vec_size_w);
       const device T* sl = scales + row * in_vec_size_g;
       const device T* bl = biases + row * in_vec_size_g;
 
       U s = sl[0];
       U b = bl[0];
-      result[row] += qdot_safe<U, values_per_thread, bits>(wl, x_thread, s, b, sum, remaining);
+      result[row] += qdot_safe<U, values_per_thread, bits>(
+          wl, x_thread, s, b, sum, remaining);
     }
 
     for (int row = 0; row < results_per_simdgroup; row++) {
@@ -542,7 +599,6 @@ template <typename T, const int group_size, const int bits>
   }
 }
 
-
 template <typename T, const int group_size, const int bits>
 [[kernel]] void qvm(
     const device T* x [[buffer(0)]],
@@ -555,7 +611,6 @@ template <typename T, const int group_size, const int bits>
     uint3 tid [[threadgroup_position_in_grid]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   constexpr int num_simdgroups = 8;
   constexpr int pack_factor = 32 / bits;
   constexpr int blocksize = SIMD_SIZE;
@@ -590,7 +645,8 @@ template <typename T, const int group_size, const int bits>
     bias = biases[(i + simd_lid) * out_vec_size_g];
     w_local = w[(i + simd_lid) * out_vec_size_w];
 
-    qouter<U, pack_factor, bits>((thread uint8_t *)&w_local, x_local, scale, bias, result);
+    qouter<U, pack_factor, bits>(
+        (thread uint8_t*)&w_local, x_local, scale, bias, result);
   }
   if (static_cast<int>(i + simd_lid) < in_vec_size) {
     x_local = x[i + simd_lid];
@@ -603,25 +659,32 @@ template <typename T, const int group_size, const int bits>
     bias = 0;
     w_local = 0;
   }
-  qouter<U, pack_factor, bits>((thread uint8_t *)&w_local, x_local, scale, bias, result);
+  qouter<U, pack_factor, bits>(
+      (thread uint8_t*)&w_local, x_local, scale, bias, result);
 
-  // Accumulate in the simdgroup
+// Accumulate in the simdgroup
-  #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-  for (int k=0; k<pack_factor; k++) {
+  for (int k = 0; k < pack_factor; k++) {
     result[k] = simd_sum(result[k]);
   }
 
   // Store the result
   if (simd_lid == 0) {
-    #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-    for (int k=0; k<pack_factor; k++) {
+    for (int k = 0; k < pack_factor; k++) {
       y[k] = static_cast<T>(result[k]);
     }
   }
 }
 
-
+template <
-template <typename T, const int BM, const int BK, const int BN, const int group_size, const int bits, const bool aligned_N>
+    typename T,
+    const int BM,
+    const int BK,
+    const int BN,
+    const int group_size,
+    const int bits,
+    const bool aligned_N>
 [[kernel]] void qmm_t(
     const device T* x [[buffer(0)]],
     const device uint32_t* w [[buffer(1)]],
@@ -635,7 +698,6 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
     uint lid [[thread_index_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   static_assert(BK >= SIMD_SIZE, "BK should be larger than SIMD_SIZE");
   static_assert(BK % SIMD_SIZE == 0, "BK should be divisible by SIMD_SIZE");
 
@@ -647,9 +709,19 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
   constexpr int BK_padded = (BK + 16 / sizeof(T));
 
   // Instantiate the appropriate BlockMMA and Loader
-  using mma_t = mlx::steel::BlockMMA<T, T, BM, BN, BK, WM, WN, false, true, BK_padded, BK_padded>;
+  using mma_t = mlx::steel::
-  using loader_x_t = mlx::steel::BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE>;
+      BlockMMA<T, T, BM, BN, BK, WM, WN, false, true, BK_padded, BK_padded>;
-  using loader_w_t = QuantizedBlockLoader<T, BN, BK, BK_padded, 1, WM * WN * SIMD_SIZE, group_size, bits>;
+  using loader_x_t =
+      mlx::steel::BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE>;
+  using loader_w_t = QuantizedBlockLoader<
+      T,
+      BN,
+      BK,
+      BK_padded,
+      1,
+      WM * WN * SIMD_SIZE,
+      group_size,
+      bits>;
 
   threadgroup T Xs[BM * BK_padded];
   threadgroup T Ws[BN * BK_padded];
@@ -675,7 +747,7 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
 
   if (num_els < BM) {
     if (!aligned_N && num_outs < BN) {
-      for (int k=0; k<K; k += BK) {
+      for (int k = 0; k < K; k += BK) {
         threadgroup_barrier(mem_flags::mem_threadgroup);
         loader_x.load_safe(short2(BK, num_els));
         loader_w.load_safe(short2(BK, num_outs));
@@ -685,7 +757,7 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
         loader_w.next();
       }
     } else {
-      for (int k=0; k<K; k += BK) {
+      for (int k = 0; k < K; k += BK) {
         threadgroup_barrier(mem_flags::mem_threadgroup);
         loader_x.load_safe(short2(BK, num_els));
         loader_w.load_unsafe();
@@ -697,7 +769,7 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
     }
   } else {
     if (!aligned_N && num_outs < BN) {
-      for (int k=0; k<K; k += BK) {
+      for (int k = 0; k < K; k += BK) {
         threadgroup_barrier(mem_flags::mem_threadgroup);
         loader_x.load_unsafe();
         loader_w.load_safe(short2(BK, num_outs));
@@ -707,7 +779,7 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
         loader_w.next();
       }
     } else {
-      for (int k=0; k<K; k += BK) {
+      for (int k = 0; k < K; k += BK) {
         threadgroup_barrier(mem_flags::mem_threadgroup);
         loader_x.load_unsafe();
         loader_w.load_unsafe();
@@ -728,8 +800,13 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
   }
 }
 
-
+template <
-template <typename T, const int BM, const int BK, const int BN, const int group_size, const int bits>
+    typename T,
+    const int BM,
+    const int BK,
+    const int BN,
+    const int group_size,
+    const int bits>
 [[kernel]] void qmm_n(
     const device T* x [[buffer(0)]],
     const device uint32_t* w [[buffer(1)]],
@@ -743,7 +820,6 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
     uint lid [[thread_index_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   static_assert(BK >= SIMD_SIZE, "BK should be larger than SIMD_SIZE");
   static_assert(BK % SIMD_SIZE == 0, "BK should be divisible by SIMD_SIZE");
 
@@ -756,9 +832,19 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
   constexpr int BN_padded = (BN + 16 / sizeof(T));
 
   // Instantiate the appropriate BlockMMA and Loader
-  using mma_t = mlx::steel::BlockMMA<T, T, BM, BN, BK, WM, WN, false, false, BK_padded, BN_padded>;
+  using mma_t = mlx::steel::
-  using loader_x_t = mlx::steel::BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE, 1, 4>;
+      BlockMMA<T, T, BM, BN, BK, WM, WN, false, false, BK_padded, BN_padded>;
-  using loader_w_t = QuantizedBlockLoader<T, BK, BN, BN_padded, 0, WM * WN * SIMD_SIZE, group_size, bits>;
+  using loader_x_t = mlx::steel::
+      BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE, 1, 4>;
+  using loader_w_t = QuantizedBlockLoader<
+      T,
+      BK,
+      BN,
+      BN_padded,
+      0,
+      WM * WN * SIMD_SIZE,
+      group_size,
+      bits>;
 
   threadgroup T Xs[BM * BK_padded];
   threadgroup T Ws[BK * BN_padded];
@@ -780,8 +866,8 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
 
   if (num_els < BM) {
     if ((K % BK) != 0) {
-      const int k_blocks = K/BK;
+      const int k_blocks = K / BK;
-      for (int k=0; k<k_blocks; k++) {
+      for (int k = 0; k < k_blocks; k++) {
         threadgroup_barrier(mem_flags::mem_threadgroup);
         loader_x.load_safe(short2(BK, num_els));
         loader_w.load_unsafe();
@@ -797,7 +883,7 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
       threadgroup_barrier(mem_flags::mem_threadgroup);
       mma_op.mma(Xs, Ws);
     } else {
-      for (int k=0; k<K; k += BK) {
+      for (int k = 0; k < K; k += BK) {
         threadgroup_barrier(mem_flags::mem_threadgroup);
         loader_x.load_safe(short2(BK, num_els));
         loader_w.load_unsafe();
@@ -809,8 +895,8 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
     }
   } else {
     if ((K % BK) != 0) {
-      const int k_blocks = K/BK;
+      const int k_blocks = K / BK;
-      for (int k=0; k<k_blocks; k++) {
+      for (int k = 0; k < k_blocks; k++) {
         threadgroup_barrier(mem_flags::mem_threadgroup);
         loader_x.load_unsafe();
         loader_w.load_unsafe();
@@ -826,7 +912,7 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
       threadgroup_barrier(mem_flags::mem_threadgroup);
       mma_op.mma(Xs, Ws);
     } else {
-      for (int k=0; k<K; k += BK) {
+      for (int k = 0; k < K; k += BK) {
         threadgroup_barrier(mem_flags::mem_threadgroup);
         loader_x.load_unsafe();
         loader_w.load_unsafe();
@@ -847,10 +933,10 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
   }
 }
 
-
 #define instantiate_qmv_fast(name, itype, group_size, bits, packs_per_thread) \
-  template [[host_name("qmv_" #name "_gs_" #group_size "_b_" #bits "_fast")]] \
+  template [[host_name("qmv_" #name "_gs_" #group_size "_b_" #bits            \
-  [[kernel]] void qmv_fast<itype, group_size, bits, packs_per_thread>( \
+                       "_fast")]] [[kernel]] void                             \
+  qmv_fast<itype, group_size, bits, packs_per_thread>(                        \
       const device uint32_t* w [[buffer(0)]],                                 \
       const device itype* scales [[buffer(1)]],                               \
       const device itype* biases [[buffer(2)]],                               \
@@ -862,11 +948,13 @@ template <typename T, const int BM, const int BK, const int BN, const int group_
       uint simd_gid [[simdgroup_index_in_threadgroup]],                       \
       uint simd_lid [[thread_index_in_simdgroup]]);
 
+// clang-format off
 #define instantiate_qmv_fast_types(group_size, bits, packs_per_thread)     \
   instantiate_qmv_fast(float32, float, group_size, bits, packs_per_thread) \
   instantiate_qmv_fast(float16, half, group_size, bits, packs_per_thread)  \
-  instantiate_qmv_fast(bfloat16, bfloat16_t, group_size, bits, packs_per_thread)
+  instantiate_qmv_fast(bfloat16, bfloat16_t, group_size, bits, packs_per_thread) // clang-format on
 
+// clang-format off
 instantiate_qmv_fast_types(128, 2, 1)
 instantiate_qmv_fast_types(128, 4, 2)
 instantiate_qmv_fast_types(128, 8, 2)
@@ -875,11 +963,12 @@ instantiate_qmv_fast_types( 64, 4, 2)
 instantiate_qmv_fast_types( 64, 8, 2)
 instantiate_qmv_fast_types( 32, 2, 1)
 instantiate_qmv_fast_types( 32, 4, 2)
-instantiate_qmv_fast_types( 32, 8, 2)
+instantiate_qmv_fast_types( 32, 8, 2) // clang-format on
 
 #define instantiate_qmv(name, itype, group_size, bits)  \
-  template [[host_name("qmv_" #name "_gs_" #group_size "_b_" #bits)]] \
+  template [[host_name("qmv_" #name "_gs_" #group_size  \
-  [[kernel]] void qmv<itype, group_size, bits>( \
+                       "_b_" #bits)]] [[kernel]] void   \
+  qmv<itype, group_size, bits>(                         \
       const device uint32_t* w [[buffer(0)]],           \
       const device itype* scales [[buffer(1)]],         \
       const device itype* biases [[buffer(2)]],         \
@@ -891,11 +980,13 @@ instantiate_qmv_fast_types( 32, 8, 2)
       uint simd_gid [[simdgroup_index_in_threadgroup]], \
       uint simd_lid [[thread_index_in_simdgroup]]);
 
+// clang-format off
 #define instantiate_qmv_types(group_size, bits)     \
   instantiate_qmv(float32, float, group_size, bits) \
   instantiate_qmv(float16, half, group_size, bits)  \
-  instantiate_qmv(bfloat16, bfloat16_t, group_size, bits)
+  instantiate_qmv(bfloat16, bfloat16_t, group_size, bits) // clang-format on
 
+    // clang-format off
 instantiate_qmv_types(128, 2)
 instantiate_qmv_types(128, 4)
 instantiate_qmv_types(128, 8)
@@ -904,11 +995,12 @@ instantiate_qmv_types( 64, 4)
 instantiate_qmv_types( 64, 8)
 instantiate_qmv_types( 32, 2)
 instantiate_qmv_types( 32, 4)
-instantiate_qmv_types( 32, 8)
+instantiate_qmv_types( 32, 8) // clang-format on
 
 #define instantiate_qvm(name, itype, group_size, bits)  \
-  template [[host_name("qvm_" #name "_gs_" #group_size "_b_" #bits)]] \
+  template [[host_name("qvm_" #name "_gs_" #group_size  \
-  [[kernel]] void qvm<itype, group_size, bits>( \
+                       "_b_" #bits)]] [[kernel]] void   \
+  qvm<itype, group_size, bits>(                         \
       const device itype* x [[buffer(0)]],              \
       const device uint32_t* w [[buffer(1)]],           \
       const device itype* scales [[buffer(2)]],         \
@@ -920,11 +1012,13 @@ instantiate_qmv_types( 32, 8)
       uint simd_gid [[simdgroup_index_in_threadgroup]], \
       uint simd_lid [[thread_index_in_simdgroup]]);
 
+// clang-format off
 #define instantiate_qvm_types(group_size, bits)     \
   instantiate_qvm(float32, float, group_size, bits) \
   instantiate_qvm(float16, half, group_size, bits)  \
-  instantiate_qvm(bfloat16, bfloat16_t, group_size, bits)
+  instantiate_qvm(bfloat16, bfloat16_t, group_size, bits) // clang-format on
 
+    // clang-format off
 instantiate_qvm_types(128, 2)
 instantiate_qvm_types(128, 4)
 instantiate_qvm_types(128, 8)
@@ -933,11 +1027,12 @@ instantiate_qvm_types( 64, 4)
 instantiate_qvm_types( 64, 8)
 instantiate_qvm_types( 32, 2)
 instantiate_qvm_types( 32, 4)
-instantiate_qvm_types( 32, 8)
+instantiate_qvm_types( 32, 8) // clang-format on
 
 #define instantiate_qmm_t(name, itype, group_size, bits, aligned_N)  \
-  template [[host_name("qmm_t_" #name "_gs_" #group_size "_b_" #bits "_alN_" #aligned_N)]] \
+  template [[host_name("qmm_t_" #name "_gs_" #group_size "_b_" #bits \
-  [[kernel]] void qmm_t<itype, 32, 32, 32, group_size, bits, aligned_N>( \
+                       "_alN_" #aligned_N)]] [[kernel]] void         \
+  qmm_t<itype, 32, 32, 32, group_size, bits, aligned_N>(             \
       const device itype* x [[buffer(0)]],                           \
       const device uint32_t* w [[buffer(1)]],                        \
       const device itype* scales [[buffer(2)]],                      \
@@ -951,14 +1046,16 @@ instantiate_qvm_types( 32, 8)
       uint simd_gid [[simdgroup_index_in_threadgroup]],              \
       uint simd_lid [[thread_index_in_simdgroup]]);
 
+// clang-format off
 #define instantiate_qmm_t_types(group_size, bits)                  \
   instantiate_qmm_t(float32, float, group_size, bits, false)       \
   instantiate_qmm_t(float16, half, group_size, bits, false)        \
   instantiate_qmm_t(bfloat16, bfloat16_t, group_size, bits, false) \
   instantiate_qmm_t(float32, float, group_size, bits, true)        \
   instantiate_qmm_t(float16, half, group_size, bits, true)         \
-  instantiate_qmm_t(bfloat16, bfloat16_t, group_size, bits, true)
+  instantiate_qmm_t(bfloat16, bfloat16_t, group_size, bits, true) // clang-format on
 
+    // clang-format off
 instantiate_qmm_t_types(128, 2)
 instantiate_qmm_t_types(128, 4)
 instantiate_qmm_t_types(128, 8)
@@ -967,11 +1064,12 @@ instantiate_qmm_t_types( 64, 4)
 instantiate_qmm_t_types( 64, 8)
 instantiate_qmm_t_types( 32, 2)
 instantiate_qmm_t_types( 32, 4)
-instantiate_qmm_t_types( 32, 8)
+instantiate_qmm_t_types( 32, 8) // clang-format on
 
 #define instantiate_qmm_n(name, itype, group_size, bits) \
-  template [[host_name("qmm_n_" #name "_gs_" #group_size "_b_" #bits)]] \
+  template [[host_name("qmm_n_" #name "_gs_" #group_size \
-  [[kernel]] void qmm_n<itype, 32, 32, 32, group_size, bits>( \
+                       "_b_" #bits)]] [[kernel]] void    \
+  qmm_n<itype, 32, 32, 32, group_size, bits>(            \
       const device itype* x [[buffer(0)]],               \
       const device uint32_t* w [[buffer(1)]],            \
       const device itype* scales [[buffer(2)]],          \
@@ -985,11 +1083,13 @@ instantiate_qmm_t_types( 32, 8)
       uint simd_gid [[simdgroup_index_in_threadgroup]],  \
       uint simd_lid [[thread_index_in_simdgroup]]);
 
+// clang-format off
 #define instantiate_qmm_n_types(group_size, bits)     \
   instantiate_qmm_n(float32, float, group_size, bits) \
   instantiate_qmm_n(float16, half, group_size, bits)  \
-  instantiate_qmm_n(bfloat16, bfloat16_t, group_size, bits)
+  instantiate_qmm_n(bfloat16, bfloat16_t, group_size, bits) // clang-format on
 
+    // clang-format off
 instantiate_qmm_n_types(128, 2)
 instantiate_qmm_n_types(128, 4)
 instantiate_qmm_n_types(128, 8)
@@ -998,4 +1098,4 @@ instantiate_qmm_n_types( 64, 4)
 instantiate_qmm_n_types( 64, 8)
 instantiate_qmm_n_types( 32, 2)
 instantiate_qmm_n_types( 32, 4)
-instantiate_qmm_n_types( 32, 8)
+instantiate_qmm_n_types( 32, 8) // clang-format on

mlx/backend/metal/kernels/random.metal

@@ -4,8 +4,7 @@
 
 static constexpr constant uint32_t rotations[2][4] = {
     {13, 15, 26, 6},
-  {17, 29, 16, 24}
+    {17, 29, 16, 24}};
-};
 
 union rbits {
   uint2 val;
@@ -13,7 +12,6 @@ union rbits {
 };
 
 rbits threefry2x32_hash(const thread uint2& key, uint2 count) {
-
   uint4 ks = {key.x, key.y, key.x ^ key.y ^ 0x1BD11BDA};
 
   rbits v;

mlx/backend/metal/kernels/reduction/kernels/reduce_all.metal

@@ -1,8 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include "mlx/backend/metal/kernels/reduction/utils.h"
 #include "mlx/backend/metal/kernels/reduction/ops.h"
 #include "mlx/backend/metal/kernels/reduction/reduce_inst.h"
+#include "mlx/backend/metal/kernels/reduction/utils.h"
 
 using namespace metal;
 
@@ -60,14 +60,13 @@ METAL_FUNC U per_thread_all_reduce(
 // All reduce kernel
 ///////////////////////////////////////////////////////////////////////////////
 
-
 // NB: This kernel assumes threads_per_threadgroup is at most
 // 1024. This way with a simd_size of 32, we are guaranteed to
 // complete the reduction in two steps of simd-level reductions.
-template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
+template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void all_reduce(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device mlx_atomic<U> *out [[buffer(1)]],
+    device mlx_atomic<U>* out [[buffer(1)]],
     const device size_t& in_size [[buffer(2)]],
     uint gid [[thread_position_in_grid]],
     uint lid [[thread_position_in_threadgroup]],
@@ -75,11 +74,11 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
     uint simd_per_group [[simdgroups_per_threadgroup]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-
   Op op;
   threadgroup U local_vals[simd_size];
 
-  U total_val = per_thread_all_reduce<T, U, Op, N_READS>(in, in_size, gid, grid_size);
+  U total_val =
+      per_thread_all_reduce<T, U, Op, N_READS>(in, in_size, gid, grid_size);
 
   // Reduction within simd group
   total_val = op.simd_reduce(total_val);
@@ -98,10 +97,10 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
   }
 }
 
-template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
+template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void all_reduce_no_atomics(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device U *out [[buffer(1)]],
+    device U* out [[buffer(1)]],
     const device size_t& in_size [[buffer(2)]],
     uint gid [[thread_position_in_grid]],
     uint lid [[thread_position_in_threadgroup]],
@@ -110,14 +109,16 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint thread_group_id [[threadgroup_position_in_grid]]) {
-
   Op op;
   threadgroup U local_vals[simd_size];
 
-  U total_val = per_thread_all_reduce<T, U, Op, N_READS>(in, in_size, gid, grid_size);
+  U total_val =
+      per_thread_all_reduce<T, U, Op, N_READS>(in, in_size, gid, grid_size);
 
-  // Reduction within simd group (simd_add isn't supported for uint64/int64 types)
+  // Reduction within simd group (simd_add isn't supported for uint64/int64
-  for (uint16_t lane_offset = simd_size/2; lane_offset > 0; lane_offset /= 2) {
+  // types)
+  for (uint16_t lane_offset = simd_size / 2; lane_offset > 0;
+       lane_offset /= 2) {
     total_val = op(total_val, simd_shuffle_down(total_val, lane_offset));
   }
   // Write simd group reduction results to local memory
@@ -128,7 +129,8 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
 
   // Reduction of simdgroup reduction results within threadgroup.
   total_val = lid < simd_per_group ? local_vals[lid] : op.init;
-  for (uint16_t lane_offset = simd_size/2; lane_offset > 0; lane_offset /= 2) {
+  for (uint16_t lane_offset = simd_size / 2; lane_offset > 0;
+       lane_offset /= 2) {
     total_val = op(total_val, simd_shuffle_down(total_val, lane_offset));
   }
 
@@ -139,10 +141,10 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
 }
 
 #define instantiate_all_reduce(name, itype, otype, op)        \
-  template [[host_name("all_reduce_" #name)]] \
+  template [[host_name("all_reduce_" #name)]] [[kernel]] void \
-  [[kernel]] void all_reduce<itype, otype, op>( \
+  all_reduce<itype, otype, op>(                               \
-      const device itype *in [[buffer(0)]], \
+      const device itype* in [[buffer(0)]],                   \
-      device mlx_atomic<otype> *out [[buffer(1)]], \
+      device mlx_atomic<otype>* out [[buffer(1)]],            \
       const device size_t& in_size [[buffer(2)]],             \
       uint gid [[thread_position_in_grid]],                   \
       uint lid [[thread_position_in_threadgroup]],            \
@@ -152,10 +154,10 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
 #define instantiate_all_reduce_no_atomics(name, itype, otype, op)        \
-  template [[host_name("all_reduce_no_atomics_" #name)]] \
+  template [[host_name("all_reduce_no_atomics_" #name)]] [[kernel]] void \
-  [[kernel]] void all_reduce_no_atomics<itype, otype, op>( \
+  all_reduce_no_atomics<itype, otype, op>(                               \
-      const device itype *in [[buffer(0)]], \
+      const device itype* in [[buffer(0)]],                              \
-      device otype *out [[buffer(1)]], \
+      device otype* out [[buffer(1)]],                                   \
       const device size_t& in_size [[buffer(2)]],                        \
       uint gid [[thread_position_in_grid]],                              \
       uint lid [[thread_position_in_threadgroup]],                       \
@@ -170,11 +172,12 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
 ///////////////////////////////////////////////////////////////////////////////
 
 #define instantiate_same_all_reduce_helper(name, tname, type, op) \
-  instantiate_all_reduce(name ##tname, type, type, op<type>)
+  instantiate_all_reduce(name##tname, type, type, op<type>)
 
 #define instantiate_same_all_reduce_na_helper(name, tname, type, op) \
-  instantiate_all_reduce_no_atomics(name ##tname, type, type, op<type>)
+  instantiate_all_reduce_no_atomics(name##tname, type, type, op<type>)
 
+// clang-format off
 instantiate_reduce_ops(instantiate_same_all_reduce_helper, instantiate_reduce_helper_types)
 instantiate_reduce_ops(instantiate_same_all_reduce_na_helper, instantiate_reduce_helper_64b)
 
@@ -182,4 +185,4 @@ instantiate_reduce_from_types(instantiate_all_reduce, and, bool, And)
 instantiate_reduce_from_types(instantiate_all_reduce, or, bool, Or)
 
 // special case bool with larger output type
-instantiate_all_reduce(sumbool_, bool, uint32_t, Sum<uint32_t>)
+instantiate_all_reduce(sumbool_, bool, uint32_t, Sum<uint32_t>) // clang-format on

mlx/backend/metal/kernels/reduction/kernels/reduce_col.metal

@@ -1,8 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include "mlx/backend/metal/kernels/reduction/utils.h"
 #include "mlx/backend/metal/kernels/reduction/ops.h"
 #include "mlx/backend/metal/kernels/reduction/reduce_inst.h"
+#include "mlx/backend/metal/kernels/reduction/utils.h"
 
 using namespace metal;
 
@@ -12,8 +12,8 @@ using namespace metal;
 
 template <typename T, typename U, typename Op>
 [[kernel]] void col_reduce_small(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device U *out [[buffer(1)]],
+    device U* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& reduction_stride [[buffer(3)]],
     const constant size_t& out_size [[buffer(4)]],
@@ -25,7 +25,6 @@ template <typename T, typename U, typename Op>
     const constant size_t* non_col_strides [[buffer(10)]],
     const constant int& non_col_ndim [[buffer(11)]],
     uint tid [[thread_position_in_grid]]) {
-
   // Appease the compiler
   (void)out_size;
 
@@ -40,10 +39,11 @@ template <typename T, typename U, typename Op>
       strides + non_col_ndim,
       ndim - non_col_ndim);
 
-  for(uint i = 0; i < non_col_reductions; i++) {
+  for (uint i = 0; i < non_col_reductions; i++) {
-    size_t in_idx = elem_to_loc(i, non_col_shapes, non_col_strides, non_col_ndim);
+    size_t in_idx =
+        elem_to_loc(i, non_col_shapes, non_col_strides, non_col_ndim);
 
-    for(uint j = 0; j < reduction_size; j++, in_idx += reduction_stride) {
+    for (uint j = 0; j < reduction_size; j++, in_idx += reduction_stride) {
       U val = static_cast<U>(in[in_idx]);
       total_val = op(total_val, val);
     }
@@ -53,10 +53,10 @@ template <typename T, typename U, typename Op>
 }
 
 #define instantiate_col_reduce_small(name, itype, otype, op)        \
-  template [[host_name("col_reduce_small_" #name)]] \
+  template [[host_name("col_reduce_small_" #name)]] [[kernel]] void \
-  [[kernel]] void col_reduce_small<itype, otype, op>( \
+  col_reduce_small<itype, otype, op>(                               \
-      const device itype *in [[buffer(0)]], \
+      const device itype* in [[buffer(0)]],                         \
-      device otype *out [[buffer(1)]], \
+      device otype* out [[buffer(1)]],                              \
       const constant size_t& reduction_size [[buffer(2)]],          \
       const constant size_t& reduction_stride [[buffer(3)]],        \
       const constant size_t& out_size [[buffer(4)]],                \
@@ -112,28 +112,23 @@ METAL_FUNC U _contiguous_strided_reduce(
 
 template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void col_reduce_general(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device mlx_atomic<U> *out [[buffer(1)]],
+    device mlx_atomic<U>* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& reduction_stride [[buffer(3)]],
     const constant size_t& out_size [[buffer(4)]],
     const constant int* shape [[buffer(5)]],
     const constant size_t* strides [[buffer(6)]],
     const constant int& ndim [[buffer(7)]],
-    threadgroup U *local_data [[threadgroup(0)]],
+    threadgroup U* local_data [[threadgroup(0)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]],
     uint3 lsize [[threads_per_threadgroup]]) {
   auto out_idx = tid.x * lsize.x + lid.x;
-  auto in_idx = elem_to_loc(
+  auto in_idx = elem_to_loc(out_idx + tid.z * out_size, shape, strides, ndim);
-    out_idx + tid.z * out_size,
-    shape,
-    strides,
-    ndim
-  );
 
   Op op;
-  if(out_idx < out_size) {
+  if (out_idx < out_size) {
     U val = _contiguous_strided_reduce<T, U, Op, N_READS>(
         in,
         local_data,
@@ -144,7 +139,8 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
         lid.xy,
         lsize.xy);
 
-    // Write out reduction results generated by threadgroups working on specific output element, contiguously.
+    // Write out reduction results generated by threadgroups working on specific
+    // output element, contiguously.
     if (lid.y == 0) {
       op.atomic_update(out, val, out_idx);
     }
@@ -153,29 +149,24 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 
 template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void col_reduce_general_no_atomics(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device U *out [[buffer(1)]],
+    device U* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& reduction_stride [[buffer(3)]],
     const constant size_t& out_size [[buffer(4)]],
     const constant int* shape [[buffer(5)]],
     const constant size_t* strides [[buffer(6)]],
     const constant int& ndim [[buffer(7)]],
-    threadgroup U *local_data [[threadgroup(0)]],
+    threadgroup U* local_data [[threadgroup(0)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]],
     uint3 gid [[thread_position_in_grid]],
     uint3 lsize [[threads_per_threadgroup]],
     uint3 gsize [[threads_per_grid]]) {
   auto out_idx = tid.x * lsize.x + lid.x;
-  auto in_idx = elem_to_loc(
+  auto in_idx = elem_to_loc(out_idx + tid.z * out_size, shape, strides, ndim);
-    out_idx + tid.z * out_size,
-    shape,
-    strides,
-    ndim
-  );
 
-  if(out_idx < out_size) {
+  if (out_idx < out_size) {
     U val = _contiguous_strided_reduce<T, U, Op, N_READS>(
         in,
         local_data,
@@ -186,7 +177,8 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
         lid.xy,
         lsize.xy);
 
-    // Write out reduction results generated by threadgroups working on specific output element, contiguously.
+    // Write out reduction results generated by threadgroups working on specific
+    // output element, contiguously.
     if (lid.y == 0) {
       uint tgsize_y = ceildiv(gsize.y, lsize.y);
       uint tgsize_z = ceildiv(gsize.z, lsize.z);
@@ -196,33 +188,34 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 }
 
 #define instantiate_col_reduce_general(name, itype, otype, op)        \
-  template [[host_name("col_reduce_general_" #name)]] \
+  template [[host_name("col_reduce_general_" #name)]] [[kernel]] void \
-  [[kernel]] void col_reduce_general<itype, otype, op>( \
+  col_reduce_general<itype, otype, op>(                               \
-      const device itype *in [[buffer(0)]], \
+      const device itype* in [[buffer(0)]],                           \
-      device mlx_atomic<otype> *out [[buffer(1)]], \
+      device mlx_atomic<otype>* out [[buffer(1)]],                    \
       const constant size_t& reduction_size [[buffer(2)]],            \
       const constant size_t& reduction_stride [[buffer(3)]],          \
       const constant size_t& out_size [[buffer(4)]],                  \
       const constant int* shape [[buffer(5)]],                        \
       const constant size_t* strides [[buffer(6)]],                   \
       const constant int& ndim [[buffer(7)]],                         \
-      threadgroup otype *local_data [[threadgroup(0)]], \
+      threadgroup otype* local_data [[threadgroup(0)]],               \
       uint3 tid [[threadgroup_position_in_grid]],                     \
       uint3 lid [[thread_position_in_threadgroup]],                   \
       uint3 lsize [[threads_per_threadgroup]]);
 
 #define instantiate_col_reduce_general_no_atomics(name, itype, otype, op)   \
-  template [[host_name("col_reduce_general_no_atomics_" #name)]] \
+  template                                                                  \
-  [[kernel]] void col_reduce_general_no_atomics<itype, otype, op>( \
+      [[host_name("col_reduce_general_no_atomics_" #name)]] [[kernel]] void \
-      const device itype *in [[buffer(0)]], \
+      col_reduce_general_no_atomics<itype, otype, op>(                      \
-      device otype *out [[buffer(1)]], \
+          const device itype* in [[buffer(0)]],                             \
+          device otype* out [[buffer(1)]],                                  \
           const constant size_t& reduction_size [[buffer(2)]],              \
           const constant size_t& reduction_stride [[buffer(3)]],            \
           const constant size_t& out_size [[buffer(4)]],                    \
           const constant int* shape [[buffer(5)]],                          \
           const constant size_t* strides [[buffer(6)]],                     \
           const constant int& ndim [[buffer(7)]],                           \
-      threadgroup otype *local_data [[threadgroup(0)]], \
+          threadgroup otype* local_data [[threadgroup(0)]],                 \
           uint3 tid [[threadgroup_position_in_grid]],                       \
           uint3 lid [[thread_position_in_threadgroup]],                     \
           uint3 gid [[thread_position_in_grid]],                            \
@@ -233,14 +226,17 @@ template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 // Instantiations
 ///////////////////////////////////////////////////////////////////////////////
 
+// clang-format off
 #define instantiate_same_col_reduce_helper(name, tname, type, op)  \
   instantiate_col_reduce_small(name ##tname, type, type, op<type>) \
-  instantiate_col_reduce_general(name ##tname, type, type, op<type>)
+  instantiate_col_reduce_general(name ##tname, type, type, op<type>) // clang-format on
 
+// clang-format off
 #define instantiate_same_col_reduce_na_helper(name, tname, type, op) \
   instantiate_col_reduce_small(name ##tname, type, type, op<type>)   \
-  instantiate_col_reduce_general_no_atomics(name ##tname, type, type, op<type>)
+  instantiate_col_reduce_general_no_atomics(name ##tname, type, type, op<type>) // clang-format on
 
+// clang-format off
 instantiate_reduce_ops(instantiate_same_col_reduce_helper, instantiate_reduce_helper_types)
 instantiate_reduce_ops(instantiate_same_col_reduce_na_helper, instantiate_reduce_helper_64b)
 
@@ -250,4 +246,4 @@ instantiate_reduce_from_types(instantiate_col_reduce_general, or, bool, Or)
 
 instantiate_col_reduce_small(sumbool_, bool, uint32_t, Sum<uint32_t>)
 instantiate_reduce_from_types(instantiate_col_reduce_small, and, bool, And)
-instantiate_reduce_from_types(instantiate_col_reduce_small, or, bool, Or)
+instantiate_reduce_from_types(instantiate_col_reduce_small, or, bool, Or) // clang-format on

mlx/backend/metal/kernels/reduction/kernels/reduce_init.metal

@@ -1,8 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include "mlx/backend/metal/kernels/reduction/utils.h"
 #include "mlx/backend/metal/kernels/reduction/ops.h"
 #include "mlx/backend/metal/kernels/reduction/reduce_inst.h"
+#include "mlx/backend/metal/kernels/reduction/utils.h"
 
 using namespace metal;
 
@@ -12,22 +12,21 @@ using namespace metal;
 
 template <typename T, typename Op>
 [[kernel]] void init_reduce(
-    device T *out [[buffer(0)]],
+    device T* out [[buffer(0)]],
     uint tid [[thread_position_in_grid]]) {
   out[tid] = Op::init;
 }
 
 #define instantiate_init_reduce(name, otype, op)                            \
-  template [[host_name("i" #name)]] \
+  template [[host_name("i" #name)]] [[kernel]] void init_reduce<otype, op>( \
-    [[kernel]] void init_reduce<otype, op>( \
+      device otype * out [[buffer(1)]], uint tid [[thread_position_in_grid]]);
-      device otype *out [[buffer(1)]], \
-      uint tid [[thread_position_in_grid]]);
 
 #define instantiate_init_reduce_helper(name, tname, type, op) \
-  instantiate_init_reduce(name ##tname, type, op<type>)
+  instantiate_init_reduce(name##tname, type, op<type>)
 
+// clang-format off
 instantiate_reduce_ops(instantiate_init_reduce_helper, instantiate_reduce_helper_types)
 instantiate_reduce_ops(instantiate_init_reduce_helper, instantiate_reduce_helper_64b)
 
 instantiate_init_reduce(andbool_, bool, And)
-instantiate_init_reduce(orbool_, bool, Or)
+instantiate_init_reduce(orbool_, bool, Or) // clang-format on

mlx/backend/metal/kernels/reduction/kernels/reduce_row.metal

@@ -1,8 +1,8 @@
 // Copyright © 2023-2024 Apple Inc.
 
-#include "mlx/backend/metal/kernels/reduction/utils.h"
 #include "mlx/backend/metal/kernels/reduction/ops.h"
 #include "mlx/backend/metal/kernels/reduction/reduce_inst.h"
+#include "mlx/backend/metal/kernels/reduction/utils.h"
 
 using namespace metal;
 
@@ -13,8 +13,8 @@ using namespace metal;
 // Each thread reduces for one output
 template <typename T, typename U, typename Op>
 [[kernel]] void row_reduce_general_small(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device U *out [[buffer(1)]],
+    device U* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& out_size [[buffer(3)]],
     const constant size_t& non_row_reductions [[buffer(4)]],
@@ -22,22 +22,21 @@ template <typename T, typename U, typename Op>
     const constant size_t* strides [[buffer(6)]],
     const constant int& ndim [[buffer(7)]],
     uint lid [[thread_position_in_grid]]) {
-
   Op op;
 
   uint out_idx = lid;
 
-  if(out_idx >= out_size) {
+  if (out_idx >= out_size) {
     return;
   }
 
   U total_val = Op::init;
 
-  for(short r = 0; r < short(non_row_reductions); r++) {
+  for (short r = 0; r < short(non_row_reductions); r++) {
     uint in_idx = elem_to_loc(out_idx + r * out_size, shape, strides, ndim);
-    const device T * in_row = in + in_idx;
+    const device T* in_row = in + in_idx;
 
-    for(short i = 0; i < short(reduction_size); i++) {
+    for (short i = 0; i < short(reduction_size); i++) {
       total_val = op(static_cast<U>(in_row[i]), total_val);
     }
   }
@@ -48,8 +47,8 @@ template <typename T, typename U, typename Op>
 // Each simdgroup reduces for one output
 template <typename T, typename U, typename Op>
 [[kernel]] void row_reduce_general_med(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device U *out [[buffer(1)]],
+    device U* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& out_size [[buffer(3)]],
     const constant size_t& non_row_reductions [[buffer(4)]],
@@ -60,45 +59,42 @@ template <typename T, typename U, typename Op>
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_per_group [[dispatch_simdgroups_per_threadgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-
   Op op;
 
   uint out_idx = simd_per_group * tid + simd_group_id;
 
-  if(out_idx >= out_size) {
+  if (out_idx >= out_size) {
     return;
   }
 
   U total_val = Op::init;
 
-  if(short(non_row_reductions) == 1) {
+  if (short(non_row_reductions) == 1) {
     uint in_idx = elem_to_loc(out_idx, shape, strides, ndim);
-    const device T * in_row = in + in_idx;
+    const device T* in_row = in + in_idx;
 
-    for(short i = simd_lane_id; i < short(reduction_size); i += 32) {
+    for (short i = simd_lane_id; i < short(reduction_size); i += 32) {
       total_val = op(static_cast<U>(in_row[i]), total_val);
     }
   }
 
   else if (short(non_row_reductions) >= 32) {
-
+    for (short r = simd_lane_id; r < short(non_row_reductions); r += 32) {
-    for(short r = simd_lane_id; r < short(non_row_reductions); r+=32) {
-
       uint in_idx = elem_to_loc(out_idx + r * out_size, shape, strides, ndim);
-      const device T * in_row = in + in_idx;
+      const device T* in_row = in + in_idx;
 
-      for(short i = 0; i < short(reduction_size); i++) {
+      for (short i = 0; i < short(reduction_size); i++) {
         total_val = op(static_cast<U>(in_row[i]), total_val);
       }
-
     }
 
   }
 
   else {
-
+    const short n_reductions =
-    const short n_reductions = short(reduction_size) * short(non_row_reductions);
+        short(reduction_size) * short(non_row_reductions);
-    const short reductions_per_thread = (n_reductions + simd_size - 1) / simd_size;
+    const short reductions_per_thread =
+        (n_reductions + simd_size - 1) / simd_size;
 
     const short r_st = simd_lane_id / reductions_per_thread;
     const short r_ed = short(non_row_reductions);
@@ -108,34 +104,30 @@ template <typename T, typename U, typename Op>
     const short i_ed = short(reduction_size);
     const short i_jump = reductions_per_thread;
 
-    if(r_st < r_jump) {
+    if (r_st < r_jump) {
-      for(short r = r_st; r < r_ed; r += r_jump) {
+      for (short r = r_st; r < r_ed; r += r_jump) {
-
         uint in_idx = elem_to_loc(out_idx + r * out_size, shape, strides, ndim);
-        const device T * in_row = in + in_idx;
+        const device T* in_row = in + in_idx;
 
-        for(short i = i_st; i < i_ed; i += i_jump) {
+        for (short i = i_st; i < i_ed; i += i_jump) {
           total_val = op(static_cast<U>(in_row[i]), total_val);
         }
-
       }
     }
-
   }
 
-
   total_val = op.simd_reduce(total_val);
 
-  if(simd_lane_id == 0) {
+  if (simd_lane_id == 0) {
     out[out_idx] = total_val;
   }
 }
 
 #define instantiate_row_reduce_small(name, itype, otype, op)                \
-  template[[host_name("row_reduce_general_small_" #name)]] \
+  template [[host_name("row_reduce_general_small_" #name)]] [[kernel]] void \
-  [[kernel]] void row_reduce_general_small<itype, otype, op>( \
+  row_reduce_general_small<itype, otype, op>(                               \
-      const device itype *in [[buffer(0)]], \
+      const device itype* in [[buffer(0)]],                                 \
-      device otype *out [[buffer(1)]], \
+      device otype* out [[buffer(1)]],                                      \
       const constant size_t& reduction_size [[buffer(2)]],                  \
       const constant size_t& out_size [[buffer(3)]],                        \
       const constant size_t& non_row_reductions [[buffer(4)]],              \
@@ -143,10 +135,10 @@ template <typename T, typename U, typename Op>
       const constant size_t* strides [[buffer(6)]],                         \
       const constant int& ndim [[buffer(7)]],                               \
       uint lid [[thread_position_in_grid]]);                                \
-  template[[host_name("row_reduce_general_med_" #name)]] \
+  template [[host_name("row_reduce_general_med_" #name)]] [[kernel]] void   \
-  [[kernel]] void row_reduce_general_med<itype, otype, op>( \
+  row_reduce_general_med<itype, otype, op>(                                 \
-      const device itype *in [[buffer(0)]], \
+      const device itype* in [[buffer(0)]],                                 \
-      device otype *out [[buffer(1)]], \
+      device otype* out [[buffer(1)]],                                      \
       const constant size_t& reduction_size [[buffer(2)]],                  \
       const constant size_t& out_size [[buffer(3)]],                        \
       const constant size_t& non_row_reductions [[buffer(4)]],              \
@@ -217,10 +209,10 @@ METAL_FUNC U per_thread_row_reduce(
   return total_val;
 }
 
-template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
+template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void row_reduce_general(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device mlx_atomic<U> *out [[buffer(1)]],
+    device mlx_atomic<U>* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& out_size [[buffer(3)]],
     const constant size_t& non_row_reductions [[buffer(4)]],
@@ -233,13 +225,21 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_per_group [[simdgroups_per_threadgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-
   (void)non_row_reductions;
 
   Op op;
   threadgroup U local_vals[simd_size];
 
-  U total_val = per_thread_row_reduce<T, U, Op, N_READS>(in, reduction_size, out_size, shape, strides, ndim, lsize.x, lid.x, tid.xy);
+  U total_val = per_thread_row_reduce<T, U, Op, N_READS>(
+      in,
+      reduction_size,
+      out_size,
+      shape,
+      strides,
+      ndim,
+      lsize.x,
+      lid.x,
+      tid.xy);
 
   total_val = op.simd_reduce(total_val);
 
@@ -251,7 +251,7 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
 
   // Reduction within thread group
   //    Only needed if multiple simd groups
-  if(reduction_size > simd_size) {
+  if (reduction_size > simd_size) {
     total_val = lid.x < simd_per_group ? local_vals[lid.x] : op.init;
     total_val = op.simd_reduce(total_val);
   }
@@ -261,10 +261,10 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
   }
 }
 
-template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
+template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
 [[kernel]] void row_reduce_general_no_atomics(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device U *out [[buffer(1)]],
+    device U* out [[buffer(1)]],
     const constant size_t& reduction_size [[buffer(2)]],
     const constant size_t& out_size [[buffer(3)]],
     const constant size_t& non_row_reductions [[buffer(4)]],
@@ -278,16 +278,24 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_per_group [[simdgroups_per_threadgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
-
   (void)non_row_reductions;
 
   Op op;
 
   threadgroup U local_vals[simd_size];
-  U total_val = per_thread_row_reduce<T, U, Op, N_READS>(in, reduction_size, out_size, shape, strides, ndim, lsize.x, lid.x, tid.xy);
+  U total_val = per_thread_row_reduce<T, U, Op, N_READS>(
+      in,
+      reduction_size,
+      out_size,
+      shape,
+      strides,
+      ndim,
+      lsize.x,
+      lid.x,
+      tid.xy);
 
   // Reduction within simd group - simd_add isn't supported for int64 types
-  for (uint16_t i = simd_size/2; i > 0; i /= 2) {
+  for (uint16_t i = simd_size / 2; i > 0; i /= 2) {
     total_val = op(total_val, simd_shuffle_down(total_val, i));
   }
 
@@ -299,9 +307,9 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
 
   // Reduction within thread group
   // Only needed if thread group has multiple simd groups
-  if(ceildiv(reduction_size, N_READS) > simd_size) {
+  if (ceildiv(reduction_size, N_READS) > simd_size) {
     total_val = lid.x < simd_per_group ? local_vals[lid.x] : op.init;
-    for (uint16_t i = simd_size/2; i > 0; i /= 2) {
+    for (uint16_t i = simd_size / 2; i > 0; i /= 2) {
       total_val = op(total_val, simd_shuffle_down(total_val, i));
     }
   }
@@ -312,11 +320,11 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
 }
 
 #define instantiate_row_reduce_general(name, itype, otype, op)     \
-  instantiate_row_reduce_small(name, itype, otype, op) \
+  instantiate_row_reduce_small(name, itype, otype, op) template    \
-  template [[host_name("row_reduce_general_" #name)]] \
+      [[host_name("row_reduce_general_" #name)]] [[kernel]] void   \
-  [[kernel]] void row_reduce_general<itype, otype, op>( \
+      row_reduce_general<itype, otype, op>(                        \
-      const device itype *in [[buffer(0)]],  \
+          const device itype* in [[buffer(0)]],                    \
-      device mlx_atomic<otype> *out [[buffer(1)]],  \
+          device mlx_atomic<otype>* out [[buffer(1)]],             \
           const constant size_t& reduction_size [[buffer(2)]],     \
           const constant size_t& out_size [[buffer(3)]],           \
           const constant size_t& non_row_reductions [[buffer(4)]], \
@@ -331,11 +339,11 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
           uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
 #define instantiate_row_reduce_general_no_atomics(name, itype, otype, op)   \
-  instantiate_row_reduce_small(name, itype, otype, op) \
+  instantiate_row_reduce_small(name, itype, otype, op) template             \
-  template [[host_name("row_reduce_general_no_atomics_" #name)]] \
+      [[host_name("row_reduce_general_no_atomics_" #name)]] [[kernel]] void \
-  [[kernel]] void row_reduce_general_no_atomics<itype, otype, op>( \
+      row_reduce_general_no_atomics<itype, otype, op>(                      \
-      const device itype *in [[buffer(0)]],  \
+          const device itype* in [[buffer(0)]],                             \
-      device otype *out [[buffer(1)]],  \
+          device otype* out [[buffer(1)]],                                  \
           const constant size_t& reduction_size [[buffer(2)]],              \
           const constant size_t& out_size [[buffer(3)]],                    \
           const constant size_t& non_row_reductions [[buffer(4)]],          \
@@ -350,22 +358,21 @@ template <typename T, typename U, typename Op, int N_READS=REDUCE_N_READS>
           uint simd_per_group [[simdgroups_per_threadgroup]],               \
           uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
-
 ///////////////////////////////////////////////////////////////////////////////
 // Instantiations
 ///////////////////////////////////////////////////////////////////////////////
 
 #define instantiate_same_row_reduce_helper(name, tname, type, op) \
-  instantiate_row_reduce_general(name ##tname, type, type, op<type>)
+  instantiate_row_reduce_general(name##tname, type, type, op<type>)
 
 #define instantiate_same_row_reduce_na_helper(name, tname, type, op) \
-  instantiate_row_reduce_general_no_atomics(name ##tname, type, type, op<type>)
+  instantiate_row_reduce_general_no_atomics(name##tname, type, type, op<type>)
 
+// clang-format off
 instantiate_reduce_ops(instantiate_same_row_reduce_helper, instantiate_reduce_helper_types)
 instantiate_reduce_ops(instantiate_same_row_reduce_na_helper, instantiate_reduce_helper_64b)
 
-
 instantiate_reduce_from_types(instantiate_row_reduce_general, and, bool, And)
 instantiate_reduce_from_types(instantiate_row_reduce_general, or, bool, Or)
 
-instantiate_row_reduce_general(sumbool_, bool, uint32_t, Sum<uint32_t>)
+instantiate_row_reduce_general(sumbool_, bool, uint32_t, Sum<uint32_t>) // clang-format on

mlx/backend/metal/kernels/rms_norm.metal

@@ -237,13 +237,17 @@ template <typename T, int N_READS = RMS_N_READS>
   gw += gid * axis_size + lid * N_READS;
   if (lid * N_READS + N_READS <= axis_size) {
     for (int i = 0; i < N_READS; i++) {
-      gx[i] = static_cast<T>(thread_g[i] * thread_w[i] * normalizer - thread_x[i] * meangwx * normalizer3);
+      gx[i] = static_cast<T>(
+          thread_g[i] * thread_w[i] * normalizer -
+          thread_x[i] * meangwx * normalizer3);
       gw[i] = static_cast<T>(thread_g[i] * thread_x[i] * normalizer);
     }
   } else {
     for (int i = 0; i < N_READS; i++) {
       if ((lid * N_READS + i) < axis_size) {
-        gx[i] = static_cast<T>(thread_g[i] * thread_w[i] * normalizer - thread_x[i] * meangwx * normalizer3);
+        gx[i] = static_cast<T>(
+            thread_g[i] * thread_w[i] * normalizer -
+            thread_x[i] * meangwx * normalizer3);
         gw[i] = static_cast<T>(thread_g[i] * thread_x[i] * normalizer);
       }
     }
@@ -342,7 +346,8 @@ template <typename T, int N_READS = RMS_N_READS>
         float wi = w[w_stride * (i + r)];
         float gi = g[i + r];
 
-        gx[i + r] = static_cast<T>(gi * wi * normalizer - xi * meangwx * normalizer3);
+        gx[i + r] =
+            static_cast<T>(gi * wi * normalizer - xi * meangwx * normalizer3);
         gw[i + r] = static_cast<T>(gi * xi * normalizer);
       }
     } else {
@@ -352,7 +357,8 @@ template <typename T, int N_READS = RMS_N_READS>
           float wi = w[w_stride * (i + r)];
           float gi = g[i + r];
 
-          gx[i + r] = static_cast<T>(gi * wi * normalizer - xi * meangwx * normalizer3);
+          gx[i + r] =
+              static_cast<T>(gi * wi * normalizer - xi * meangwx * normalizer3);
           gw[i + r] = static_cast<T>(gi * xi * normalizer);
         }
       }
@@ -431,5 +437,4 @@ template <typename T, int N_READS = RMS_N_READS>
 
 instantiate_rms(float32, float)
 instantiate_rms(float16, half)
-instantiate_rms(bfloat16, bfloat16_t)
+instantiate_rms(bfloat16, bfloat16_t) // clang-format on
-    // clang-format on

mlx/backend/metal/kernels/rope.metal

@@ -7,8 +7,8 @@
 
 template <typename T, bool traditional, bool forward>
 [[kernel]] void rope(
-    const device T *in [[buffer(0)]],
+    const device T* in [[buffer(0)]],
-    device T * out [[buffer(1)]],
+    device T* out [[buffer(1)]],
     constant const size_t strides[3],
     constant const size_t out_strides[3],
     constant const int& offset,
@@ -20,12 +20,15 @@ template <typename T, bool traditional, bool forward>
   uint in_index_1, in_index_2;
   uint out_index_1, out_index_2;
   if (traditional) {
-    out_index_1 = 2 * pos.x * out_strides[2] + pos.y * out_strides[1] + pos.z * out_strides[0];
+    out_index_1 = 2 * pos.x * out_strides[2] + pos.y * out_strides[1] +
+        pos.z * out_strides[0];
     out_index_2 = out_index_1 + 1;
-    in_index_1 = 2 * pos.x * strides[2] + pos.y * strides[1] + pos.z * strides[0];
+    in_index_1 =
+        2 * pos.x * strides[2] + pos.y * strides[1] + pos.z * strides[0];
     in_index_2 = in_index_1 + strides[2];
   } else {
-    out_index_1 = pos.x * out_strides[2] + pos.y * out_strides[1] + pos.z * out_strides[0];
+    out_index_1 = pos.x * out_strides[2] + pos.y * out_strides[1] +
+        pos.z * out_strides[0];
     out_index_2 = out_index_1 + grid.x * out_strides[2];
     in_index_1 = pos.x * strides[2] + pos.y * strides[1] + pos.z * strides[0];
     in_index_2 = in_index_1 + grid.x * strides[2];
@@ -57,8 +60,8 @@ template <typename T, bool traditional, bool forward>
 }
 
 #define instantiate_rope(name, type, traditional, forward) \
-  template [[host_name("rope_" #name)]] \
+  template [[host_name("rope_" #name)]] [[kernel]] void    \
-  [[kernel]] void rope<type, traditional, forward>( \
+  rope<type, traditional, forward>(                        \
       const device type* in [[buffer(0)]],                 \
       device type* out [[buffer(1)]],                      \
       constant const size_t strides[3],                    \
@@ -69,6 +72,7 @@ template <typename T, bool traditional, bool forward>
       uint3 pos [[thread_position_in_grid]],               \
       uint3 grid [[threads_per_grid]]);
 
+// clang-format off
 instantiate_rope(traditional_float16, half, true, true)
 instantiate_rope(traditional_bfloat16, bfloat16_t, true, true)
 instantiate_rope(traditional_float32, float, true, true)
@@ -80,4 +84,4 @@ instantiate_rope(vjp_traditional_bfloat16, bfloat16_t, true, false)
 instantiate_rope(vjp_traditional_float32, float, true, false)
 instantiate_rope(vjp_float16, half, false, false)
 instantiate_rope(vjp_bfloat16, bfloat16_t, false, false)
-instantiate_rope(vjp_float32, float, false, false)
+instantiate_rope(vjp_float32, float, false, false) // clang-format on

mlx/backend/metal/kernels/scaled_dot_product_attention.metal

@@ -1,13 +1,19 @@
-#include <metal_stdlib>
 #include <metal_simdgroup>
+#include <metal_stdlib>
 
 #include "mlx/backend/metal/kernels/scaled_dot_product_attention_params.h"
 using namespace metal;
 
-template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_t NSIMDGROUPS>
+template <
-[[kernel]] void fast_inference_sdpa_compute_partials_template(const device T *Q [[buffer(0)]],
+    typename T,
-                              const device T *K [[buffer(1)]],
+    typename T2,
-                              const device T *V [[buffer(2)]],
+    typename T4,
+    uint16_t TILE_SIZE_CONST,
+    uint16_t NSIMDGROUPS>
+[[kernel]] void fast_inference_sdpa_compute_partials_template(
+    const device T* Q [[buffer(0)]],
+    const device T* K [[buffer(1)]],
+    const device T* V [[buffer(2)]],
     const device uint64_t& L [[buffer(3)]],
     const device MLXScaledDotProductAttentionParams& params [[buffer(4)]],
     device float* O_partials [[buffer(5)]],
@@ -23,29 +29,36 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
   constexpr const uint iter_offset = NSIMDGROUPS * 4;
   const bool is_gqa = params.N_KV_HEADS != params.N_Q_HEADS;
   uint kv_head_offset_factor = tid.x;
-    if(is_gqa) {
+  if (is_gqa) {
     int q_kv_head_ratio = params.N_Q_HEADS / params.N_KV_HEADS;
     kv_head_offset_factor = tid.x / q_kv_head_ratio;
   }
   constexpr const uint16_t P_VEC4 = TILE_SIZE_CONST / NSIMDGROUPS / 4;
-    constexpr const size_t MATRIX_LOADS_PER_SIMDGROUP = TILE_SIZE_CONST / (SIMDGROUP_MATRIX_LOAD_FACTOR * NSIMDGROUPS);
+  constexpr const size_t MATRIX_LOADS_PER_SIMDGROUP =
+      TILE_SIZE_CONST / (SIMDGROUP_MATRIX_LOAD_FACTOR * NSIMDGROUPS);
   constexpr const size_t MATRIX_COLS = DK / SIMDGROUP_MATRIX_LOAD_FACTOR;
-    constexpr const uint totalSmemV = SIMDGROUP_MATRIX_LOAD_FACTOR * SIMDGROUP_MATRIX_LOAD_FACTOR * (MATRIX_LOADS_PER_SIMDGROUP + 1) * NSIMDGROUPS;
+  constexpr const uint totalSmemV = SIMDGROUP_MATRIX_LOAD_FACTOR *
+      SIMDGROUP_MATRIX_LOAD_FACTOR * (MATRIX_LOADS_PER_SIMDGROUP + 1) *
+      NSIMDGROUPS;
 
   threadgroup T4* smemFlush = (threadgroup T4*)threadgroup_block;
-    #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-    for(uint i = 0; i < 8; i++) {
+  for (uint i = 0; i < 8; i++) {
-        smemFlush[simd_lane_id + simd_group_id * THREADS_PER_SIMDGROUP + i * NSIMDGROUPS * THREADS_PER_SIMDGROUP] = T4(0.f);
+    smemFlush
+        [simd_lane_id + simd_group_id * THREADS_PER_SIMDGROUP +
+         i * NSIMDGROUPS * THREADS_PER_SIMDGROUP] = T4(0.f);
   }
   threadgroup_barrier(mem_flags::mem_threadgroup);
   // TODO: multiple query sequence length for speculative decoding
-    const uint tgroup_query_head_offset = tid.x * DK + tid.z * (params.N_Q_HEADS * DK);
+  const uint tgroup_query_head_offset =
+      tid.x * DK + tid.z * (params.N_Q_HEADS * DK);
 
   const uint tgroup_k_head_offset = kv_head_offset_factor * DK * L;
   const uint tgroup_k_tile_offset = tid.y * TILE_SIZE_CONST * DK;
   const uint tgroup_k_batch_offset = tid.z * L * params.N_KV_HEADS * DK;
 
-    const device T* baseK = K + tgroup_k_batch_offset + tgroup_k_tile_offset + tgroup_k_head_offset;
+  const device T* baseK =
+      K + tgroup_k_batch_offset + tgroup_k_tile_offset + tgroup_k_head_offset;
   const device T* baseQ = Q + tgroup_query_head_offset;
 
   device T4* simdgroupQueryData = (device T4*)baseQ;
@@ -53,8 +66,9 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
   constexpr const size_t ACCUM_PER_GROUP = TILE_SIZE_CONST / NSIMDGROUPS;
   float threadAccum[ACCUM_PER_GROUP];
 
-    #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-    for(size_t threadAccumIndex = 0; threadAccumIndex < ACCUM_PER_GROUP; threadAccumIndex++) {
+  for (size_t threadAccumIndex = 0; threadAccumIndex < ACCUM_PER_GROUP;
+       threadAccumIndex++) {
     threadAccum[threadAccumIndex] = -INFINITY;
   }
 
@@ -62,14 +76,16 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
 
   const int32_t SEQUENCE_LENGTH_LESS_TILE_SIZE = L - TILE_SIZE_CONST;
   const bool LAST_TILE = (tid.y + 1) * TILE_SIZE_CONST >= L;
-    const bool LAST_TILE_ALIGNED = (SEQUENCE_LENGTH_LESS_TILE_SIZE == int32_t(tid.y * TILE_SIZE_CONST));
+  const bool LAST_TILE_ALIGNED =
+      (SEQUENCE_LENGTH_LESS_TILE_SIZE == int32_t(tid.y * TILE_SIZE_CONST));
 
   T4 thread_data_x4;
   T4 thread_data_y4;
-    if(!LAST_TILE || LAST_TILE_ALIGNED) {
+  if (!LAST_TILE || LAST_TILE_ALIGNED) {
     thread_data_x4 = *(simdgroupQueryData + simd_lane_id);
-        #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-        for(size_t KROW = simd_group_id; KROW < TILE_SIZE_CONST; KROW += NSIMDGROUPS) {
+    for (size_t KROW = simd_group_id; KROW < TILE_SIZE_CONST;
+         KROW += NSIMDGROUPS) {
       const uint KROW_OFFSET = KROW * DK;
       const device T* baseKRow = baseK + KROW_OFFSET;
       device T4* keysData = (device T4*)baseKRow;
@@ -81,9 +97,11 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
   } else {
     thread_data_x4 = *(simdgroupQueryData + simd_lane_id);
     const uint START_ROW = tid.y * TILE_SIZE_CONST;
-        const device T* baseKThisHead = K + tgroup_k_batch_offset + tgroup_k_head_offset;
+    const device T* baseKThisHead =
+        K + tgroup_k_batch_offset + tgroup_k_head_offset;
 
-        for(size_t KROW = START_ROW + simd_group_id; KROW < L; KROW += NSIMDGROUPS) {
+    for (size_t KROW = START_ROW + simd_group_id; KROW < L;
+         KROW += NSIMDGROUPS) {
       const uint KROW_OFFSET = KROW * DK;
       const device T* baseKRow = baseKThisHead + KROW_OFFSET;
       device T4* keysData = (device T4*)baseKRow;
@@ -95,12 +113,16 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
   }
   threadgroup float* smemP = (threadgroup float*)threadgroup_block;
 
-    #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-    for(size_t i = 0; i < P_VEC4; i++) {
+  for (size_t i = 0; i < P_VEC4; i++) {
-        thread_data_x4 = T4(threadAccum[4 * i], threadAccum[4 * i + 1], threadAccum[4 * i + 2], threadAccum[4 * i + 3]);
+    thread_data_x4 =
+        T4(threadAccum[4 * i],
+           threadAccum[4 * i + 1],
+           threadAccum[4 * i + 2],
+           threadAccum[4 * i + 3]);
     simdgroup_barrier(mem_flags::mem_none);
     thread_data_y4 = simd_sum(thread_data_x4);
-        if(simd_lane_id == 0) {
+    if (simd_lane_id == 0) {
       const uint base_smem_p_offset = i * iter_offset + simd_group_id;
       smemP[base_smem_p_offset + NSIMDGROUPS * 0] = float(thread_data_y4.x);
       smemP[base_smem_p_offset + NSIMDGROUPS * 1] = float(thread_data_y4.y);
@@ -115,11 +137,13 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
   float lse = 0.f;
 
   constexpr const size_t THREADS_PER_THREADGROUP_TIMES_4 = 4 * 32;
-    constexpr const size_t ACCUM_ARRAY_LENGTH = TILE_SIZE_CONST / THREADS_PER_THREADGROUP_TIMES_4 + 1;
+  constexpr const size_t ACCUM_ARRAY_LENGTH =
+      TILE_SIZE_CONST / THREADS_PER_THREADGROUP_TIMES_4 + 1;
   float4 pvals[ACCUM_ARRAY_LENGTH];
 
-    #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-    for(uint accum_array_iter = 0; accum_array_iter < ACCUM_ARRAY_LENGTH; accum_array_iter++) {
+  for (uint accum_array_iter = 0; accum_array_iter < ACCUM_ARRAY_LENGTH;
+       accum_array_iter++) {
     pvals[accum_array_iter] = float4(-INFINITY);
   }
 
@@ -148,8 +172,8 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
   if (TILE_SIZE_LARGER_THAN_64) {
     float maxval = -INFINITY;
     threadgroup float4* smemPtrFlt4 = (threadgroup float4*)threadgroup_block;
-        #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-        for(int i = 0; i < TILE_SIZE_ITERS_128; i++) {
+    for (int i = 0; i < TILE_SIZE_ITERS_128; i++) {
       float4 vals = smemPtrFlt4[simd_lane_id + i * THREADS_PER_SIMDGROUP];
       vals *= params.INV_ALPHA;
       pvals[i] = vals;
@@ -160,16 +184,16 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
     groupMax = simd_max(maxval);
 
     float sumExpLocal = 0.f;
-        #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-        for(int i = 0; i < TILE_SIZE_ITERS_128; i++) {
+    for (int i = 0; i < TILE_SIZE_ITERS_128; i++) {
       pvals[i] = exp(pvals[i] - groupMax);
       sumExpLocal += pvals[i].x + pvals[i].y + pvals[i].z + pvals[i].w;
     }
     simdgroup_barrier(mem_flags::mem_none);
     float tgroupExpSum = simd_sum(sumExpLocal);
     lse = log(tgroupExpSum);
-        #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-        for(int i = 0; i < TILE_SIZE_ITERS_128; i++) {
+    for (int i = 0; i < TILE_SIZE_ITERS_128; i++) {
       pvals[i] = pvals[i] / tgroupExpSum;
       smemPtrFlt4[simd_lane_id + i * THREADS_PER_SIMDGROUP] = float4(0.f);
     }
@@ -187,15 +211,20 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
   threadgroup float* smemOpartial = (threadgroup float*)(smemV + totalSmemV);
 
   if (!LAST_TILE || LAST_TILE_ALIGNED) {
-        #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-        for(size_t col = 0; col < MATRIX_COLS; col++) {
+    for (size_t col = 0; col < MATRIX_COLS; col++) {
       uint matrix_load_loop_iter = 0;
       constexpr const size_t TILE_SIZE_CONST_DIV_8 = TILE_SIZE_CONST / 8;
 
-            for(size_t tile_start = simd_group_id; tile_start < TILE_SIZE_CONST_DIV_8; tile_start += NSIMDGROUPS) {
+      for (size_t tile_start = simd_group_id;
+           tile_start < TILE_SIZE_CONST_DIV_8;
+           tile_start += NSIMDGROUPS) {
         simdgroup_matrix<T, 8, 8> tmp;
-                ulong simdgroup_matrix_offset = matrix_load_loop_iter * NSIMDGROUPS * SIMDGROUP_MATRIX_LOAD_FACTOR + simd_group_id * SIMDGROUP_MATRIX_LOAD_FACTOR;
+        ulong simdgroup_matrix_offset =
-                ulong2 matrixOrigin = ulong2(col * SIMDGROUP_MATRIX_LOAD_FACTOR, simdgroup_matrix_offset);
+            matrix_load_loop_iter * NSIMDGROUPS * SIMDGROUP_MATRIX_LOAD_FACTOR +
+            simd_group_id * SIMDGROUP_MATRIX_LOAD_FACTOR;
+        ulong2 matrixOrigin =
+            ulong2(col * SIMDGROUP_MATRIX_LOAD_FACTOR, simdgroup_matrix_offset);
         simdgroup_load(tmp, baseV, DK, matrixOrigin, true);
         const ulong2 matrixOriginSmem = ulong2(simdgroup_matrix_offset, 0);
         const ulong elemsPerRowSmem = TILE_SIZE_CONST;
@@ -208,10 +237,12 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
       if (TILE_SIZE_CONST == 64) {
         T2 local_p_hat = T2(pvals[0].x, pvals[0].y);
         uint loop_iter = 0;
-                threadgroup float* oPartialSmem = smemOpartial + SIMDGROUP_MATRIX_LOAD_FACTOR * col;
+        threadgroup float* oPartialSmem =
+            smemOpartial + SIMDGROUP_MATRIX_LOAD_FACTOR * col;
 
-                #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-                for(size_t row = simd_group_id; row < SIMDGROUP_MATRIX_LOAD_FACTOR; row += NSIMDGROUPS) {
+        for (size_t row = simd_group_id; row < SIMDGROUP_MATRIX_LOAD_FACTOR;
+             row += NSIMDGROUPS) {
           threadgroup T* smemV_row = smemV + (TILE_SIZE_CONST * row);
           threadgroup T2* smemV2 = (threadgroup T2*)smemV_row;
           T2 v_local = *(smemV2 + simd_lane_id);
@@ -220,20 +251,24 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
           simdgroup_barrier(mem_flags::mem_none);
 
           T row_sum = simd_sum(val);
-                    oPartialSmem[simd_group_id + loop_iter * NSIMDGROUPS] = float(row_sum);
+          oPartialSmem[simd_group_id + loop_iter * NSIMDGROUPS] =
+              float(row_sum);
           loop_iter++;
         }
       }
 
       if (TILE_SIZE_CONST > 64) {
-                constexpr const size_t TILE_SIZE_CONST_DIV_128 = (TILE_SIZE_CONST + 1) / 128;
+        constexpr const size_t TILE_SIZE_CONST_DIV_128 =
-                threadgroup float* oPartialSmem = smemOpartial + SIMDGROUP_MATRIX_LOAD_FACTOR * col;
+            (TILE_SIZE_CONST + 1) / 128;
+        threadgroup float* oPartialSmem =
+            smemOpartial + SIMDGROUP_MATRIX_LOAD_FACTOR * col;
         uint loop_iter = 0;
-                for(size_t row = simd_group_id; row < SIMDGROUP_MATRIX_LOAD_FACTOR; row += NSIMDGROUPS) {
+        for (size_t row = simd_group_id; row < SIMDGROUP_MATRIX_LOAD_FACTOR;
+             row += NSIMDGROUPS) {
           threadgroup T* smemV_row = smemV + (TILE_SIZE_CONST * row);
 
           T row_sum = 0.f;
-                    for(size_t i = 0; i < TILE_SIZE_CONST_DIV_128; i++) {
+          for (size_t i = 0; i < TILE_SIZE_CONST_DIV_128; i++) {
             threadgroup T4* smemV2 = (threadgroup T4*)smemV_row;
             T4 v_local = *(smemV2 + simd_lane_id + i * THREADS_PER_SIMDGROUP);
             T4 p_local = T4(pvals[i]);
@@ -242,7 +277,8 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
           }
           simdgroup_barrier(mem_flags::mem_none);
           row_sum = simd_sum(row_sum);
-                    oPartialSmem[simd_group_id + loop_iter * NSIMDGROUPS] = float(row_sum);
+          oPartialSmem[simd_group_id + loop_iter * NSIMDGROUPS] =
+              float(row_sum);
           loop_iter++;
         }
       }
@@ -252,35 +288,50 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
     const int32_t MAX_START_ROW = L - SIMDGROUP_MATRIX_LOAD_FACTOR + 1;
     const device T* baseVThisHead = V + v_batch_offset + v_head_offset;
     constexpr const int ROWS_PER_ITER = 8;
-        #pragma clang loop unroll(full)
+#pragma clang loop unroll(full)
-        for(size_t col = 0; col < MATRIX_COLS; col++) {
+    for (size_t col = 0; col < MATRIX_COLS; col++) {
       uint smem_col_index = simd_group_id * SIMDGROUP_MATRIX_LOAD_FACTOR;
       int32_t tile_start;
-            for(tile_start = START_ROW + simd_group_id * SIMDGROUP_MATRIX_LOAD_FACTOR; tile_start < MAX_START_ROW; tile_start += NSIMDGROUPS * SIMDGROUP_MATRIX_LOAD_FACTOR) {
+      for (tile_start =
+               START_ROW + simd_group_id * SIMDGROUP_MATRIX_LOAD_FACTOR;
+           tile_start < MAX_START_ROW;
+           tile_start += NSIMDGROUPS * SIMDGROUP_MATRIX_LOAD_FACTOR) {
         simdgroup_matrix<T, 8, 8> tmp;
-                ulong2 matrixOrigin = ulong2(col * SIMDGROUP_MATRIX_LOAD_FACTOR, tile_start);
+        ulong2 matrixOrigin =
-                simdgroup_load(tmp, baseVThisHead, DK, matrixOrigin, /* transpose */ true);
+            ulong2(col * SIMDGROUP_MATRIX_LOAD_FACTOR, tile_start);
+        simdgroup_load(
+            tmp, baseVThisHead, DK, matrixOrigin, /* transpose */ true);
         const ulong2 matrixOriginSmem = ulong2(smem_col_index, 0);
         constexpr const ulong elemsPerRowSmem = TILE_SIZE_CONST;
-                simdgroup_store(tmp, smemV, elemsPerRowSmem, matrixOriginSmem, /* transpose */ false);
+        simdgroup_store(
+            tmp,
+            smemV,
+            elemsPerRowSmem,
+            matrixOriginSmem,
+            /* transpose */ false);
         smem_col_index += NSIMDGROUPS * SIMDGROUP_MATRIX_LOAD_FACTOR;
       };
 
-            tile_start = ((L / SIMDGROUP_MATRIX_LOAD_FACTOR) * SIMDGROUP_MATRIX_LOAD_FACTOR);
+      tile_start =
+          ((L / SIMDGROUP_MATRIX_LOAD_FACTOR) * SIMDGROUP_MATRIX_LOAD_FACTOR);
 
       const int32_t INT_L = int32_t(L);
-            for(int row_index  = tile_start + simd_group_id ; row_index < INT_L; row_index += NSIMDGROUPS) {
+      for (int row_index = tile_start + simd_group_id; row_index < INT_L;
-                if(simd_lane_id < SIMDGROUP_MATRIX_LOAD_FACTOR) {
+           row_index += NSIMDGROUPS) {
+        if (simd_lane_id < SIMDGROUP_MATRIX_LOAD_FACTOR) {
           const uint elems_per_row_gmem = DK;
-                    const uint col_index_v_gmem = col * SIMDGROUP_MATRIX_LOAD_FACTOR + simd_lane_id;
+          const uint col_index_v_gmem =
+              col * SIMDGROUP_MATRIX_LOAD_FACTOR + simd_lane_id;
           const uint row_index_v_gmem = row_index;
 
           const uint elems_per_row_smem = TILE_SIZE_CONST;
           const uint col_index_v_smem = row_index % TILE_SIZE_CONST;
           const uint row_index_v_smem = simd_lane_id;
 
-                    const uint scalar_offset_gmem = row_index_v_gmem * elems_per_row_gmem + col_index_v_gmem;
+          const uint scalar_offset_gmem =
-                    const uint scalar_offset_smem = row_index_v_smem * elems_per_row_smem + col_index_v_smem;
+              row_index_v_gmem * elems_per_row_gmem + col_index_v_gmem;
+          const uint scalar_offset_smem =
+              row_index_v_smem * elems_per_row_smem + col_index_v_smem;
           T vdata = T(*(baseVThisHead + scalar_offset_gmem));
           smemV[scalar_offset_smem] = vdata;
           smem_col_index += NSIMDGROUPS;
@@ -291,9 +342,11 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
 
       if (TILE_SIZE_CONST == 64) {
         T2 local_p_hat = T2(pvals[0].x, pvals[0].y);
-                threadgroup float* oPartialSmem = smemOpartial + SIMDGROUP_MATRIX_LOAD_FACTOR * col;
+        threadgroup float* oPartialSmem =
-                for(size_t smem_row_index = simd_group_id;
+            smemOpartial + SIMDGROUP_MATRIX_LOAD_FACTOR * col;
-                    smem_row_index < ROWS_PER_ITER; smem_row_index += NSIMDGROUPS) {
+        for (size_t smem_row_index = simd_group_id;
+             smem_row_index < ROWS_PER_ITER;
+             smem_row_index += NSIMDGROUPS) {
           threadgroup T* smemV_row = smemV + (TILE_SIZE_CONST * smem_row_index);
           threadgroup T2* smemV2 = (threadgroup T2*)smemV_row;
           T2 v_local = *(smemV2 + simd_lane_id);
@@ -305,22 +358,25 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
       }
 
       if (TILE_SIZE_CONST > 64) {
-                threadgroup float* oPartialSmem = smemOpartial + SIMDGROUP_MATRIX_LOAD_FACTOR * col;
+        threadgroup float* oPartialSmem =
+            smemOpartial + SIMDGROUP_MATRIX_LOAD_FACTOR * col;
         uint loop_count = 0;
-                for(size_t row_index = simd_group_id;
+        for (size_t row_index = simd_group_id; row_index < ROWS_PER_ITER;
-                    row_index < ROWS_PER_ITER; row_index += NSIMDGROUPS) {
+             row_index += NSIMDGROUPS) {
           T row_sum = 0.f;
-                    for(size_t tile_iters = 0; tile_iters < TILE_SIZE_ITERS_128; tile_iters++) {
+          for (size_t tile_iters = 0; tile_iters < TILE_SIZE_ITERS_128;
+               tile_iters++) {
             threadgroup T* smemV_row = smemV + (TILE_SIZE_CONST * row_index);
             threadgroup T4* smemV2 = (threadgroup T4*)smemV_row;
-                        T4 v_local = *(smemV2 + simd_lane_id + tile_iters * THREADS_PER_SIMDGROUP);
+            T4 v_local =
+                *(smemV2 + simd_lane_id + tile_iters * THREADS_PER_SIMDGROUP);
             T4 p_local = T4(pvals[tile_iters]);
             row_sum += dot(p_local, v_local);
-                        
           }
           simdgroup_barrier(mem_flags::mem_none);
           row_sum = simd_sum(row_sum);
-                    oPartialSmem[simd_group_id + NSIMDGROUPS * loop_count] = float(row_sum);
+          oPartialSmem[simd_group_id + NSIMDGROUPS * loop_count] =
+              float(row_sum);
           loop_count++;
         }
       }
@@ -329,76 +385,121 @@ template<typename T, typename T2, typename T4, uint16_t TILE_SIZE_CONST, uint16_
 
   threadgroup_barrier(mem_flags::mem_threadgroup);
 
-    if(simd_group_id == 0) {
+  if (simd_group_id == 0) {
     threadgroup float4* oPartialVec4 = (threadgroup float4*)smemOpartial;
     float4 vals = *(oPartialVec4 + simd_lane_id);
-        device float* oPartialGmem = O_partials + tid.x * DK * params.KV_TILES + tid.y * DK;
+    device float* oPartialGmem =
+        O_partials + tid.x * DK * params.KV_TILES + tid.y * DK;
     device float4* oPartialGmemVec4 = (device float4*)oPartialGmem;
     oPartialGmemVec4[simd_lane_id] = vals;
   }
 
-    if(simd_group_id == 0 && simd_lane_id == 0) {
+  if (simd_group_id == 0 && simd_lane_id == 0) {
     const uint tileIndex = tid.y;
-        const uint gmem_partial_scalar_offset = tid.z * params.N_Q_HEADS * params.KV_TILES + tid.x * params.KV_TILES + tileIndex;
+    const uint gmem_partial_scalar_offset =
+        tid.z * params.N_Q_HEADS * params.KV_TILES + tid.x * params.KV_TILES +
+        tileIndex;
     p_lse[gmem_partial_scalar_offset] = lse;
     p_maxes[gmem_partial_scalar_offset] = groupMax;
   }
 }
 
-#define instantiate_fast_inference_sdpa_to_partials_kernel(itype, itype2, itype4, tile_size, nsimdgroups) \
+#define instantiate_fast_inference_sdpa_to_partials_kernel(                  \
-template [[host_name("fast_inference_sdpa_compute_partials_" #itype "_" #tile_size "_" #nsimdgroups )]] \
+    itype, itype2, itype4, tile_size, nsimdgroups)                           \
-[[kernel]] void fast_inference_sdpa_compute_partials_template<itype, itype2, itype4, tile_size, nsimdgroups>( \
+  template [[host_name("fast_inference_sdpa_compute_partials_" #itype        \
-    const device itype *Q [[buffer(0)]], \
+                       "_" #tile_size "_" #nsimdgroups)]] [[kernel]] void    \
-    const device itype *K [[buffer(1)]], \
+  fast_inference_sdpa_compute_partials_template<                             \
-    const device itype *V [[buffer(2)]], \
+      itype,                                                                 \
+      itype2,                                                                \
+      itype4,                                                                \
+      tile_size,                                                             \
+      nsimdgroups>(                                                          \
+      const device itype* Q [[buffer(0)]],                                   \
+      const device itype* K [[buffer(1)]],                                   \
+      const device itype* V [[buffer(2)]],                                   \
       const device uint64_t& L [[buffer(3)]],                                \
       const device MLXScaledDotProductAttentionParams& params [[buffer(4)]], \
       device float* O_partials [[buffer(5)]],                                \
       device float* p_lse [[buffer(6)]],                                     \
       device float* p_maxes [[buffer(7)]],                                   \
-    threadgroup itype *threadgroup_block [[threadgroup(0)]], \
+      threadgroup itype* threadgroup_block [[threadgroup(0)]],               \
       uint simd_lane_id [[thread_index_in_simdgroup]],                       \
       uint simd_group_id [[simdgroup_index_in_threadgroup]],                 \
       uint3 tid [[threadgroup_position_in_grid]]);
 
+// clang-format off
+#define instantiate_fast_inference_sdpa_to_partials_shapes_helper( \
+    itype, itype2, itype4, tile_size)                              \
+  instantiate_fast_inference_sdpa_to_partials_kernel(              \
+      itype, itype2, itype4, tile_size, 4)                         \
+  instantiate_fast_inference_sdpa_to_partials_kernel(              \
+      itype, itype2, itype4, tile_size, 8) // clang-format on
 
-#define instantiate_fast_inference_sdpa_to_partials_shapes_helper(itype, itype2, itype4, tile_size) \
+instantiate_fast_inference_sdpa_to_partials_shapes_helper(
-    instantiate_fast_inference_sdpa_to_partials_kernel(itype, itype2, itype4, tile_size, 4) \
+    float,
-    instantiate_fast_inference_sdpa_to_partials_kernel(itype, itype2, itype4, tile_size, 8) \
+    float2,
-
+    float4,
-instantiate_fast_inference_sdpa_to_partials_shapes_helper(float, float2, float4, 64);
+    64);
-instantiate_fast_inference_sdpa_to_partials_shapes_helper(float, float2, float4, 128);
+instantiate_fast_inference_sdpa_to_partials_shapes_helper(
-instantiate_fast_inference_sdpa_to_partials_shapes_helper(float, float2, float4, 256);
+    float,
-instantiate_fast_inference_sdpa_to_partials_shapes_helper(float, float2, float4, 512);
+    float2,
-
+    float4,
-instantiate_fast_inference_sdpa_to_partials_shapes_helper(half, half2, half4, 64);
+    128);
-instantiate_fast_inference_sdpa_to_partials_shapes_helper(half, half2, half4, 128);
+instantiate_fast_inference_sdpa_to_partials_shapes_helper(
-instantiate_fast_inference_sdpa_to_partials_shapes_helper(half, half2, half4, 256);
+    float,
-instantiate_fast_inference_sdpa_to_partials_shapes_helper(half, half2, half4, 512);
+    float2,
+    float4,
+    256);
+instantiate_fast_inference_sdpa_to_partials_shapes_helper(
+    float,
+    float2,
+    float4,
+    512);
 
+instantiate_fast_inference_sdpa_to_partials_shapes_helper(
+    half,
+    half2,
+    half4,
+    64);
+instantiate_fast_inference_sdpa_to_partials_shapes_helper(
+    half,
+    half2,
+    half4,
+    128);
+instantiate_fast_inference_sdpa_to_partials_shapes_helper(
+    half,
+    half2,
+    half4,
+    256);
+instantiate_fast_inference_sdpa_to_partials_shapes_helper(
+    half,
+    half2,
+    half4,
+    512);
 
 template <typename T>
 void fast_inference_sdpa_reduce_tiles_template(
-    const device float *O_partials [[buffer(0)]],
+    const device float* O_partials [[buffer(0)]],
-    const device float *p_lse[[buffer(1)]],
+    const device float* p_lse [[buffer(1)]],
-    const device float *p_maxes [[buffer(2)]],
+    const device float* p_maxes [[buffer(2)]],
     const device MLXScaledDotProductAttentionParams& params [[buffer(3)]],
     device T* O [[buffer(4)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]]) {
-
   constexpr const int DK = 128;
-    const ulong offset_rows = tid.z * params.KV_TILES * params.N_Q_HEADS + tid.x * params.KV_TILES;
+  const ulong offset_rows =
+      tid.z * params.KV_TILES * params.N_Q_HEADS + tid.x * params.KV_TILES;
   const device float* p_lse_row = p_lse + offset_rows;
   const device float* p_rowmax_row = p_maxes + offset_rows;
-    // reserve some number of registers.  this constitutes an assumption on max value of KV TILES.
+  // reserve some number of registers.  this constitutes an assumption on max
+  // value of KV TILES.
   constexpr const uint8_t reserve = 128;
   float p_lse_regs[reserve];
   float p_rowmax_regs[reserve];
   float weights[reserve];
 
   float true_max = -INFINITY;
-    for(size_t i = 0; i < params.KV_TILES; i++) {
+  for (size_t i = 0; i < params.KV_TILES; i++) {
     p_lse_regs[i] = float(*(p_lse_row + i));
     p_rowmax_regs[i] = float(*(p_rowmax_row + i));
     true_max = fmax(p_rowmax_regs[i], true_max);
@@ -406,15 +507,17 @@ void fast_inference_sdpa_reduce_tiles_template(
   }
 
   float denom = 0.f;
-    for(size_t i = 0; i < params.KV_TILES; i++) {
+  for (size_t i = 0; i < params.KV_TILES; i++) {
-        weights[i] *= exp(p_rowmax_regs[i]-true_max);
+    weights[i] *= exp(p_rowmax_regs[i] - true_max);
     denom += weights[i];
   }
 
-    const device float* O_partials_with_offset = O_partials + tid.z * params.N_Q_HEADS * DK * params.KV_TILES + tid.x * DK * params.KV_TILES;
+  const device float* O_partials_with_offset = O_partials +
+      tid.z * params.N_Q_HEADS * DK * params.KV_TILES +
+      tid.x * DK * params.KV_TILES;
 
   float o_value = 0.f;
-    for(size_t i = 0; i < params.KV_TILES; i++) {
+  for (size_t i = 0; i < params.KV_TILES; i++) {
     float val = *(O_partials_with_offset + i * DK + lid.x);
     o_value += val * weights[i] / denom;
   }
@@ -423,29 +526,26 @@ void fast_inference_sdpa_reduce_tiles_template(
   return;
 }
 
-
 kernel void fast_inference_sdpa_reduce_tiles_float(
-    const device float *O_partials [[buffer(0)]],
+    const device float* O_partials [[buffer(0)]],
-    const device float *p_lse[[buffer(1)]],
+    const device float* p_lse [[buffer(1)]],
-    const device float *p_maxes [[buffer(2)]],
+    const device float* p_maxes [[buffer(2)]],
     const device MLXScaledDotProductAttentionParams& params [[buffer(3)]],
     device float* O [[buffer(4)]],
     uint3 tid [[threadgroup_position_in_grid]],
-    uint3 lid [[thread_position_in_threadgroup]])
+    uint3 lid [[thread_position_in_threadgroup]]) {
-{
+  fast_inference_sdpa_reduce_tiles_template<float>(
-    fast_inference_sdpa_reduce_tiles_template<float>(O_partials, p_lse, p_maxes, params,
+      O_partials, p_lse, p_maxes, params, O, tid, lid);
-                                     O, tid, lid);
 }
 
 kernel void fast_inference_sdpa_reduce_tiles_half(
-    const device float *O_partials [[buffer(0)]],
+    const device float* O_partials [[buffer(0)]],
-    const device float *p_lse[[buffer(1)]],
+    const device float* p_lse [[buffer(1)]],
-    const device float *p_maxes [[buffer(2)]],
+    const device float* p_maxes [[buffer(2)]],
     const device MLXScaledDotProductAttentionParams& params [[buffer(3)]],
     device half* O [[buffer(4)]],
     uint3 tid [[threadgroup_position_in_grid]],
-    uint3 lid [[thread_position_in_threadgroup]])
+    uint3 lid [[thread_position_in_threadgroup]]) {
-{
+  fast_inference_sdpa_reduce_tiles_template<half>(
-    fast_inference_sdpa_reduce_tiles_template<half>(O_partials, p_lse, p_maxes, params,
+      O_partials, p_lse, p_maxes, params, O, tid, lid);
-                                     O, tid, lid);
 }

mlx/backend/metal/kernels/scan.metal

@@ -54,7 +54,7 @@ struct CumProd<bool> {
   }
 
   bool simd_scan(bool x) {
-    for (int i=1; i<=16; i*=2) {
+    for (int i = 1; i <= 16; i *= 2) {
       bool other = simd_shuffle_up(x, i);
       x &= other;
     }
@@ -77,7 +77,7 @@ struct CumMax {
   }
 
   U simd_scan(U x) {
-    for (int i=1; i<=16; i*=2) {
+    for (int i = 1; i <= 16; i *= 2) {
       U other = simd_shuffle_up(x, i);
       x = (x >= other) ? x : other;
     }
@@ -100,7 +100,7 @@ struct CumMin {
   }
 
   U simd_scan(U x) {
-    for (int i=1; i<=16; i*=2) {
+    for (int i = 1; i <= 16; i *= 2) {
       U other = simd_shuffle_up(x, i);
       x = (x <= other) ? x : other;
     }
@@ -114,54 +114,60 @@ struct CumMin {
 };
 
 template <typename T, typename U, int N_READS, bool reverse>
-inline void load_unsafe(U values[N_READS], const device T * input) {
+inline void load_unsafe(U values[N_READS], const device T* input) {
   if (reverse) {
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
-      values[N_READS-i-1] = input[i];
+      values[N_READS - i - 1] = input[i];
     }
   } else {
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
       values[i] = input[i];
     }
   }
 }
 
 template <typename T, typename U, int N_READS, bool reverse>
-inline void load_safe(U values[N_READS], const device T * input, int start, int total, U init) {
+inline void load_safe(
+    U values[N_READS],
+    const device T* input,
+    int start,
+    int total,
+    U init) {
   if (reverse) {
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
-      values[N_READS-i-1] = (start + N_READS - i - 1 < total) ? input[i] : init;
+      values[N_READS - i - 1] =
+          (start + N_READS - i - 1 < total) ? input[i] : init;
     }
   } else {
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
       values[i] = (start + i < total) ? input[i] : init;
     }
   }
 }
 
 template <typename U, int N_READS, bool reverse>
-inline void write_unsafe(U values[N_READS], device U * out) {
+inline void write_unsafe(U values[N_READS], device U* out) {
   if (reverse) {
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
-      out[i] = values[N_READS-i-1];
+      out[i] = values[N_READS - i - 1];
     }
   } else {
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
       out[i] = values[i];
     }
   }
 }
 
 template <typename U, int N_READS, bool reverse>
-inline void write_safe(U values[N_READS], device U * out, int start, int total) {
+inline void write_safe(U values[N_READS], device U* out, int start, int total) {
   if (reverse) {
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
       if (start + N_READS - i - 1 < total) {
-        out[i] = values[N_READS-i-1];
+        out[i] = values[N_READS - i - 1];
       }
     }
   } else {
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
       if (start + i < total) {
         out[i] = values[i];
       }
@@ -169,12 +175,17 @@ inline void write_safe(U values[N_READS], device U * out, int start, int total)
   }
 }
 
-
+template <
-template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool reverse>
+    typename T,
+    typename U,
+    typename Op,
+    int N_READS,
+    bool inclusive,
+    bool reverse>
 [[kernel]] void contiguous_scan(
     const device T* in [[buffer(0)]],
     device U* out [[buffer(1)]],
-    const constant size_t & axis_size [[buffer(2)]],
+    const constant size_t& axis_size [[buffer(2)]],
     uint gid [[thread_position_in_grid]],
     uint lid [[thread_position_in_threadgroup]],
     uint lsize [[threads_per_threadgroup]],
@@ -195,42 +206,51 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
   U values[N_READS];
   threadgroup U simdgroup_sums[32];
 
-  // Loop over the reduced axis in blocks of size ceildiv(axis_size, N_READS*lsize)
+  // Loop over the reduced axis in blocks of size ceildiv(axis_size,
+  // N_READS*lsize)
   //    Read block
   //    Compute inclusive scan of the block
   //      Compute inclusive scan per thread
   //      Compute exclusive scan of thread sums in simdgroup
   //      Write simdgroup sums in SM
   //      Compute exclusive scan of simdgroup sums
-  //      Compute the output by scanning prefix, prev_simdgroup, prev_thread, value
+  //      Compute the output by scanning prefix, prev_simdgroup, prev_thread,
+  //      value
   //    Write block
 
-  for (uint r = 0; r < ceildiv(axis_size, N_READS*lsize); r++) {
+  for (uint r = 0; r < ceildiv(axis_size, N_READS * lsize); r++) {
     // Compute the block offset
-    uint offset = r*lsize*N_READS + lid*N_READS;
+    uint offset = r * lsize * N_READS + lid * N_READS;
 
     // Read the values
     if (reverse) {
       if ((offset + N_READS) < axis_size) {
-        load_unsafe<T, U, N_READS, reverse>(values, in + axis_size - offset - N_READS);
+        load_unsafe<T, U, N_READS, reverse>(
+            values, in + axis_size - offset - N_READS);
       } else {
-        load_safe<T, U, N_READS, reverse>(values, in + axis_size - offset - N_READS, offset, axis_size, Op::init);
+        load_safe<T, U, N_READS, reverse>(
+            values,
+            in + axis_size - offset - N_READS,
+            offset,
+            axis_size,
+            Op::init);
       }
     } else {
       if ((offset + N_READS) < axis_size) {
         load_unsafe<T, U, N_READS, reverse>(values, in + offset);
       } else {
-        load_safe<T, U, N_READS, reverse>(values, in + offset, offset, axis_size, Op::init);
+        load_safe<T, U, N_READS, reverse>(
+            values, in + offset, offset, axis_size, Op::init);
       }
     }
 
     // Compute an inclusive scan per thread
-    for (int i=1; i<N_READS; i++) {
+    for (int i = 1; i < N_READS; i++) {
-      values[i] = op(values[i], values[i-1]);
+      values[i] = op(values[i], values[i - 1]);
     }
 
     // Compute exclusive scan of thread sums
-    U prev_thread = op.simd_exclusive_scan(values[N_READS-1]);
+    U prev_thread = op.simd_exclusive_scan(values[N_READS - 1]);
 
     // Write simdgroup_sums to SM
     if (simd_lane_id == simd_size - 1) {
@@ -246,7 +266,7 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
     // Compute the output
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
       values[i] = op(values[i], prefix);
       values[i] = op(values[i], simdgroup_sums[simd_group_id]);
       values[i] = op(values[i], prev_thread);
@@ -256,18 +276,25 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
     if (reverse) {
       if (inclusive) {
         if ((offset + N_READS) < axis_size) {
-          write_unsafe<U, N_READS, reverse>(values, out + axis_size - offset - N_READS);
+          write_unsafe<U, N_READS, reverse>(
+              values, out + axis_size - offset - N_READS);
         } else {
-          write_safe<U, N_READS, reverse>(values, out + axis_size - offset - N_READS, offset, axis_size);
+          write_safe<U, N_READS, reverse>(
+              values, out + axis_size - offset - N_READS, offset, axis_size);
         }
       } else {
         if (lid == 0 && offset == 0) {
-          out[axis_size-1] = Op::init;
+          out[axis_size - 1] = Op::init;
         }
         if ((offset + N_READS + 1) < axis_size) {
-          write_unsafe<U, N_READS, reverse>(values, out + axis_size - offset - 1 - N_READS);
+          write_unsafe<U, N_READS, reverse>(
+              values, out + axis_size - offset - 1 - N_READS);
         } else {
-          write_safe<U, N_READS, reverse>(values, out + axis_size - offset - 1 - N_READS, offset + 1, axis_size);
+          write_safe<U, N_READS, reverse>(
+              values,
+              out + axis_size - offset - 1 - N_READS,
+              offset + 1,
+              axis_size);
         }
       }
     } else {
@@ -275,7 +302,8 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
         if ((offset + N_READS) < axis_size) {
           write_unsafe<U, N_READS, reverse>(values, out + offset);
         } else {
-          write_safe<U, N_READS, reverse>(values, out + offset, offset, axis_size);
+          write_safe<U, N_READS, reverse>(
+              values, out + offset, offset, axis_size);
         }
       } else {
         if (lid == 0 && offset == 0) {
@@ -284,26 +312,33 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
         if ((offset + N_READS + 1) < axis_size) {
           write_unsafe<U, N_READS, reverse>(values, out + offset + 1);
         } else {
-          write_safe<U, N_READS, reverse>(values, out + offset + 1, offset + 1, axis_size);
+          write_safe<U, N_READS, reverse>(
+              values, out + offset + 1, offset + 1, axis_size);
         }
       }
     }
 
     // Share the prefix
     if (simd_group_id == simd_groups - 1 && simd_lane_id == simd_size - 1) {
-      simdgroup_sums[0] = values[N_READS-1];
+      simdgroup_sums[0] = values[N_READS - 1];
     }
     threadgroup_barrier(mem_flags::mem_threadgroup);
     prefix = simdgroup_sums[0];
   }
 }
 
-template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool reverse>
+template <
+    typename T,
+    typename U,
+    typename Op,
+    int N_READS,
+    bool inclusive,
+    bool reverse>
 [[kernel]] void strided_scan(
     const device T* in [[buffer(0)]],
     device U* out [[buffer(1)]],
-    const constant size_t & axis_size [[buffer(2)]],
+    const constant size_t& axis_size [[buffer(2)]],
-    const constant size_t & stride [[buffer(3)]],
+    const constant size_t& stride [[buffer(3)]],
     uint2 gid [[threadgroup_position_in_grid]],
     uint2 lid [[thread_position_in_threadgroup]],
     uint2 lsize [[threads_per_threadgroup]],
@@ -311,10 +346,10 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
   Op op;
 
   // Allocate memory
-  threadgroup U read_buffer[N_READS*32*32 + N_READS*32];
+  threadgroup U read_buffer[N_READS * 32 * 32 + N_READS * 32];
   U values[N_READS];
   U prefix[N_READS];
-  for (int i=0; i<N_READS; i++) {
+  for (int i = 0; i < N_READS; i++) {
     prefix[i] = Op::init;
   }
 
@@ -322,7 +357,7 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
   int offset = gid.y * axis_size * stride;
   int global_index_x = gid.x * lsize.y * N_READS;
 
-  for (uint j=0; j<axis_size; j+=simd_size) {
+  for (uint j = 0; j < axis_size; j += simd_size) {
     // Calculate the indices for the current thread
     uint index_y = j + lid.y;
     uint check_index_y = index_y;
@@ -333,37 +368,43 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
 
     // Read in SM
     if (check_index_y < axis_size && (index_x + N_READS) < stride) {
-      for (int i=0; i<N_READS; i++) {
+      for (int i = 0; i < N_READS; i++) {
-        read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i] = in[offset + index_y * stride + index_x + i];
+        read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i] =
+            in[offset + index_y * stride + index_x + i];
       }
     } else {
-      for (int i=0; i<N_READS; i++) {
+      for (int i = 0; i < N_READS; i++) {
         if (check_index_y < axis_size && (index_x + i) < stride) {
-          read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i] = in[offset + index_y * stride + index_x + i];
+          read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i] =
+              in[offset + index_y * stride + index_x + i];
         } else {
-          read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i] = Op::init;
+          read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i] =
+              Op::init;
         }
       }
     }
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
     // Read strided into registers
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
-      values[i] = read_buffer[lid.x * simd_size * N_READS + lid.y * N_READS + i];
+      values[i] =
+          read_buffer[lid.x * simd_size * N_READS + lid.y * N_READS + i];
     }
-    // Do we need the following barrier? Shouldn't all simd threads execute simultaneously?
+    // Do we need the following barrier? Shouldn't all simd threads execute
+    // simultaneously?
     simdgroup_barrier(mem_flags::mem_threadgroup);
 
     // Perform the scan
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
       values[i] = op.simd_scan(values[i]);
       values[i] = op(values[i], prefix[i]);
-      prefix[i] = simd_shuffle(values[i], simd_size-1);
+      prefix[i] = simd_shuffle(values[i], simd_size - 1);
     }
 
     // Write to SM
-    for (int i=0; i<N_READS; i++) {
+    for (int i = 0; i < N_READS; i++) {
-      read_buffer[lid.x * simd_size * N_READS + lid.y * N_READS + i] = values[i];
+      read_buffer[lid.x * simd_size * N_READS + lid.y * N_READS + i] =
+          values[i];
     }
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
@@ -371,11 +412,11 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
     if (!inclusive) {
       if (check_index_y == 0) {
         if ((index_x + N_READS) < stride) {
-          for (int i=0; i<N_READS; i++) {
+          for (int i = 0; i < N_READS; i++) {
             out[offset + index_y * stride + index_x + i] = Op::init;
           }
         } else {
-          for (int i=0; i<N_READS; i++) {
+          for (int i = 0; i < N_READS; i++) {
             if ((index_x + i) < stride) {
               out[offset + index_y * stride + index_x + i] = Op::init;
             }
@@ -391,25 +432,28 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
       }
     }
     if (check_index_y < axis_size && (index_x + N_READS) < stride) {
-      for (int i=0; i<N_READS; i++) {
+      for (int i = 0; i < N_READS; i++) {
-        out[offset + index_y * stride + index_x + i] = read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i];
+        out[offset + index_y * stride + index_x + i] =
+            read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i];
       }
     } else {
-      for (int i=0; i<N_READS; i++) {
+      for (int i = 0; i < N_READS; i++) {
         if (check_index_y < axis_size && (index_x + i) < stride) {
-          out[offset + index_y * stride + index_x + i] = read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i];
+          out[offset + index_y * stride + index_x + i] =
+              read_buffer[lid.y * simd_size * N_READS + lid.x * N_READS + i];
         }
       }
     }
   }
 }
 
-#define instantiate_contiguous_scan(name, itype, otype, op, inclusive, reverse, nreads) \
+#define instantiate_contiguous_scan(                                    \
-  template [[host_name("contiguous_scan_" #name)]] \
+    name, itype, otype, op, inclusive, reverse, nreads)                 \
-  [[kernel]] void contiguous_scan<itype, otype, op<otype>, nreads, inclusive, reverse>( \
+  template [[host_name("contiguous_scan_" #name)]] [[kernel]] void      \
+  contiguous_scan<itype, otype, op<otype>, nreads, inclusive, reverse>( \
       const device itype* in [[buffer(0)]],                             \
       device otype* out [[buffer(1)]],                                  \
-    const constant size_t & axis_size [[buffer(2)]], \
+      const constant size_t& axis_size [[buffer(2)]],                   \
       uint gid [[thread_position_in_grid]],                             \
       uint lid [[thread_position_in_threadgroup]],                      \
       uint lsize [[threads_per_threadgroup]],                           \
@@ -417,19 +461,20 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
       uint simd_lane_id [[thread_index_in_simdgroup]],                  \
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
-#define instantiate_strided_scan(name, itype, otype, op, inclusive, reverse, nreads) \
+#define instantiate_strided_scan(                                    \
-  template [[host_name("strided_scan_" #name)]] \
+    name, itype, otype, op, inclusive, reverse, nreads)              \
-  [[kernel]] void strided_scan<itype, otype, op<otype>, nreads, inclusive, reverse>( \
+  template [[host_name("strided_scan_" #name)]] [[kernel]] void      \
+  strided_scan<itype, otype, op<otype>, nreads, inclusive, reverse>( \
       const device itype* in [[buffer(0)]],                          \
       device otype* out [[buffer(1)]],                               \
-    const constant size_t & axis_size [[buffer(2)]], \
+      const constant size_t& axis_size [[buffer(2)]],                \
-    const constant size_t & stride [[buffer(3)]], \
+      const constant size_t& stride [[buffer(3)]],                   \
       uint2 gid [[thread_position_in_grid]],                         \
       uint2 lid [[thread_position_in_threadgroup]],                  \
       uint2 lsize [[threads_per_threadgroup]],                       \
       uint simd_size [[threads_per_simdgroup]]);
 
-
+// clang-format off
 #define instantiate_scan_helper(name, itype, otype, op, nreads)                                \
   instantiate_contiguous_scan(inclusive_##name, itype, otype, op, true, false, nreads)         \
   instantiate_contiguous_scan(exclusive_##name, itype, otype, op, false, false, nreads)        \
@@ -438,8 +483,9 @@ template <typename T, typename U, typename Op, int N_READS, bool inclusive, bool
   instantiate_strided_scan(inclusive_##name, itype, otype, op, true, false, nreads)            \
   instantiate_strided_scan(exclusive_##name, itype, otype, op, false, false, nreads)           \
   instantiate_strided_scan(reverse_inclusive_##name, itype, otype, op, true, true, nreads)     \
-  instantiate_strided_scan(reverse_exclusive_##name, itype, otype, op, false, true, nreads)
+  instantiate_strided_scan(reverse_exclusive_##name, itype, otype, op, false, true, nreads) // clang-format on
 
+// clang-format off
 instantiate_scan_helper(sum_bool__int32,         bool,        int32_t,     CumSum, 4)
 instantiate_scan_helper(sum_uint8_uint8,         uint8_t,     uint8_t,     CumSum, 4)
 instantiate_scan_helper(sum_uint16_uint16,       uint16_t,    uint16_t,    CumSum, 4)
@@ -491,4 +537,4 @@ instantiate_scan_helper(min_int32_int32,         int32_t,     int32_t,     CumMi
 instantiate_scan_helper(min_float16_float16,     half,        half,        CumMin, 4)
 instantiate_scan_helper(min_float32_float32,     float,       float,       CumMin, 4)
 instantiate_scan_helper(min_bfloat16_bfloat16,   bfloat16_t,  bfloat16_t,  CumMin, 4)
-//instantiate_scan_helper(min_complex64_complex64, complex64_t, complex64_t, CumMin)
+//instantiate_scan_helper(min_complex64_complex64, complex64_t, complex64_t, CumMin) // clang-format on

mlx/backend/metal/kernels/scatter.metal

@@ -13,22 +13,20 @@ using namespace metal;
 // Scatter kernel
 /////////////////////////////////////////////////////////////////////
 
-template <typename T, typename IdxT, typename Op, int NIDX> \
+template <typename T, typename IdxT, typename Op, int NIDX>
 METAL_FUNC void scatter_1d_index_impl(
-  const device T *updates [[buffer(1)]],
+    const device T* updates [[buffer(1)]],
-  device mlx_atomic<T> *out [[buffer(2)]],
+    device mlx_atomic<T>* out [[buffer(2)]],
     const constant int* out_shape [[buffer(3)]],
     const constant size_t* out_strides [[buffer(4)]],
     const constant size_t& upd_size [[buffer(5)]],
     const thread array<const device IdxT*, NIDX>& idx_buffers,
     uint2 gid [[thread_position_in_grid]]) {
-
   Op op;
 
   uint out_idx = 0;
   for (int i = 0; i < NIDX; i++) {
-    auto idx_val = offset_neg_idx(
+    auto idx_val = offset_neg_idx(idx_buffers[i][gid.y], out_shape[i]);
-        idx_buffers[i][gid.y], out_shape[i]);
     out_idx += idx_val * out_strides[i];
   }
 
@@ -36,44 +34,34 @@ METAL_FUNC void scatter_1d_index_impl(
 }
 
 #define make_scatter_1d_index(IDX_ARG, IDX_ARR)                            \
-template <typename T, typename IdxT, typename Op, int NIDX> \
+  template <typename T, typename IdxT, typename Op, int NIDX>              \
-[[kernel]] void scatter_1d_index( \
+  [[kernel]] void scatter_1d_index(                                        \
-  const device T *updates [[buffer(1)]], \
+      const device T* updates [[buffer(1)]],                               \
-  device mlx_atomic<T> *out [[buffer(2)]], \
+      device mlx_atomic<T>* out [[buffer(2)]],                             \
       const constant int* out_shape [[buffer(3)]],                         \
       const constant size_t* out_strides [[buffer(4)]],                    \
       const constant size_t& upd_size [[buffer(5)]],                       \
-  IDX_ARG(IdxT) \
+      IDX_ARG(IdxT) uint2 gid [[thread_position_in_grid]]) {               \
-  uint2 gid [[thread_position_in_grid]]) { \
-  \
     const array<const device IdxT*, NIDX> idx_buffers = {IDX_ARR()};       \
                                                                            \
     return scatter_1d_index_impl<T, IdxT, Op, NIDX>(                       \
-    updates, \
+        updates, out, out_shape, out_strides, upd_size, idx_buffers, gid); \
-    out, \
+  }
-    out_shape, \
-    out_strides, \
-    upd_size, \
-    idx_buffers, \
-    gid); \
-  \
-}
 
 template <typename T, typename IdxT, typename Op, int NIDX>
 METAL_FUNC void scatter_impl(
-    const device T *updates [[buffer(1)]],
+    const device T* updates [[buffer(1)]],
-    device mlx_atomic<T> *out [[buffer(2)]],
+    device mlx_atomic<T>* out [[buffer(2)]],
-    const constant int *upd_shape [[buffer(3)]],
+    const constant int* upd_shape [[buffer(3)]],
-    const constant size_t *upd_strides [[buffer(4)]],
+    const constant size_t* upd_strides [[buffer(4)]],
     const constant size_t& upd_ndim [[buffer(5)]],
     const constant size_t& upd_size [[buffer(6)]],
-    const constant int *out_shape [[buffer(7)]],
+    const constant int* out_shape [[buffer(7)]],
-    const constant size_t *out_strides [[buffer(8)]],
+    const constant size_t* out_strides [[buffer(8)]],
     const constant size_t& out_ndim [[buffer(9)]],
     const constant int* axes [[buffer(10)]],
     const thread Indices<IdxT, NIDX>& indices,
     uint2 gid [[thread_position_in_grid]]) {
-
   Op op;
   auto ind_idx = gid.y;
   auto ind_offset = gid.x;
@@ -86,8 +74,7 @@ METAL_FUNC void scatter_impl(
         &indices.strides[indices.ndim * i],
         indices.ndim);
     auto ax = axes[i];
-    auto idx_val = offset_neg_idx(
+    auto idx_val = offset_neg_idx(indices.buffers[i][idx_loc], out_shape[ax]);
-        indices.buffers[i][idx_loc], out_shape[ax]);
     out_idx += idx_val * out_strides[ax];
   }
 
@@ -97,34 +84,30 @@ METAL_FUNC void scatter_impl(
     out_idx += out_offset;
   }
 
-  auto upd_idx = elem_to_loc(gid.y * upd_size + gid.x, upd_shape, upd_strides, upd_ndim);
+  auto upd_idx =
+      elem_to_loc(gid.y * upd_size + gid.x, upd_shape, upd_strides, upd_ndim);
   op.atomic_update(out, updates[upd_idx], out_idx);
 }
 
 #define make_scatter_impl(IDX_ARG, IDX_ARR)                   \
-template <typename T, typename IdxT, typename Op, int NIDX>  \
+  template <typename T, typename IdxT, typename Op, int NIDX> \
-[[kernel]] void scatter( \
+  [[kernel]] void scatter(                                    \
-    const device T *updates [[buffer(1)]], \
+      const device T* updates [[buffer(1)]],                  \
-    device mlx_atomic<T> *out [[buffer(2)]], \
+      device mlx_atomic<T>* out [[buffer(2)]],                \
-    const constant int *upd_shape [[buffer(3)]], \
+      const constant int* upd_shape [[buffer(3)]],            \
-    const constant size_t *upd_strides [[buffer(4)]], \
+      const constant size_t* upd_strides [[buffer(4)]],       \
       const constant size_t& upd_ndim [[buffer(5)]],          \
       const constant size_t& upd_size [[buffer(6)]],          \
-    const constant int *out_shape [[buffer(7)]], \
+      const constant int* out_shape [[buffer(7)]],            \
-    const constant size_t *out_strides [[buffer(8)]], \
+      const constant size_t* out_strides [[buffer(8)]],       \
       const constant size_t& out_ndim [[buffer(9)]],          \
       const constant int* axes [[buffer(10)]],                \
-    const constant int *idx_shapes [[buffer(11)]], \
+      const constant int* idx_shapes [[buffer(11)]],          \
-    const constant size_t *idx_strides [[buffer(12)]], \
+      const constant size_t* idx_strides [[buffer(12)]],      \
       const constant int& idx_ndim [[buffer(13)]],            \
-    IDX_ARG(IdxT) \
+      IDX_ARG(IdxT) uint2 gid [[thread_position_in_grid]]) {  \
-    uint2 gid [[thread_position_in_grid]]) { \
- \
     Indices<IdxT, NIDX> idxs{                                 \
-      {{IDX_ARR()}}, \
+        {{IDX_ARR()}}, idx_shapes, idx_strides, idx_ndim};    \
-      idx_shapes, \
-      idx_strides, \
-      idx_ndim}; \
                                                               \
     return scatter_impl<T, IdxT, Op, NIDX>(                   \
         updates,                                              \
@@ -139,70 +122,63 @@ template <typename T, typename IdxT, typename Op, int NIDX>  \
         axes,                                                 \
         idxs,                                                 \
         gid);                                                 \
-}
+  }
 
 #define make_scatter(n)                       \
-make_scatter_impl(IDX_ARG_ ##n, IDX_ARR_ ##n) \
+  make_scatter_impl(IDX_ARG_##n, IDX_ARR_##n) \
-make_scatter_1d_index(IDX_ARG_ ##n, IDX_ARR_ ##n)
+      make_scatter_1d_index(IDX_ARG_##n, IDX_ARR_##n)
 
-make_scatter(0)
+make_scatter(0) make_scatter(1) make_scatter(2) make_scatter(3) make_scatter(4)
-make_scatter(1)
+    make_scatter(5) make_scatter(6) make_scatter(7) make_scatter(8)
-make_scatter(2)
+        make_scatter(9) make_scatter(10)
-make_scatter(3)
-make_scatter(4)
-make_scatter(5)
-make_scatter(6)
-make_scatter(7)
-make_scatter(8)
-make_scatter(9)
-make_scatter(10)
 
 /////////////////////////////////////////////////////////////////////
 // Scatter instantiations
 /////////////////////////////////////////////////////////////////////
 
 #define instantiate_scatter5(name, src_t, idx_t, op_t, nidx, IDX_ARG) \
-template [[host_name("scatter" name "_" #nidx)]] \
+  template [[host_name("scatter" name "_" #nidx)]] [[kernel]] void    \
-[[kernel]] void scatter<src_t, idx_t, op_t, nidx>( \
+  scatter<src_t, idx_t, op_t, nidx>(                                  \
-    const device src_t *updates [[buffer(1)]], \
+      const device src_t* updates [[buffer(1)]],                      \
-    device mlx_atomic<src_t> *out [[buffer(2)]], \
+      device mlx_atomic<src_t>* out [[buffer(2)]],                    \
-    const constant int *upd_shape [[buffer(3)]], \
+      const constant int* upd_shape [[buffer(3)]],                    \
-    const constant size_t *upd_strides [[buffer(4)]], \
+      const constant size_t* upd_strides [[buffer(4)]],               \
       const constant size_t& upd_ndim [[buffer(5)]],                  \
       const constant size_t& upd_size [[buffer(6)]],                  \
-    const constant int *out_shape [[buffer(7)]], \
+      const constant int* out_shape [[buffer(7)]],                    \
-    const constant size_t *out_strides [[buffer(8)]], \
+      const constant size_t* out_strides [[buffer(8)]],               \
       const constant size_t& out_ndim [[buffer(9)]],                  \
       const constant int* axes [[buffer(10)]],                        \
-    const constant int *idx_shapes [[buffer(11)]], \
+      const constant int* idx_shapes [[buffer(11)]],                  \
-    const constant size_t *idx_strides [[buffer(12)]], \
+      const constant size_t* idx_strides [[buffer(12)]],              \
       const constant int& idx_ndim [[buffer(13)]],                    \
-    IDX_ARG(idx_t) \
+      IDX_ARG(idx_t) uint2 gid [[thread_position_in_grid]]);
-    uint2 gid [[thread_position_in_grid]]);
 
 #define instantiate_scatter6(name, src_t, idx_t, op_t, nidx, IDX_ARG)       \
-template [[host_name("scatter_1d_index" name "_" #nidx)]] \
+  template [[host_name("scatter_1d_index" name "_" #nidx)]] [[kernel]] void \
-[[kernel]] void scatter_1d_index<src_t, idx_t, op_t, nidx>( \
+  scatter_1d_index<src_t, idx_t, op_t, nidx>(                               \
-  const device src_t *updates [[buffer(1)]], \
+      const device src_t* updates [[buffer(1)]],                            \
-  device mlx_atomic<src_t> *out [[buffer(2)]], \
+      device mlx_atomic<src_t>* out [[buffer(2)]],                          \
       const constant int* out_shape [[buffer(3)]],                          \
       const constant size_t* out_strides [[buffer(4)]],                     \
       const constant size_t& upd_size [[buffer(5)]],                        \
-  IDX_ARG(idx_t) \
+      IDX_ARG(idx_t) uint2 gid [[thread_position_in_grid]]);
-  uint2 gid [[thread_position_in_grid]]);
 
+// clang-format off
 #define instantiate_scatter4(name, src_t, idx_t, op_t, nidx)            \
   instantiate_scatter5(name, src_t, idx_t, op_t, nidx, IDX_ARG_ ##nidx) \
-  instantiate_scatter6(name, src_t, idx_t, op_t, nidx, IDX_ARG_ ##nidx)
+  instantiate_scatter6(name, src_t, idx_t, op_t, nidx, IDX_ARG_ ##nidx) // clang-format on
 
+// clang-format off
 // Special case NINDEX=0
 #define instantiate_scatter_nd0(name, type)                      \
   instantiate_scatter4(#name "none", type, bool, None, 0)        \
   instantiate_scatter4(#name "_sum", type, bool, Sum<type>, 0)   \
   instantiate_scatter4(#name "_prod", type, bool, Prod<type>, 0) \
   instantiate_scatter4(#name "_max", type, bool, Max<type>, 0)   \
-  instantiate_scatter4(#name "_min", type, bool, Min<type>, 0)
+  instantiate_scatter4(#name "_min", type, bool, Min<type>, 0) // clang-format on
 
+// clang-format off
 #define instantiate_scatter3(name, type, ind_type, op_type) \
   instantiate_scatter4(name, type, ind_type, op_type, 1)    \
   instantiate_scatter4(name, type, ind_type, op_type, 2)    \
@@ -213,15 +189,17 @@ template [[host_name("scatter_1d_index" name "_" #nidx)]] \
   instantiate_scatter4(name, type, ind_type, op_type, 7)    \
   instantiate_scatter4(name, type, ind_type, op_type, 8)    \
   instantiate_scatter4(name, type, ind_type, op_type, 9)    \
-  instantiate_scatter4(name, type, ind_type, op_type, 10)
+  instantiate_scatter4(name, type, ind_type, op_type, 10) // clang-format on
 
+// clang-format off
 #define instantiate_scatter2(name, type, ind_type)               \
   instantiate_scatter3(name "_none", type, ind_type, None)       \
   instantiate_scatter3(name "_sum", type, ind_type, Sum<type>)   \
   instantiate_scatter3(name "_prod", type, ind_type, Prod<type>) \
   instantiate_scatter3(name "_max", type, ind_type, Max<type>)   \
-  instantiate_scatter3(name "_min", type, ind_type, Min<type>)
+  instantiate_scatter3(name "_min", type, ind_type, Min<type>) // clang-format on
 
+// clang-format off
 #define instantiate_scatter(name, type)                \
   instantiate_scatter2(#name "bool_", type, bool)      \
   instantiate_scatter2(#name "uint8", type, uint8_t)   \
@@ -231,8 +209,9 @@ template [[host_name("scatter_1d_index" name "_" #nidx)]] \
   instantiate_scatter2(#name "int8", type, int8_t)     \
   instantiate_scatter2(#name "int16", type, int16_t)   \
   instantiate_scatter2(#name "int32", type, int32_t)   \
-  instantiate_scatter2(#name "int64", type, int64_t)
+  instantiate_scatter2(#name "int64", type, int64_t) // clang-format on
 
+    // clang-format off
 // TODO uint64 and int64 unsupported
 instantiate_scatter_nd0(bool_, bool)
 instantiate_scatter_nd0(uint8, uint8_t)
@@ -254,4 +233,4 @@ instantiate_scatter(int16, int16_t)
 instantiate_scatter(int32, int32_t)
 instantiate_scatter(float16, half)
 instantiate_scatter(float32, float)
-instantiate_scatter(bfloat16, bfloat16_t)
+instantiate_scatter(bfloat16, bfloat16_t) // clang-format on

mlx/backend/metal/kernels/softmax.metal

@@ -198,7 +198,6 @@ template <typename T, typename AccT = T, int N_READS = SOFTMAX_N_READS>
   }
 }
 
-// clang-format off
 #define instantiate_softmax(name, itype)                          \
   template [[host_name("softmax_" #name)]] [[kernel]] void        \
   softmax_single_row<itype>(                                      \
@@ -241,9 +240,9 @@ template <typename T, typename AccT = T, int N_READS = SOFTMAX_N_READS>
       uint simd_lane_id [[thread_index_in_simdgroup]],                    \
       uint simd_group_id [[simdgroup_index_in_threadgroup]]);
 
+// clang-format off
 instantiate_softmax(float32, float)
 instantiate_softmax(float16, half)
 instantiate_softmax(bfloat16, bfloat16_t)
 instantiate_softmax_precise(float16, half)
-instantiate_softmax_precise(bfloat16, bfloat16_t)
+instantiate_softmax_precise(bfloat16, bfloat16_t) // clang-format on
-// clang-format on

mlx/backend/metal/kernels/sort.metal

@@ -11,7 +11,8 @@
 
 using namespace metal;
 
-// Based on GPU merge sort algorithm at https://github.com/NVIDIA/cccl/tree/main/cub/cub
+// Based on GPU merge sort algorithm at
+// https://github.com/NVIDIA/cccl/tree/main/cub/cub
 
 ///////////////////////////////////////////////////////////////////////////////
 // Thread-level sort
@@ -43,20 +44,18 @@ struct ThreadSort {
   static METAL_FUNC void sort(
       thread val_t (&vals)[N_PER_THREAD],
       thread idx_t (&idxs)[N_PER_THREAD]) {
-    
     CompareOp op;
 
     MLX_MTL_LOOP_UNROLL
-    for(short i = 0; i < N_PER_THREAD; ++i) {
+    for (short i = 0; i < N_PER_THREAD; ++i) {
       MLX_MTL_LOOP_UNROLL
-      for(short j = i & 1; j < N_PER_THREAD - 1; j += 2) {
+      for (short j = i & 1; j < N_PER_THREAD - 1; j += 2) {
-        if(op(vals[j + 1], vals[j])) {
+        if (op(vals[j + 1], vals[j])) {
           thread_swap(vals[j + 1], vals[j]);
           thread_swap(idxs[j + 1], idxs[j]);
         }
       }
     }
-
   }
 };
 
@@ -72,25 +71,25 @@ template <
     short N_PER_THREAD,
     typename CompareOp>
 struct BlockMergeSort {
-  using thread_sort_t = ThreadSort<val_t, idx_t, ARG_SORT, N_PER_THREAD, CompareOp>;
+  using thread_sort_t =
+      ThreadSort<val_t, idx_t, ARG_SORT, N_PER_THREAD, CompareOp>;
   static METAL_FUNC int merge_partition(
       const threadgroup val_t* As,
       const threadgroup val_t* Bs,
       short A_sz,
       short B_sz,
       short sort_md) {
-
     CompareOp op;
 
     short A_st = max(0, sort_md - B_sz);
     short A_ed = min(sort_md, A_sz);
 
-    while(A_st < A_ed) {
+    while (A_st < A_ed) {
       short md = A_st + (A_ed - A_st) / 2;
       auto a = As[md];
       auto b = Bs[sort_md - 1 - md];
 
-      if(op(b, a)) {
+      if (op(b, a)) {
         A_ed = md;
       } else {
         A_st = md + 1;
@@ -98,7 +97,6 @@ struct BlockMergeSort {
     }
 
     return A_ed;
-
   }
 
   static METAL_FUNC void merge_step(
@@ -110,12 +108,11 @@ struct BlockMergeSort {
       short B_sz,
       thread val_t (&vals)[N_PER_THREAD],
       thread idx_t (&idxs)[N_PER_THREAD]) {
-
     CompareOp op;
     short a_idx = 0;
     short b_idx = 0;
 
-    for(int i = 0; i < N_PER_THREAD; ++i) {
+    for (int i = 0; i < N_PER_THREAD; ++i) {
       auto a = As[a_idx];
       auto b = Bs[b_idx];
       bool pred = (b_idx < B_sz) && (a_idx >= A_sz || op(b, a));
@@ -126,7 +123,6 @@ struct BlockMergeSort {
       b_idx += short(pred);
       a_idx += short(!pred);
     }
-
   }
 
   static METAL_FUNC void sort(
@@ -134,32 +130,32 @@ struct BlockMergeSort {
       threadgroup idx_t* tgp_idxs [[threadgroup(1)]],
       int size_sorted_axis,
       uint3 lid [[thread_position_in_threadgroup]]) {
-    
     // Get thread location
     int idx = lid.x * N_PER_THREAD;
 
     // Load from shared memory
     thread val_t thread_vals[N_PER_THREAD];
     thread idx_t thread_idxs[N_PER_THREAD];
-    for(int i = 0; i < N_PER_THREAD; ++i) {
+    for (int i = 0; i < N_PER_THREAD; ++i) {
       thread_vals[i] = tgp_vals[idx + i];
-      if(ARG_SORT) {
+      if (ARG_SORT) {
         thread_idxs[i] = tgp_idxs[idx + i];
       }
     }
 
     // Per thread sort
-    if(idx < size_sorted_axis) {
+    if (idx < size_sorted_axis) {
       thread_sort_t::sort(thread_vals, thread_idxs);
     }
 
     // Do merges using threadgroup memory
-    for (int merge_threads = 2; merge_threads <= BLOCK_THREADS; merge_threads *= 2) {
+    for (int merge_threads = 2; merge_threads <= BLOCK_THREADS;
+         merge_threads *= 2) {
       // Update threadgroup memory
       threadgroup_barrier(mem_flags::mem_threadgroup);
-      for(int i = 0; i < N_PER_THREAD; ++i) {
+      for (int i = 0; i < N_PER_THREAD; ++i) {
         tgp_vals[idx + i] = thread_vals[i];
-        if(ARG_SORT) {
+        if (ARG_SORT) {
           tgp_idxs[idx + i] = thread_idxs[i];
         }
       }
@@ -189,12 +185,7 @@ struct BlockMergeSort {
       //       of size N_PER_THREAD for each merge lane i
       //  C = [Ci] is sorted
       int sort_md = N_PER_THREAD * merge_lane;
-      int partition = merge_partition(
+      int partition = merge_partition(As, Bs, A_sz, B_sz, sort_md);
-          As,
-          Bs,
-          A_sz,
-          B_sz,
-          sort_md);
 
       As += partition;
       Bs += sort_md - partition;
@@ -202,27 +193,20 @@ struct BlockMergeSort {
       A_sz -= partition;
       B_sz -= sort_md - partition;
 
-      const threadgroup idx_t* As_idx = ARG_SORT ? tgp_idxs + A_st + partition : nullptr;
+      const threadgroup idx_t* As_idx =
-      const threadgroup idx_t* Bs_idx = ARG_SORT ? tgp_idxs + B_st + sort_md - partition : nullptr;
+          ARG_SORT ? tgp_idxs + A_st + partition : nullptr;
+      const threadgroup idx_t* Bs_idx =
+          ARG_SORT ? tgp_idxs + B_st + sort_md - partition : nullptr;
 
       // Merge starting at the partition and store results in thread registers
-      merge_step(
+      merge_step(As, Bs, As_idx, Bs_idx, A_sz, B_sz, thread_vals, thread_idxs);
-          As,
-          Bs,
-          As_idx,
-          Bs_idx,
-          A_sz,
-          B_sz,
-          thread_vals,
-          thread_idxs);
-
     }
 
     // Write out to shared memory
     threadgroup_barrier(mem_flags::mem_threadgroup);
-    for(int i = 0; i < N_PER_THREAD; ++i) {
+    for (int i = 0; i < N_PER_THREAD; ++i) {
       tgp_vals[idx + i] = thread_vals[i];
-      if(ARG_SORT) {
+      if (ARG_SORT) {
         tgp_idxs[idx + i] = thread_idxs[i];
       }
     }
@@ -263,15 +247,15 @@ struct KernelMergeSort {
       threadgroup idx_t* tgp_idxs,
       uint3 tid [[threadgroup_position_in_grid]],
       uint3 lid [[thread_position_in_threadgroup]]) {
-
     // tid.y tells us the segment index
     inp += tid.y * stride_segment_axis;
     out += tid.y * stride_segment_axis;
 
     // Copy into threadgroup memory
-    for(short i = lid.x; i < N_PER_BLOCK; i+= BLOCK_THREADS) {
+    for (short i = lid.x; i < N_PER_BLOCK; i += BLOCK_THREADS) {
-      tgp_vals[i] = i < size_sorted_axis ? inp[i * stride_sorted_axis] : val_t(CompareOp::init);
+      tgp_vals[i] = i < size_sorted_axis ? inp[i * stride_sorted_axis]
-      if(ARG_SORT) {
+                                         : val_t(CompareOp::init);
+      if (ARG_SORT) {
         tgp_idxs[i] = i;
       }
     }
@@ -284,8 +268,8 @@ struct KernelMergeSort {
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
     // Write output
-    for(int i = lid.x; i < size_sorted_axis; i+= BLOCK_THREADS) {
+    for (int i = lid.x; i < size_sorted_axis; i += BLOCK_THREADS) {
-      if(ARG_SORT) {
+      if (ARG_SORT) {
         out[i * stride_sorted_axis] = tgp_idxs[i];
       } else {
         out[i * stride_sorted_axis] = tgp_vals[i];
@@ -308,12 +292,12 @@ template <
     const constant int& stride_segment_axis [[buffer(4)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]]) {
-
+  using sort_kernel =
-  using sort_kernel = KernelMergeSort<T, U, ARG_SORT, BLOCK_THREADS, N_PER_THREAD>;
+      KernelMergeSort<T, U, ARG_SORT, BLOCK_THREADS, N_PER_THREAD>;
   using val_t = typename sort_kernel::val_t;
   using idx_t = typename sort_kernel::idx_t;
 
-  if(ARG_SORT) {
+  if (ARG_SORT) {
     threadgroup val_t tgp_vals[sort_kernel::N_PER_BLOCK];
     threadgroup idx_t tgp_idxs[sort_kernel::N_PER_BLOCK];
     sort_kernel::block_sort(
@@ -339,7 +323,6 @@ template <
         tid,
         lid);
   }
-
 }
 
 constant constexpr const int zero_helper = 0;
@@ -360,8 +343,8 @@ template <
     const device size_t* nc_strides [[buffer(6)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]]) {
-
+  using sort_kernel =
-  using sort_kernel = KernelMergeSort<T, U, ARG_SORT, BLOCK_THREADS, N_PER_THREAD>;
+      KernelMergeSort<T, U, ARG_SORT, BLOCK_THREADS, N_PER_THREAD>;
   using val_t = typename sort_kernel::val_t;
   using idx_t = typename sort_kernel::idx_t;
 
@@ -369,7 +352,7 @@ template <
   inp += block_idx;
   out += block_idx;
 
-  if(ARG_SORT) {
+  if (ARG_SORT) {
     threadgroup val_t tgp_vals[sort_kernel::N_PER_BLOCK];
     threadgroup idx_t tgp_idxs[sort_kernel::N_PER_BLOCK];
     sort_kernel::block_sort(
@@ -395,17 +378,17 @@ template <
         tid,
         lid);
   }
-
 }
 
 ///////////////////////////////////////////////////////////////////////////////
 // Instantiations
 ///////////////////////////////////////////////////////////////////////////////
 
-
+#define instantiate_block_sort(                                          \
-#define instantiate_block_sort(name, itname, itype, otname, otype, arg_sort, bn, tn) \
+    name, itname, itype, otname, otype, arg_sort, bn, tn)                \
-  template [[host_name(#name "_" #itname "_" #otname "_bn" #bn "_tn" #tn)]] \
+  template [[host_name(#name "_" #itname "_" #otname "_bn" #bn           \
-  [[kernel]] void block_sort<itype, otype, arg_sort, bn, tn>( \
+                             "_tn" #tn)]] [[kernel]] void                \
+  block_sort<itype, otype, arg_sort, bn, tn>(                            \
       const device itype* inp [[buffer(0)]],                             \
       device otype* out [[buffer(1)]],                                   \
       const constant int& size_sorted_axis [[buffer(2)]],                \
@@ -413,8 +396,9 @@ template <
       const constant int& stride_segment_axis [[buffer(4)]],             \
       uint3 tid [[threadgroup_position_in_grid]],                        \
       uint3 lid [[thread_position_in_threadgroup]]);                     \
-  template [[host_name(#name "_" #itname "_" #otname "_bn" #bn "_tn" #tn "_nc")]] \
+  template [[host_name(#name "_" #itname "_" #otname "_bn" #bn "_tn" #tn \
-  [[kernel]] void block_sort_nc<itype, otype, arg_sort, bn, tn>( \
+                             "_nc")]] [[kernel]] void                    \
+  block_sort_nc<itype, otype, arg_sort, bn, tn>(                         \
       const device itype* inp [[buffer(0)]],                             \
       device otype* out [[buffer(1)]],                                   \
       const constant int& size_sorted_axis [[buffer(2)]],                \
@@ -426,15 +410,19 @@ template <
       uint3 lid [[thread_position_in_threadgroup]]);
 
 #define instantiate_arg_block_sort_base(itname, itype, bn, tn) \
-  instantiate_block_sort(arg_block_merge_sort, itname, itype, uint32, uint32_t, true, bn, tn)
+  instantiate_block_sort(                                      \
+      arg_block_merge_sort, itname, itype, uint32, uint32_t, true, bn, tn)
 
 #define instantiate_block_sort_base(itname, itype, bn, tn) \
-  instantiate_block_sort(block_merge_sort, itname, itype, itname, itype, false, bn, tn)
+  instantiate_block_sort(                                  \
+      block_merge_sort, itname, itype, itname, itype, false, bn, tn)
 
+// clang-format off
 #define instantiate_block_sort_tn(itname, itype, bn) \
   instantiate_block_sort_base(itname, itype, bn, 8)  \
-  instantiate_arg_block_sort_base(itname, itype, bn, 8)
+  instantiate_arg_block_sort_base(itname, itype, bn, 8) // clang-format on
 
+// clang-format off
 #define instantiate_block_sort_bn(itname, itype) \
   instantiate_block_sort_tn(itname, itype, 128)  \
   instantiate_block_sort_tn(itname, itype, 256)  \
@@ -448,27 +436,27 @@ instantiate_block_sort_bn(int16, int16_t)
 instantiate_block_sort_bn(int32, int32_t)
 instantiate_block_sort_bn(float16, half)
 instantiate_block_sort_bn(float32, float)
-instantiate_block_sort_bn(bfloat16, bfloat16_t)
+instantiate_block_sort_bn(bfloat16, bfloat16_t) // clang-format on
-
+// clang-format off
 #define instantiate_block_sort_long(itname, itype) \
   instantiate_block_sort_tn(itname, itype, 128)    \
   instantiate_block_sort_tn(itname, itype, 256)
 
 instantiate_block_sort_long(uint64, uint64_t)
-instantiate_block_sort_long(int64, int64_t)
+instantiate_block_sort_long(int64, int64_t) // clang-format on
 
-///////////////////////////////////////////////////////////////////////////////
+    ///////////////////////////////////////////////////////////////////////////////
-// Multi block merge sort
+    // Multi block merge sort
-///////////////////////////////////////////////////////////////////////////////
+    ///////////////////////////////////////////////////////////////////////////////
 
-template <
+    template <
         typename val_t,
         typename idx_t,
         bool ARG_SORT,
         short BLOCK_THREADS,
         short N_PER_THREAD,
         typename CompareOp = LessThan<val_t>>
-struct KernelMultiBlockMergeSort {
+    struct KernelMultiBlockMergeSort {
   using block_merge_sort_t = BlockMergeSort<
       val_t,
       idx_t,
@@ -489,14 +477,14 @@ struct KernelMultiBlockMergeSort {
       threadgroup idx_t* tgp_idxs,
       uint3 tid [[threadgroup_position_in_grid]],
       uint3 lid [[thread_position_in_threadgroup]]) {
-
     // tid.y tells us the segment index
     int base_idx = tid.x * N_PER_BLOCK;
 
     // Copy into threadgroup memory
-    for(short i = lid.x; i < N_PER_BLOCK; i+= BLOCK_THREADS) {
+    for (short i = lid.x; i < N_PER_BLOCK; i += BLOCK_THREADS) {
       int idx = base_idx + i;
-      tgp_vals[i] = idx < size_sorted_axis ? inp[idx * stride_sorted_axis] : val_t(CompareOp::init);
+      tgp_vals[i] = idx < size_sorted_axis ? inp[idx * stride_sorted_axis]
+                                           : val_t(CompareOp::init);
       tgp_idxs[i] = idx;
     }
 
@@ -508,9 +496,9 @@ struct KernelMultiBlockMergeSort {
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
     // Write output
-    for(int i = lid.x; i < N_PER_BLOCK; i+= BLOCK_THREADS) {
+    for (int i = lid.x; i < N_PER_BLOCK; i += BLOCK_THREADS) {
       int idx = base_idx + i;
-      if(idx < size_sorted_axis) {
+      if (idx < size_sorted_axis) {
         out_vals[idx] = tgp_vals[i];
         out_idxs[idx] = tgp_idxs[i];
       }
@@ -523,18 +511,17 @@ struct KernelMultiBlockMergeSort {
       int A_sz,
       int B_sz,
       int sort_md) {
-
     CompareOp op;
 
     int A_st = max(0, sort_md - B_sz);
     int A_ed = min(sort_md, A_sz);
 
-    while(A_st < A_ed) {
+    while (A_st < A_ed) {
       int md = A_st + (A_ed - A_st) / 2;
       auto a = As[md];
       auto b = Bs[sort_md - 1 - md];
 
-      if(op(b, a)) {
+      if (op(b, a)) {
         A_ed = md;
       } else {
         A_st = md + 1;
@@ -542,7 +529,6 @@ struct KernelMultiBlockMergeSort {
     }
 
     return A_ed;
-
   }
 };
 
@@ -563,8 +549,12 @@ template <
     const device size_t* nc_strides [[buffer(7)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]]) {
-
+  using sort_kernel = KernelMultiBlockMergeSort<
-  using sort_kernel = KernelMultiBlockMergeSort<val_t, idx_t, ARG_SORT, BLOCK_THREADS, N_PER_THREAD>;
+      val_t,
+      idx_t,
+      ARG_SORT,
+      BLOCK_THREADS,
+      N_PER_THREAD>;
 
   auto block_idx = elem_to_loc(tid.y, nc_shape, nc_strides, nc_dim);
   inp += block_idx;
@@ -592,7 +582,8 @@ template <
     bool ARG_SORT,
     short BLOCK_THREADS,
     short N_PER_THREAD>
-[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void mb_block_partition(
+[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void
+mb_block_partition(
     device idx_t* block_partitions [[buffer(0)]],
     const device val_t* dev_vals [[buffer(1)]],
     const device idx_t* dev_idxs [[buffer(2)]],
@@ -601,7 +592,6 @@ template <
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]],
     uint3 tgp_dims [[threads_per_threadgroup]]) {
-
   using sort_kernel = KernelMultiBlockMergeSort<
       val_t,
       idx_t,
@@ -627,14 +617,9 @@ template <
 
   int partition_at = min(B_ed - A_st, sort_kernel::N_PER_BLOCK * merge_lane);
   int partition = sort_kernel::merge_partition(
-      dev_vals + A_st,
+      dev_vals + A_st, dev_vals + B_st, A_ed - A_st, B_ed - B_st, partition_at);
-      dev_vals + B_st,
-      A_ed - A_st,
-      B_ed - B_st,
-      partition_at);
 
   block_partitions[lid.x] = A_st + partition;
-
 }
 
 template <
@@ -644,7 +629,8 @@ template <
     short BLOCK_THREADS,
     short N_PER_THREAD,
     typename CompareOp = LessThan<val_t>>
-[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void mb_block_merge(
+[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void
+mb_block_merge(
     const device idx_t* block_partitions [[buffer(0)]],
     const device val_t* dev_vals_in [[buffer(1)]],
     const device idx_t* dev_idxs_in [[buffer(2)]],
@@ -655,7 +641,6 @@ template <
     const constant int& num_tiles [[buffer(7)]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]]) {
-
   using sort_kernel = KernelMultiBlockMergeSort<
       val_t,
       idx_t,
@@ -680,11 +665,13 @@ template <
 
   int A_st = block_partitions[block_idx + 0];
   int A_ed = block_partitions[block_idx + 1];
-  int B_st = min(size_sorted_axis, 2 * sort_st + sort_sz/2 + sort_md - A_st);
+  int B_st = min(size_sorted_axis, 2 * sort_st + sort_sz / 2 + sort_md - A_st);
-  int B_ed = min(size_sorted_axis, 2 * sort_st + sort_sz/2 + sort_md + sort_kernel::N_PER_BLOCK - A_ed);
+  int B_ed = min(
+      size_sorted_axis,
+      2 * sort_st + sort_sz / 2 + sort_md + sort_kernel::N_PER_BLOCK - A_ed);
 
-  if((block_idx % merge_tiles) == merge_tiles - 1) {
+  if ((block_idx % merge_tiles) == merge_tiles - 1) {
-    A_ed = min(size_sorted_axis, sort_st + sort_sz/2);
+    A_ed = min(size_sorted_axis, sort_st + sort_sz / 2);
     B_ed = min(size_sorted_axis, sort_st + sort_sz);
   }
 
@@ -694,11 +681,13 @@ template <
   // Load from global memory
   thread val_t thread_vals[N_PER_THREAD];
   thread idx_t thread_idxs[N_PER_THREAD];
-  for(int i = 0; i < N_PER_THREAD; i++) {
+  for (int i = 0; i < N_PER_THREAD; i++) {
     int idx = BLOCK_THREADS * i + lid.x;
-    if(idx < (A_sz + B_sz)) {
+    if (idx < (A_sz + B_sz)) {
-      thread_vals[i] = (idx < A_sz) ? dev_vals_in[A_st + idx] : dev_vals_in[B_st + idx - A_sz];
+      thread_vals[i] = (idx < A_sz) ? dev_vals_in[A_st + idx]
-      thread_idxs[i] = (idx < A_sz) ? dev_idxs_in[A_st + idx] : dev_idxs_in[B_st + idx - A_sz];
+                                    : dev_vals_in[B_st + idx - A_sz];
+      thread_idxs[i] = (idx < A_sz) ? dev_idxs_in[A_st + idx]
+                                    : dev_idxs_in[B_st + idx - A_sz];
     } else {
       thread_vals[i] = CompareOp::init;
       thread_idxs[i] = 0;
@@ -709,7 +698,7 @@ template <
   threadgroup val_t tgp_vals[sort_kernel::N_PER_BLOCK];
   threadgroup idx_t tgp_idxs[sort_kernel::N_PER_BLOCK];
   threadgroup_barrier(mem_flags::mem_threadgroup);
-  for(int i = 0; i < N_PER_THREAD; i++) {
+  for (int i = 0; i < N_PER_THREAD; i++) {
     int idx = BLOCK_THREADS * i + lid.x;
     tgp_vals[idx] = thread_vals[i];
     tgp_idxs[idx] = thread_idxs[i];
@@ -720,11 +709,7 @@ template <
   int sort_md_local = min(A_sz + B_sz, N_PER_THREAD * int(lid.x));
 
   int A_st_local = block_sort_t::merge_partition(
-      tgp_vals,
+      tgp_vals, tgp_vals + A_sz, A_sz, B_sz, sort_md_local);
-      tgp_vals + A_sz,
-      A_sz,
-      B_sz,
-      sort_md_local);
   int A_ed_local = A_sz;
 
   int B_st_local = sort_md_local - A_st_local;
@@ -745,7 +730,7 @@ template <
       thread_idxs);
 
   threadgroup_barrier(mem_flags::mem_threadgroup);
-  for(int i = 0; i < N_PER_THREAD; ++i) {
+  for (int i = 0; i < N_PER_THREAD; ++i) {
     int idx = lid.x * N_PER_THREAD;
     tgp_vals[idx + i] = thread_vals[i];
     tgp_idxs[idx + i] = thread_idxs[i];
@@ -754,19 +739,20 @@ template <
   threadgroup_barrier(mem_flags::mem_threadgroup);
   // Write output
   int base_idx = tid.x * sort_kernel::N_PER_BLOCK;
-  for(int i = lid.x; i < sort_kernel::N_PER_BLOCK; i+= BLOCK_THREADS) {
+  for (int i = lid.x; i < sort_kernel::N_PER_BLOCK; i += BLOCK_THREADS) {
     int idx = base_idx + i;
-    if(idx < size_sorted_axis) {
+    if (idx < size_sorted_axis) {
       dev_vals_out[idx] = tgp_vals[i];
       dev_idxs_out[idx] = tgp_idxs[i];
     }
   }
-
 }
 
-#define instantiate_multi_block_sort(vtname, vtype, itname, itype, arg_sort, bn, tn) \
+#define instantiate_multi_block_sort(                                      \
-  template [[host_name("mb_block_sort_" #vtname "_" #itname "_bn" #bn "_tn" #tn)]] \
+    vtname, vtype, itname, itype, arg_sort, bn, tn)                        \
-  [[kernel]] void mb_block_sort<vtype, itype, arg_sort, bn, tn>( \
+  template [[host_name("mb_block_sort_" #vtname "_" #itname "_bn" #bn      \
+                       "_tn" #tn)]] [[kernel]] void                        \
+  mb_block_sort<vtype, itype, arg_sort, bn, tn>(                           \
       const device vtype* inp [[buffer(0)]],                               \
       device vtype* out_vals [[buffer(1)]],                                \
       device itype* out_idxs [[buffer(2)]],                                \
@@ -777,9 +763,10 @@ template <
       const device size_t* nc_strides [[buffer(7)]],                       \
       uint3 tid [[threadgroup_position_in_grid]],                          \
       uint3 lid [[thread_position_in_threadgroup]]);                       \
-  template [[host_name("mb_block_partition_" #vtname "_" #itname "_bn" #bn "_tn" #tn)]] \
+  template [[host_name("mb_block_partition_" #vtname "_" #itname "_bn" #bn \
-  [[kernel]] void mb_block_partition<vtype, itype, arg_sort, bn, tn>( \
+                       "_tn" #tn)]] [[kernel]] void                        \
-    device itype* block_partitions [[buffer(0)]], \
+  mb_block_partition<vtype, itype, arg_sort, bn, tn>(                      \
+      device itype * block_partitions [[buffer(0)]],                       \
       const device vtype* dev_vals [[buffer(1)]],                          \
       const device itype* dev_idxs [[buffer(2)]],                          \
       const constant int& size_sorted_axis [[buffer(3)]],                  \
@@ -787,8 +774,9 @@ template <
       uint3 tid [[threadgroup_position_in_grid]],                          \
       uint3 lid [[thread_position_in_threadgroup]],                        \
       uint3 tgp_dims [[threads_per_threadgroup]]);                         \
-  template [[host_name("mb_block_merge_" #vtname "_" #itname "_bn" #bn "_tn" #tn)]] \
+  template [[host_name("mb_block_merge_" #vtname "_" #itname "_bn" #bn     \
-  [[kernel]] void mb_block_merge<vtype, itype, arg_sort, bn, tn>( \
+                       "_tn" #tn)]] [[kernel]] void                        \
+  mb_block_merge<vtype, itype, arg_sort, bn, tn>(                          \
       const device itype* block_partitions [[buffer(0)]],                  \
       const device vtype* dev_vals_in [[buffer(1)]],                       \
       const device itype* dev_idxs_in [[buffer(2)]],                       \
@@ -800,6 +788,7 @@ template <
       uint3 tid [[threadgroup_position_in_grid]],                          \
       uint3 lid [[thread_position_in_threadgroup]]);
 
+// clang-format off
 #define instantiate_multi_block_sort_base(vtname, vtype) \
   instantiate_multi_block_sort(vtname, vtype, uint32, uint32_t, true, 512, 8)
 
@@ -811,10 +800,11 @@ instantiate_multi_block_sort_base(int16, int16_t)
 instantiate_multi_block_sort_base(int32, int32_t)
 instantiate_multi_block_sort_base(float16, half)
 instantiate_multi_block_sort_base(float32, float)
-instantiate_multi_block_sort_base(bfloat16, bfloat16_t)
+instantiate_multi_block_sort_base(bfloat16, bfloat16_t) // clang-format on
 
+// clang-format off
 #define instantiate_multi_block_sort_long(vtname, vtype) \
   instantiate_multi_block_sort(vtname, vtype, uint32, uint32_t, true, 256, 8)
 
 instantiate_multi_block_sort_long(uint64, uint64_t)
-instantiate_multi_block_sort_long(int64, int64_t)
+instantiate_multi_block_sort_long(int64, int64_t) // clang-format on

mlx/backend/metal/kernels/steel/conv/kernels/steel_conv.metal

@@ -4,13 +4,14 @@
 
 #include "mlx/backend/metal/kernels/steel/gemm/mma.h"
 
+#include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/steel/conv/conv.h"
 #include "mlx/backend/metal/kernels/steel/conv/params.h"
-#include "mlx/backend/metal/kernels/bf16.h"
 
 using namespace metal;
 
-template <typename T,
+template <
+    typename T,
     int BM,
     int BN,
     int BK,
@@ -18,7 +19,8 @@ template <typename T,
     int WN,
     int N_CHANNELS = 0,
     bool SMALL_FILTER = false>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void implicit_gemm_conv_2d(
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void
+implicit_gemm_conv_2d(
     const device T* A [[buffer(0)]],
     const device T* B [[buffer(1)]],
     device T* C [[buffer(2)]],
@@ -28,8 +30,6 @@ template <typename T,
     uint3 lid [[thread_position_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
-
   using namespace mlx::steel;
 
   (void)lid;
@@ -56,7 +56,13 @@ template <typename T,
 
       // Go to small channel specialization
       Conv2DInputBlockLoaderSmallChannels<
-            T, BM, BN, BK, tgp_size, N_CHANNELS, tgp_padding_a>,
+          T,
+          BM,
+          BN,
+          BK,
+          tgp_size,
+          N_CHANNELS,
+          tgp_padding_a>,
 
       // Else go to general loader
       typename metal::conditional_t<
@@ -65,14 +71,21 @@ template <typename T,
 
           // Go to small filter specialization
           Conv2DInputBlockLoaderSmallFilter<
-                  T, BM, BN, BK, tgp_size, tgp_padding_a>,
+              T,
+              BM,
+              BN,
+              BK,
+              tgp_size,
+              tgp_padding_a>,
 
           // Else go to large filter generalization
           Conv2DInputBlockLoaderLargeFilter<
-                  T, BM, BN, BK, tgp_size, tgp_padding_a>
+              T,
-        >
+              BM,
-  >;
+              BN,
-
+              BK,
+              tgp_size,
+              tgp_padding_a>>>;
 
   // Weight loader
   using loader_b_t = typename metal::conditional_t<
@@ -81,11 +94,16 @@ template <typename T,
 
       // Go to small channel specialization
       Conv2DWeightBlockLoaderSmallChannels<
-            T, BM, BN, BK, tgp_size, N_CHANNELS, tgp_padding_b>,
+          T,
+          BM,
+          BN,
+          BK,
+          tgp_size,
+          N_CHANNELS,
+          tgp_padding_b>,
 
       // Else go to general loader
-        Conv2DWeightBlockLoader<T, BM, BN, BK, tgp_size, tgp_padding_b>
+      Conv2DWeightBlockLoader<T, BM, BN, BK, tgp_size, tgp_padding_b>>;
-  >;
 
   using mma_t = BlockMMA<
       T,
@@ -123,8 +141,10 @@ template <typename T,
   const int2 offsets_b(0, c_col);
 
   // Prepare threadgroup loading operations
-  loader_a_t loader_a(A, As, offsets_a, params, gemm_params, simd_gid, simd_lid);
+  loader_a_t loader_a(
-  loader_b_t loader_b(B, Bs, offsets_b, params, gemm_params, simd_gid, simd_lid);
+      A, As, offsets_a, params, gemm_params, simd_gid, simd_lid);
+  loader_b_t loader_b(
+      B, Bs, offsets_b, params, gemm_params, simd_gid, simd_lid);
 
   // Prepare threadgroup mma operation
   mma_t mma_op(simd_gid, simd_lid);
@@ -152,12 +172,24 @@ template <typename T,
   short tgp_bm = min(BM, gemm_params->M - c_row);
   short tgp_bn = min(BN, gemm_params->N - c_col);
   mma_op.store_result_safe(C, N, short2(tgp_bn, tgp_bm));
-
 }
 
-#define instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn, channel_name, n_channels, filter_name, small_filter) \
+#define instantiate_implicit_conv_2d(                                          \
-  template [[host_name("implicit_gemm_conv_2d_" #name "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn "_channel_" #channel_name "_filter_" #filter_name)]] \
+    name,                                                                      \
-  [[kernel]] void implicit_gemm_conv_2d<itype, bm, bn, bk, wm, wn, n_channels, small_filter>( \
+    itype,                                                                     \
+    bm,                                                                        \
+    bn,                                                                        \
+    bk,                                                                        \
+    wm,                                                                        \
+    wn,                                                                        \
+    channel_name,                                                              \
+    n_channels,                                                                \
+    filter_name,                                                               \
+    small_filter)                                                              \
+  template [[host_name("implicit_gemm_conv_2d_" #name "_bm" #bm "_bn" #bn      \
+                       "_bk" #bk "_wm" #wm "_wn" #wn "_channel_" #channel_name \
+                       "_filter_" #filter_name)]] [[kernel]] void              \
+  implicit_gemm_conv_2d<itype, bm, bn, bk, wm, wn, n_channels, small_filter>(  \
       const device itype* A [[buffer(0)]],                                     \
       const device itype* B [[buffer(1)]],                                     \
       device itype* C [[buffer(2)]],                                           \
@@ -168,22 +200,25 @@ template <typename T,
       uint simd_gid [[simdgroup_index_in_threadgroup]],                        \
       uint simd_lid [[thread_index_in_simdgroup]]);
 
+// clang-format off
 #define instantiate_implicit_2d_filter(name, itype, bm, bn, bk, wm, wn)           \
     instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn, l, 0, s, true)  \
     instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn, l, 0, l, false) \
     instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn, 1, 1, l, false) \
     instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn, 2, 2, l, false) \
     instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn, 3, 3, l, false) \
-    instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn, 4, 4, l, false)
+    instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn, 4, 4, l, false) // clang-format on
 
+// clang-format off
 #define instantiate_implicit_2d_blocks(name, itype)               \
     instantiate_implicit_2d_filter(name, itype, 32,  8, 16, 4, 1) \
     instantiate_implicit_2d_filter(name, itype, 64,  8, 16, 4, 1) \
     instantiate_implicit_2d_filter(name, itype, 32, 32, 16, 2, 2) \
     instantiate_implicit_2d_filter(name, itype, 32, 64, 16, 2, 2) \
     instantiate_implicit_2d_filter(name, itype, 64, 32, 16, 2, 2) \
-    instantiate_implicit_2d_filter(name, itype, 64, 64, 16, 2, 2)
+    instantiate_implicit_2d_filter(name, itype, 64, 64, 16, 2, 2) // clang-format on
 
+// clang-format off
 instantiate_implicit_2d_blocks(float32, float);
 instantiate_implicit_2d_blocks(float16, half);
-instantiate_implicit_2d_blocks(bfloat16, bfloat16_t);
+instantiate_implicit_2d_blocks(bfloat16, bfloat16_t); // clang-format on

mlx/backend/metal/kernels/steel/conv/kernels/steel_conv_general.metal

@@ -4,15 +4,16 @@
 
 #include "mlx/backend/metal/kernels/steel/gemm/mma.h"
 
-#include "mlx/backend/metal/kernels/steel/conv/conv.h"
-#include "mlx/backend/metal/kernels/steel/conv/params.h"
-#include "mlx/backend/metal/kernels/steel/conv/loaders/loader_general.h"
 #include "mlx/backend/metal/kernels/bf16.h"
+#include "mlx/backend/metal/kernels/steel/conv/conv.h"
+#include "mlx/backend/metal/kernels/steel/conv/loaders/loader_general.h"
+#include "mlx/backend/metal/kernels/steel/conv/params.h"
 
 using namespace metal;
 using namespace mlx::steel;
 
-template <typename T,
+template <
+    typename T,
     int BM,
     int BN,
     int BK,
@@ -20,7 +21,8 @@ template <typename T,
     int WN,
     typename AccumType = float,
     typename Epilogue = TransformNone<T, AccumType>>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void implicit_gemm_conv_2d_general(
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void
+implicit_gemm_conv_2d_general(
     const device T* A [[buffer(0)]],
     const device T* B [[buffer(1)]],
     device T* C [[buffer(2)]],
@@ -33,7 +35,6 @@ template <typename T,
     uint3 lid [[thread_position_in_threadgroup]],
     uint simd_gid [[simdgroup_index_in_threadgroup]],
     uint simd_lid [[thread_index_in_simdgroup]]) {
-
   (void)lid;
 
   constexpr bool transpose_a = false;
@@ -51,12 +52,12 @@ template <typename T,
   constexpr short tgp_size = WM * WN * 32;
 
   // Input loader
-  using loader_a_t = Conv2DInputBlockLoaderGeneral<
+  using loader_a_t =
-      T, BM, BN, BK, tgp_size, tgp_padding_a>;
+      Conv2DInputBlockLoaderGeneral<T, BM, BN, BK, tgp_size, tgp_padding_a>;
 
   // Weight loader
-  using loader_b_t = Conv2DWeightBlockLoaderGeneral<
+  using loader_b_t =
-      T, BM, BN, BK, tgp_size, tgp_padding_b>;
+      Conv2DWeightBlockLoaderGeneral<T, BM, BN, BK, tgp_size, tgp_padding_b>;
 
   using mma_t = BlockMMA<
       T,
@@ -103,13 +104,32 @@ template <typename T,
   const int2 offsets_b(0, c_col);
 
   // Prepare threadgroup loading operations
-  loader_a_t loader_a(A, As, offsets_a, params, jump_params, base_wh, base_ww, simd_gid, simd_lid);
+  loader_a_t loader_a(
-  loader_b_t loader_b(B, Bs, offsets_b, params, jump_params, base_wh, base_ww, simd_gid, simd_lid);
+      A,
+      As,
+      offsets_a,
+      params,
+      jump_params,
+      base_wh,
+      base_ww,
+      simd_gid,
+      simd_lid);
+  loader_b_t loader_b(
+      B,
+      Bs,
+      offsets_b,
+      params,
+      jump_params,
+      base_wh,
+      base_ww,
+      simd_gid,
+      simd_lid);
 
   // Prepare threadgroup mma operation
   mma_t mma_op(simd_gid, simd_lid);
 
-  int gemm_k_iterations = base_wh_size * base_ww_size * gemm_params->gemm_k_iterations;
+  int gemm_k_iterations =
+      base_wh_size * base_ww_size * gemm_params->gemm_k_iterations;
 
   for (int k = 0; k < gemm_k_iterations; k++) {
     threadgroup_barrier(mem_flags::mem_threadgroup);
@@ -143,22 +163,24 @@ template <typename T,
 
     STEEL_PRAGMA_UNROLL
     for (int i = 0; i < mma_t::TM; i++) {
-
       int cm = offset_m + i * mma_t::TM_stride;
 
       int n = cm / jump_params->adj_out_hw;
       int hw = cm % jump_params->adj_out_hw;
-      int oh = (hw / jump_params->adj_out_w) * jump_params->f_out_jump_h + base_oh;
+      int oh =
-      int ow = (hw % jump_params->adj_out_w) * jump_params->f_out_jump_w + base_ow;
+          (hw / jump_params->adj_out_w) * jump_params->f_out_jump_h + base_oh;
+      int ow =
+          (hw % jump_params->adj_out_w) * jump_params->f_out_jump_w + base_ow;
 
-      if(n < params->N && oh < params->oS[0] && ow < params->oS[1]) {
+      if (n < params->N && oh < params->oS[0] && ow < params->oS[1]) {
-
+        int offset_cm = n * params->out_strides[0] +
-        int offset_cm = n * params->out_strides[0] + oh * params->out_strides[1] + ow * params->out_strides[2];
+            oh * params->out_strides[1] + ow * params->out_strides[2];
 
         STEEL_PRAGMA_UNROLL
         for (int j = 0; j < mma_t::TN; j++) {
           // Get accumulated result and associated offset in C
-          thread const auto& accum = mma_op.results[i * mma_t::TN + j].thread_elements();
+          thread const auto& accum =
+              mma_op.results[i * mma_t::TN + j].thread_elements();
           int offset = offset_cm + (j * mma_t::TN_stride);
 
           // Apply epilogue and output C
@@ -170,16 +192,16 @@ template <typename T,
             C[offset + 1] = Epilogue::apply(accum[1]);
           }
         }
-
       }
     }
   }
-
 }
 
 #define instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn)         \
-  template [[host_name("implicit_gemm_conv_2d_general_" #name "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn)]] \
+  template                                                                    \
-  [[kernel]] void implicit_gemm_conv_2d_general<itype, bm, bn, bk, wm, wn>( \
+      [[host_name("implicit_gemm_conv_2d_general_" #name "_bm" #bm "_bn" #bn  \
+                  "_bk" #bk "_wm" #wm "_wn" #wn)]] [[kernel]] void            \
+      implicit_gemm_conv_2d_general<itype, bm, bn, bk, wm, wn>(               \
           const device itype* A [[buffer(0)]],                                \
           const device itype* B [[buffer(1)]],                                \
           device itype* C [[buffer(2)]],                                      \
@@ -196,14 +218,16 @@ template <typename T,
 #define instantiate_implicit_2d_filter(name, itype, bm, bn, bk, wm, wn) \
   instantiate_implicit_conv_2d(name, itype, bm, bn, bk, wm, wn)
 
+// clang-format off
 #define instantiate_implicit_2d_blocks(name, itype)               \
     instantiate_implicit_2d_filter(name, itype, 32,  8, 16, 4, 1) \
     instantiate_implicit_2d_filter(name, itype, 64,  8, 16, 4, 1) \
     instantiate_implicit_2d_filter(name, itype, 32, 32, 16, 2, 2) \
     instantiate_implicit_2d_filter(name, itype, 32, 64, 16, 2, 2) \
     instantiate_implicit_2d_filter(name, itype, 64, 32, 16, 2, 2) \
-    instantiate_implicit_2d_filter(name, itype, 64, 64, 16, 2, 2)
+    instantiate_implicit_2d_filter(name, itype, 64, 64, 16, 2, 2) // clang-format on
 
+// clang-format off
 instantiate_implicit_2d_blocks(float32, float);
 instantiate_implicit_2d_blocks(float16, half);
-instantiate_implicit_2d_blocks(bfloat16, bfloat16_t);
+instantiate_implicit_2d_blocks(bfloat16, bfloat16_t); // clang-format on

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm.metal

@@ -1,8 +1,8 @@
 // Copyright © 2024 Apple Inc.
 
 #include "mlx/backend/metal/kernels/bf16.h"
-#include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/steel/gemm/gemm.h"
+#include "mlx/backend/metal/kernels/utils.h"
 
 using namespace metal;
 using namespace mlx::steel;
@@ -11,7 +11,8 @@ using namespace mlx::steel;
 // GEMM kernels
 ///////////////////////////////////////////////////////////////////////////////
 
-template <typename T,
+template <
+    typename T,
     int BM,
     int BN,
     int BK,
@@ -21,10 +22,10 @@ template <typename T,
     bool transpose_b,
     bool MN_aligned,
     bool K_aligned>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void gemm(
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void gemm(
-    const device T *A [[buffer(0)]],
+    const device T* A [[buffer(0)]],
-    const device T *B [[buffer(1)]],
+    const device T* B [[buffer(1)]],
-    device T *D [[buffer(3)]],
+    device T* D [[buffer(3)]],
     const constant GEMMParams* params [[buffer(4)]],
     const constant int* batch_shape [[buffer(6)]],
     const constant size_t* batch_strides [[buffer(7)]],
@@ -32,14 +33,24 @@ template <typename T,
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]]) {
-    
+  using gemm_kernel = GEMMKernel<
-    using gemm_kernel = GEMMKernel<T, T, BM, BN, BK, WM, WN, transpose_a, transpose_b, MN_aligned, K_aligned>;
+      T,
+      T,
+      BM,
+      BN,
+      BK,
+      WM,
+      WN,
+      transpose_a,
+      transpose_b,
+      MN_aligned,
+      K_aligned>;
 
   threadgroup T As[gemm_kernel::tgp_mem_size_a];
   threadgroup T Bs[gemm_kernel::tgp_mem_size_b];
 
   // Adjust for batch
-    if(params->batch_ndim > 1) {
+  if (params->batch_ndim > 1) {
     const constant size_t* A_bstrides = batch_strides;
     const constant size_t* B_bstrides = batch_strides + params->batch_ndim;
 
@@ -57,23 +68,37 @@ template <typename T,
   D += params->batch_stride_d * tid.z;
 
   gemm_kernel::run(
-      A, B, D, 
+      A, B, D, params, As, Bs, simd_lane_id, simd_group_id, tid, lid);
-      params,
-      As, Bs,
-      simd_lane_id, simd_group_id, tid, lid
-    );
 }
 
 ///////////////////////////////////////////////////////////////////////////////
 // GEMM kernel initializations
 ///////////////////////////////////////////////////////////////////////////////
 
-#define instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned) \
+#define instantiate_gemm(                                                    \
-  template [[host_name("steel_gemm_" #tname "_"  #iname "_" #oname "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn "_MN_" #aname "_K_" #kname)]] \
+    tname,                                                                   \
-  [[kernel]] void gemm<itype, bm, bn, bk, wm, wn, trans_a, trans_b, mn_aligned, k_aligned>( \
+    trans_a,                                                                 \
-      const device itype *A [[buffer(0)]], \
+    trans_b,                                                                 \
-      const device itype *B [[buffer(1)]], \
+    iname,                                                                   \
-      device itype *D [[buffer(3)]], \
+    itype,                                                                   \
+    oname,                                                                   \
+    otype,                                                                   \
+    bm,                                                                      \
+    bn,                                                                      \
+    bk,                                                                      \
+    wm,                                                                      \
+    wn,                                                                      \
+    aname,                                                                   \
+    mn_aligned,                                                              \
+    kname,                                                                   \
+    k_aligned)                                                               \
+  template [[host_name("steel_gemm_" #tname "_" #iname "_" #oname "_bm" #bm  \
+                       "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn "_MN_" #aname \
+                       "_K_" #kname)]] [[kernel]] void                       \
+  gemm<itype, bm, bn, bk, wm, wn, trans_a, trans_b, mn_aligned, k_aligned>(  \
+      const device itype* A [[buffer(0)]],                                   \
+      const device itype* B [[buffer(1)]],                                   \
+      device itype* D [[buffer(3)]],                                         \
       const constant GEMMParams* params [[buffer(4)]],                       \
       const constant int* batch_shape [[buffer(6)]],                         \
       const constant size_t* batch_strides [[buffer(7)]],                    \
@@ -82,26 +107,30 @@ template <typename T,
       uint3 tid [[threadgroup_position_in_grid]],                            \
       uint3 lid [[thread_position_in_threadgroup]]);
 
+// clang-format off
 #define instantiate_gemm_aligned_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn)             \
   instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, taligned, true)  \
   instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, naligned, false) \
   instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, taligned, true) \
-  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, naligned, false)
+  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, naligned, false) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_transpose_helper(iname, itype, oname, otype, bm, bn, bk, wm, wn)             \
     instantiate_gemm_aligned_helper(nn, false, false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
     instantiate_gemm_aligned_helper(nt, false, true , iname, itype, oname, otype, bm, bn, bk, wm, wn) \
     instantiate_gemm_aligned_helper(tn, true , false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
-    instantiate_gemm_aligned_helper(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn)
+    instantiate_gemm_aligned_helper(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_shapes_helper(iname, itype, oname, otype)                  \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 32, 16, 2, 2) \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 64, 16, 2, 2) \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 32, 32, 2, 2) \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 32, 16, 2, 2) \
-    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 64, 16, 2, 2)
+    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 64, 16, 2, 2) // clang-format on
 
+// clang-format off
 instantiate_gemm_shapes_helper(float16, half, float16, half);
 instantiate_gemm_shapes_helper(bfloat16, bfloat16_t, bfloat16, bfloat16_t);
 
-instantiate_gemm_shapes_helper(float32, float, float32, float);
+instantiate_gemm_shapes_helper(float32, float, float32, float); // clang-format on

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_addmm.metal

@@ -10,7 +10,8 @@ using namespace mlx::steel;
 // GEMM kernels
 ///////////////////////////////////////////////////////////////////////////////
 
-template <typename T,
+template <
+    typename T,
     int BM,
     int BN,
     int BK,
@@ -22,11 +23,11 @@ template <typename T,
     bool K_aligned,
     typename AccumType = float,
     typename Epilogue = TransformAdd<T, AccumType>>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void addmm(
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void addmm(
-    const device T *A [[buffer(0)]],
+    const device T* A [[buffer(0)]],
-    const device T *B [[buffer(1)]],
+    const device T* B [[buffer(1)]],
-    const device T *C [[buffer(2)]],
+    const device T* C [[buffer(2)]],
-    device T *D [[buffer(3)]],
+    device T* D [[buffer(3)]],
     const constant GEMMParams* params [[buffer(4)]],
     const constant GEMMAddMMParams* addmm_params [[buffer(5)]],
     const constant int* batch_shape [[buffer(6)]],
@@ -35,15 +36,23 @@ template <typename T,
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]]) {
-    
   // Pacifying compiler
   (void)lid;
 
-    using gemm_kernel = 
+  using gemm_kernel = GEMMKernel<
-        GEMMKernel<T, T, BM, BN, BK, WM, WN, 
+      T,
-        transpose_a, transpose_b, 
+      T,
-        MN_aligned, K_aligned,
+      BM,
-        AccumType, Epilogue>;
+      BN,
+      BK,
+      WM,
+      WN,
+      transpose_a,
+      transpose_b,
+      MN_aligned,
+      K_aligned,
+      AccumType,
+      Epilogue>;
 
   using loader_a_t = typename gemm_kernel::loader_a_t;
   using loader_b_t = typename gemm_kernel::loader_b_t;
@@ -53,13 +62,18 @@ template <typename T,
   threadgroup T Bs[gemm_kernel::tgp_mem_size_b];
 
   // Adjust for batch
-    if(params->batch_ndim > 1) {
+  if (params->batch_ndim > 1) {
     const constant size_t* A_bstrides = batch_strides;
     const constant size_t* B_bstrides = batch_strides + params->batch_ndim;
     const constant size_t* C_bstrides = B_bstrides + params->batch_ndim;
 
     ulong3 batch_offsets = elem_to_loc_broadcast(
-          tid.z, batch_shape, A_bstrides, B_bstrides, C_bstrides, params->batch_ndim);
+        tid.z,
+        batch_shape,
+        A_bstrides,
+        B_bstrides,
+        C_bstrides,
+        params->batch_ndim);
 
     A += batch_offsets.x;
     B += batch_offsets.y;
@@ -140,7 +154,8 @@ template <typename T,
     }
 
     // Store results to device memory
-      mma_op.store_result(D, params->ldd, C, addmm_params->ldc, addmm_params->fdc, epilogue_op);
+    mma_op.store_result(
+        D, params->ldd, C, addmm_params->ldc, addmm_params->fdc, epilogue_op);
     return;
 
   }
@@ -164,7 +179,8 @@ template <typename T,
           leftover_bk,
           LoopAlignment<true, true, K_aligned>{});
 
-        mma_op.store_result(D, params->ldd, C, addmm_params->ldc, addmm_params->fdc, epilogue_op);
+      mma_op.store_result(
+          D, params->ldd, C, addmm_params->ldc, addmm_params->fdc, epilogue_op);
       return;
 
     } else if (tgp_bn == BN) {
@@ -181,8 +197,11 @@ template <typename T,
           LoopAlignment<false, true, K_aligned>{});
 
       return mma_op.store_result_safe(
-            D, params->ldd, 
+          D,
-            C, addmm_params->ldc, addmm_params->fdc,
+          params->ldd,
+          C,
+          addmm_params->ldc,
+          addmm_params->fdc,
           short2(tgp_bn, tgp_bm),
           epilogue_op);
 
@@ -200,8 +219,11 @@ template <typename T,
           LoopAlignment<true, false, K_aligned>{});
 
       return mma_op.store_result_safe(
-            D, params->ldd, 
+          D,
-            C, addmm_params->ldc, addmm_params->fdc,
+          params->ldd,
+          C,
+          addmm_params->ldc,
+          addmm_params->fdc,
           short2(tgp_bn, tgp_bm),
           epilogue_op);
 
@@ -219,8 +241,11 @@ template <typename T,
           LoopAlignment<false, false, K_aligned>{});
 
       return mma_op.store_result_safe(
-            D, params->ldd, 
+          D,
-            C, addmm_params->ldc, addmm_params->fdc,
+          params->ldd,
+          C,
+          addmm_params->ldc,
+          addmm_params->fdc,
           short2(tgp_bn, tgp_bm),
           epilogue_op);
     }
@@ -231,13 +256,45 @@ template <typename T,
 // GEMM kernel initializations
 ///////////////////////////////////////////////////////////////////////////////
 
-#define instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned, ep_name, epilogue) \
+#define instantiate_gemm(                                                    \
-  template [[host_name("steel_addmm_" #tname "_"  #iname "_" #oname "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn "_MN_" #aname "_K_" #kname "_" #ep_name)]] \
+    tname,                                                                   \
-  [[kernel]] void addmm<itype, bm, bn, bk, wm, wn, trans_a, trans_b, mn_aligned, k_aligned, float, epilogue<itype, float>>( \
+    trans_a,                                                                 \
-      const device itype *A [[buffer(0)]], \
+    trans_b,                                                                 \
-      const device itype *B [[buffer(1)]], \
+    iname,                                                                   \
-      const device itype *C [[buffer(2)]], \
+    itype,                                                                   \
-      device itype *D [[buffer(3)]], \
+    oname,                                                                   \
+    otype,                                                                   \
+    bm,                                                                      \
+    bn,                                                                      \
+    bk,                                                                      \
+    wm,                                                                      \
+    wn,                                                                      \
+    aname,                                                                   \
+    mn_aligned,                                                              \
+    kname,                                                                   \
+    k_aligned,                                                               \
+    ep_name,                                                                 \
+    epilogue)                                                                \
+  template [[host_name("steel_addmm_" #tname "_" #iname "_" #oname "_bm" #bm \
+                       "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn "_MN_" #aname \
+                       "_K_" #kname "_" #ep_name)]] [[kernel]] void          \
+  addmm<                                                                     \
+      itype,                                                                 \
+      bm,                                                                    \
+      bn,                                                                    \
+      bk,                                                                    \
+      wm,                                                                    \
+      wn,                                                                    \
+      trans_a,                                                               \
+      trans_b,                                                               \
+      mn_aligned,                                                            \
+      k_aligned,                                                             \
+      float,                                                                 \
+      epilogue<itype, float>>(                                               \
+      const device itype* A [[buffer(0)]],                                   \
+      const device itype* B [[buffer(1)]],                                   \
+      const device itype* C [[buffer(2)]],                                   \
+      device itype* D [[buffer(3)]],                                         \
       const constant GEMMParams* gemm_params [[buffer(4)]],                  \
       const constant GEMMAddMMParams* params [[buffer(5)]],                  \
       const constant int* batch_shape [[buffer(6)]],                         \
@@ -247,30 +304,35 @@ template <typename T,
       uint3 tid [[threadgroup_position_in_grid]],                            \
       uint3 lid [[thread_position_in_threadgroup]]);
 
+// clang-format off
 #define instantiate_gemm_bias_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned)      \
   instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned, add, TransformAdd)     \
-  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned, axpby, TransformAxpby)
+  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned, axpby, TransformAxpby) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_aligned_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn)                         \
   instantiate_gemm_bias_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, taligned, true)  \
   instantiate_gemm_bias_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, naligned, false) \
   instantiate_gemm_bias_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, taligned, true) \
-  instantiate_gemm_bias_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, naligned, false)
+  instantiate_gemm_bias_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, naligned, false) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_transpose_helper(iname, itype, oname, otype, bm, bn, bk, wm, wn)             \
     instantiate_gemm_aligned_helper(nn, false, false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
     instantiate_gemm_aligned_helper(nt, false, true , iname, itype, oname, otype, bm, bn, bk, wm, wn) \
     instantiate_gemm_aligned_helper(tn, true , false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
-    instantiate_gemm_aligned_helper(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn)
+    instantiate_gemm_aligned_helper(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_shapes_helper(iname, itype, oname, otype)                  \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 32, 16, 2, 2) \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 64, 16, 2, 2) \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 32, 32, 2, 2) \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 32, 16, 2, 2) \
-    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 64, 16, 2, 2)
+    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 64, 16, 2, 2) // clang-format on
 
+// clang-format off
 instantiate_gemm_shapes_helper(float16, half, float16, half);
 instantiate_gemm_shapes_helper(bfloat16, bfloat16_t, bfloat16, bfloat16_t);
 
-instantiate_gemm_shapes_helper(float32, float, float32, float);
+instantiate_gemm_shapes_helper(float32, float, float32, float); // clang-format on

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_masked.metal

@@ -1,8 +1,8 @@
 // Copyright © 2024 Apple Inc.
 
 #include "mlx/backend/metal/kernels/bf16.h"
-#include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/steel/gemm/gemm.h"
+#include "mlx/backend/metal/kernels/utils.h"
 
 using namespace metal;
 using namespace mlx::steel;
@@ -11,7 +11,8 @@ using namespace mlx::steel;
 // GEMM kernels
 ///////////////////////////////////////////////////////////////////////////////
 
-template <typename T,
+template <
+    typename T,
     int BM,
     int BN,
     int BK,
@@ -21,27 +22,38 @@ template <typename T,
     bool transpose_b,
     bool MN_aligned,
     bool K_aligned,
-          bool has_operand_mask=false>
+    bool has_operand_mask = false>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void block_masked_gemm(
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void
-    const device T *A [[buffer(0)]],
+block_masked_gemm(
-    const device T *B [[buffer(1)]],
+    const device T* A [[buffer(0)]],
-    device T *D [[buffer(3)]],
+    const device T* B [[buffer(1)]],
+    device T* D [[buffer(3)]],
     const constant GEMMParams* params [[buffer(4)]],
     const constant int* batch_shape [[buffer(6)]],
     const constant size_t* batch_strides [[buffer(7)]],
-    const device bool *out_mask [[buffer(10)]],
+    const device bool* out_mask [[buffer(10)]],
-    const device bool *lhs_mask [[buffer(11)]],
+    const device bool* lhs_mask [[buffer(11)]],
-    const device bool *rhs_mask [[buffer(12)]],
+    const device bool* rhs_mask [[buffer(12)]],
     const constant int* mask_strides [[buffer(13)]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]]) {
-
   // Appease the compiler
   (void)lid;
 
-    using gemm_kernel = GEMMKernel<T, T, BM, BN, BK, WM, WN, transpose_a, transpose_b, MN_aligned, K_aligned>;
+  using gemm_kernel = GEMMKernel<
+      T,
+      T,
+      BM,
+      BN,
+      BK,
+      WM,
+      WN,
+      transpose_a,
+      transpose_b,
+      MN_aligned,
+      K_aligned>;
 
   const int tid_y = ((tid.y) << params->swizzle_log) +
       ((tid.x) & ((1 << params->swizzle_log) - 1));
@@ -51,30 +63,38 @@ template <typename T,
     return;
   }
 
-    if(params->batch_ndim > 1) {
+  if (params->batch_ndim > 1) {
-      const constant size_t* mask_batch_strides = batch_strides + 2 * params->batch_ndim;
+    const constant size_t* mask_batch_strides =
-      out_mask += elem_to_loc(tid.z, batch_shape, mask_batch_strides, params->batch_ndim);
+        batch_strides + 2 * params->batch_ndim;
+    out_mask +=
+        elem_to_loc(tid.z, batch_shape, mask_batch_strides, params->batch_ndim);
 
-      if(has_operand_mask) {
+    if (has_operand_mask) {
-        const constant size_t* mask_strides_lhs = mask_batch_strides + params->batch_ndim;
+      const constant size_t* mask_strides_lhs =
-        const constant size_t* mask_strides_rhs = mask_strides_lhs + params->batch_ndim;
+          mask_batch_strides + params->batch_ndim;
+      const constant size_t* mask_strides_rhs =
+          mask_strides_lhs + params->batch_ndim;
 
       ulong2 batch_offsets = elem_to_loc_broadcast(
-            tid.z, batch_shape, mask_strides_lhs, mask_strides_rhs, params->batch_ndim);
+          tid.z,
+          batch_shape,
+          mask_strides_lhs,
+          mask_strides_rhs,
+          params->batch_ndim);
 
       lhs_mask += batch_offsets.x;
       rhs_mask += batch_offsets.y;
     }
   } else {
     out_mask += tid.z * batch_strides[2 * params->batch_ndim];
-      if(has_operand_mask) { 
+    if (has_operand_mask) {
       lhs_mask += tid.z * batch_strides[3 * params->batch_ndim];
       rhs_mask += tid.z * batch_strides[4 * params->batch_ndim];
     }
   }
 
   // Adjust for batch
-    if(params->batch_ndim > 1) {
+  if (params->batch_ndim > 1) {
     const constant size_t* A_bstrides = batch_strides;
     const constant size_t* B_bstrides = batch_strides + params->batch_ndim;
 
@@ -99,11 +119,10 @@ template <typename T,
   B += transpose_b ? c_col * params->ldb : c_col;
   D += c_row * params->ldd + c_col;
 
-
   bool mask_out = out_mask[tid_y * mask_strides[1] + tid_x * mask_strides[0]];
 
   // Write zeros and return
-    if(!mask_out) {
+  if (!mask_out) {
     constexpr short tgp_size = WM * WN * 32;
     constexpr short vec_size = 4;
 
@@ -123,7 +142,7 @@ template <typename T,
     if (MN_aligned || (tgp_bm == BM && tgp_bn == BN)) {
       for (short ti = 0; ti < BM; ti += TM) {
         STEEL_PRAGMA_UNROLL
-          for(short j = 0; j < vec_size; j++) {
+        for (short j = 0; j < vec_size; j++) {
           D[ti * params->ldd + j] = T(0.);
         }
       }
@@ -131,7 +150,7 @@ template <typename T,
       short jmax = tgp_bn - bj;
       jmax = jmax < vec_size ? jmax : vec_size;
       for (short ti = 0; (bi + ti) < tgp_bm; ti += TM) {
-          for(short j = 0; j < jmax; j++) {
+        for (short j = 0; j < jmax; j++) {
           D[ti * params->ldd + j] = T(0.);
         }
       }
@@ -151,8 +170,10 @@ template <typename T,
   threadgroup T Bs[gemm_kernel::tgp_mem_size_b];
 
   // Prepare threadgroup loading operations
-    thread typename gemm_kernel::loader_a_t loader_a(A, params->lda, As, simd_group_id, simd_lane_id);
+  thread typename gemm_kernel::loader_a_t loader_a(
-    thread typename gemm_kernel::loader_b_t loader_b(B, params->ldb, Bs, simd_group_id, simd_lane_id);
+      A, params->lda, As, simd_group_id, simd_lane_id);
+  thread typename gemm_kernel::loader_b_t loader_b(
+      B, params->ldb, Bs, simd_group_id, simd_lane_id);
 
   ///////////////////////////////////////////////////////////////////////////////
   // MNK aligned loop
@@ -160,10 +181,11 @@ template <typename T,
     for (int k = 0; k < gemm_k_iterations; k++) {
       threadgroup_barrier(mem_flags::mem_threadgroup);
 
-        if(!has_operand_mask || 
+      if (!has_operand_mask ||
-            (lhs_mask[tid_y * mask_strides[3] + ((k * BK) / BM) * mask_strides[2]] && 
+          (lhs_mask
-             rhs_mask[((k * BK) / BM) * mask_strides[5] + tid_x * mask_strides[4]])) {
+               [tid_y * mask_strides[3] + ((k * BK) / BM) * mask_strides[2]] &&
-
+           rhs_mask
+               [((k * BK) / BM) * mask_strides[5] + tid_x * mask_strides[4]])) {
         // Load elements into threadgroup
         loader_a.load_unsafe();
         loader_b.load_unsafe();
@@ -172,7 +194,6 @@ template <typename T,
 
         // Multiply and accumulate threadgroup elements
         mma_op.mma(As, Bs);
-
       }
 
       // Prepare for next iteration
@@ -184,11 +205,12 @@ template <typename T,
 
     // Loop tail
     if (!K_aligned) {
-
+      if (!has_operand_mask ||
-        if(!has_operand_mask || 
+          (lhs_mask
-            (lhs_mask[tid_y * mask_strides[3] + (params->K / BM) * mask_strides[2]] && 
+               [tid_y * mask_strides[3] + (params->K / BM) * mask_strides[2]] &&
-             rhs_mask[(params->K / BM) * mask_strides[5] + tid_x * mask_strides[4]])) {
+           rhs_mask
-
+               [(params->K / BM) * mask_strides[5] +
+                tid_x * mask_strides[4]])) {
         int lbk = params->K - params->gemm_k_iterations_aligned * BK;
         short2 tile_dims_A = transpose_a ? short2(BM, lbk) : short2(lbk, BM);
         short2 tile_dims_B = transpose_b ? short2(lbk, BN) : short2(BN, lbk);
@@ -199,7 +221,6 @@ template <typename T,
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
         mma_op.mma(As, Bs);
-
       }
     }
 
@@ -223,10 +244,11 @@ template <typename T,
 
     for (int k = 0; k < gemm_k_iterations; k++) {
       threadgroup_barrier(mem_flags::mem_threadgroup);
-        if(!has_operand_mask || 
+      if (!has_operand_mask ||
-            (lhs_mask[tid_y * mask_strides[3] + ((k * BK) / BM) * mask_strides[2]] && 
+          (lhs_mask
-             rhs_mask[((k * BK) / BM) * mask_strides[5] + tid_x * mask_strides[4]])) {
+               [tid_y * mask_strides[3] + ((k * BK) / BM) * mask_strides[2]] &&
-
+           rhs_mask
+               [((k * BK) / BM) * mask_strides[5] + tid_x * mask_strides[4]])) {
         // Load elements into threadgroup
         if (M_aligned) {
           loader_a.load_unsafe();
@@ -244,7 +266,6 @@ template <typename T,
 
         // Multiply and accumulate threadgroup elements
         mma_op.mma(As, Bs);
-
       }
 
       // Prepare for next iteration
@@ -255,10 +276,12 @@ template <typename T,
     if (!K_aligned) {
       threadgroup_barrier(mem_flags::mem_threadgroup);
 
-        if(!has_operand_mask || 
+      if (!has_operand_mask ||
-            (lhs_mask[tid_y * mask_strides[3] + (params->K / BM) * mask_strides[2]] && 
+          (lhs_mask
-             rhs_mask[(params->K / BM) * mask_strides[5] + tid_x * mask_strides[4]])) {
+               [tid_y * mask_strides[3] + (params->K / BM) * mask_strides[2]] &&
-
+           rhs_mask
+               [(params->K / BM) * mask_strides[5] +
+                tid_x * mask_strides[4]])) {
         short2 tile_dims_A_last =
             transpose_a ? short2(tgp_bm, lbk) : short2(lbk, tgp_bm);
         short2 tile_dims_B_last =
@@ -270,11 +293,10 @@ template <typename T,
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
         mma_op.mma(As, Bs);
-
       }
     }
 
-      if(M_aligned && N_aligned) {
+    if (M_aligned && N_aligned) {
       mma_op.store_result(D, params->ldd);
     } else {
       mma_op.store_result_safe(D, params->ldd, short2(tgp_bn, tgp_bm));
@@ -286,44 +308,81 @@ template <typename T,
 // GEMM kernel initializations
 ///////////////////////////////////////////////////////////////////////////////
 
-#define instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned, omname, op_mask) \
+#define instantiate_gemm(                                                      \
-  template [[host_name("steel_block_masked_gemm_" #tname "_"  #iname "_" #oname "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn "_MN_" #aname "_K_" #kname "_op_mask_" #omname)]] \
+    tname,                                                                     \
-  [[kernel]] void block_masked_gemm<itype, bm, bn, bk, wm, wn, trans_a, trans_b, mn_aligned, k_aligned, op_mask>( \
+    trans_a,                                                                   \
-      const device itype *A [[buffer(0)]], \
+    trans_b,                                                                   \
-      const device itype *B [[buffer(1)]], \
+    iname,                                                                     \
-      device itype *D [[buffer(3)]], \
+    itype,                                                                     \
+    oname,                                                                     \
+    otype,                                                                     \
+    bm,                                                                        \
+    bn,                                                                        \
+    bk,                                                                        \
+    wm,                                                                        \
+    wn,                                                                        \
+    aname,                                                                     \
+    mn_aligned,                                                                \
+    kname,                                                                     \
+    k_aligned,                                                                 \
+    omname,                                                                    \
+    op_mask)                                                                   \
+  template [[host_name("steel_block_masked_gemm_" #tname "_" #iname "_" #oname \
+                       "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn       \
+                       "_MN_" #aname "_K_" #kname                              \
+                       "_op_mask_" #omname)]] [[kernel]] void                  \
+  block_masked_gemm<                                                           \
+      itype,                                                                   \
+      bm,                                                                      \
+      bn,                                                                      \
+      bk,                                                                      \
+      wm,                                                                      \
+      wn,                                                                      \
+      trans_a,                                                                 \
+      trans_b,                                                                 \
+      mn_aligned,                                                              \
+      k_aligned,                                                               \
+      op_mask>(                                                                \
+      const device itype* A [[buffer(0)]],                                     \
+      const device itype* B [[buffer(1)]],                                     \
+      device itype* D [[buffer(3)]],                                           \
       const constant GEMMParams* params [[buffer(4)]],                         \
       const constant int* batch_shape [[buffer(6)]],                           \
       const constant size_t* batch_strides [[buffer(7)]],                      \
-      const device bool *out_mask [[buffer(10)]], \
+      const device bool* out_mask [[buffer(10)]],                              \
-      const device bool *lhs_mask [[buffer(11)]], \
+      const device bool* lhs_mask [[buffer(11)]],                              \
-      const device bool *rhs_mask [[buffer(12)]], \
+      const device bool* rhs_mask [[buffer(12)]],                              \
       const constant int* mask_strides [[buffer(13)]],                         \
       uint simd_lane_id [[thread_index_in_simdgroup]],                         \
       uint simd_group_id [[simdgroup_index_in_threadgroup]],                   \
       uint3 tid [[threadgroup_position_in_grid]],                              \
       uint3 lid [[thread_position_in_threadgroup]]);
 
+// clang-format off
 #define instantiate_gemm_mask_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned) \
   instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned, N, false)         \
-  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned, T, true)
+  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned, T, true) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_aligned_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn)                         \
   instantiate_gemm_mask_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, taligned, true)  \
   instantiate_gemm_mask_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, naligned, false) \
   instantiate_gemm_mask_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, taligned, true) \
-  instantiate_gemm_mask_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, naligned, false)
+  instantiate_gemm_mask_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, naligned, false) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_transpose_helper(iname, itype, oname, otype, bm, bn, bk, wm, wn)             \
     instantiate_gemm_aligned_helper(nn, false, false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
     instantiate_gemm_aligned_helper(nt, false, true , iname, itype, oname, otype, bm, bn, bk, wm, wn) \
     instantiate_gemm_aligned_helper(tn, true , false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
-    instantiate_gemm_aligned_helper(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn)
+    instantiate_gemm_aligned_helper(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_shapes_helper(iname, itype, oname, otype)                  \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 32, 16, 2, 2) \
-    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 64, 16, 2, 2)
+    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 64, 64, 16, 2, 2) // clang-format on
 
+// clang-format off
 instantiate_gemm_shapes_helper(float16, half, float16, half);
 instantiate_gemm_shapes_helper(bfloat16, bfloat16_t, bfloat16, bfloat16_t);
-instantiate_gemm_shapes_helper(float32, float, float32, float);
+instantiate_gemm_shapes_helper(float32, float, float32, float); // clang-format on

mlx/backend/metal/kernels/steel/gemm/kernels/steel_gemm_splitk.metal

@@ -10,7 +10,8 @@ using namespace mlx::steel;
 // GEMM kernels
 ///////////////////////////////////////////////////////////////////////////////
 
-template <typename T,
+template <
+    typename T,
     typename U,
     int BM,
     int BN,
@@ -21,19 +22,29 @@ template <typename T,
     bool transpose_b,
     bool MN_aligned,
     bool K_aligned>
-[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void gemm_splitk(
+[[kernel, max_total_threads_per_threadgroup(WM* WN * 32)]] void gemm_splitk(
-    const device T *A [[buffer(0)]],
+    const device T* A [[buffer(0)]],
-    const device T *B [[buffer(1)]],
+    const device T* B [[buffer(1)]],
-    device U *C [[buffer(2)]],
+    device U* C [[buffer(2)]],
     const constant GEMMSpiltKParams* params [[buffer(3)]],
     uint simd_lane_id [[thread_index_in_simdgroup]],
     uint simd_group_id [[simdgroup_index_in_threadgroup]],
     uint3 tid [[threadgroup_position_in_grid]],
     uint3 lid [[thread_position_in_threadgroup]]) {
-
   (void)lid;
 
-    using gemm_kernel = GEMMKernel<T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, MN_aligned, K_aligned>;
+  using gemm_kernel = GEMMKernel<
+      T,
+      U,
+      BM,
+      BN,
+      BK,
+      WM,
+      WN,
+      transpose_a,
+      transpose_b,
+      MN_aligned,
+      K_aligned>;
   using loader_a_t = typename gemm_kernel::loader_a_t;
   using loader_b_t = typename gemm_kernel::loader_b_t;
   using mma_t = typename gemm_kernel::mma_t;
@@ -54,9 +65,12 @@ template <typename T,
   const int c_col = tid_x * BN;
   const int k_start = params->split_k_partition_size * tid_z;
 
-    A += transpose_a ? (c_row + k_start * params->lda) : (k_start + c_row * params->lda);
+  A += transpose_a ? (c_row + k_start * params->lda)
-    B += transpose_b ? (k_start + c_col * params->ldb) : (c_col + k_start * params->ldb);
+                   : (k_start + c_row * params->lda);
-    C += (params->split_k_partition_stride * tid_z) + (c_row * params->ldc + c_col);
+  B += transpose_b ? (k_start + c_col * params->ldb)
+                   : (c_col + k_start * params->ldb);
+  C += (params->split_k_partition_stride * tid_z) +
+      (c_row * params->ldc + c_col);
 
   // Prepare threadgroup loading operations
   thread loader_a_t loader_a(A, params->lda, As, simd_group_id, simd_lane_id);
@@ -71,7 +85,7 @@ template <typename T,
   short tgp_bn = min(BN, params->N - c_col);
   short leftover_bk = params->K % BK;
 
-    if(MN_aligned || (tgp_bm == BM && tgp_bn == BN)) {
+  if (MN_aligned || (tgp_bm == BM && tgp_bn == BN)) {
     gemm_kernel::gemm_loop(
         As,
         Bs,
@@ -124,8 +138,9 @@ template <typename T,
   threadgroup_barrier(mem_flags::mem_threadgroup);
 
   if ((tid_z + 1) == (params->split_k_partitions)) {
-      int gemm_k_iter_remaining = (params->K - (k_start + params->split_k_partition_size)) / BK;
+    int gemm_k_iter_remaining =
-      if(!K_aligned || gemm_k_iter_remaining > 0)
+        (params->K - (k_start + params->split_k_partition_size)) / BK;
+    if (!K_aligned || gemm_k_iter_remaining > 0)
       gemm_kernel::gemm_loop(
           As,
           Bs,
@@ -139,7 +154,7 @@ template <typename T,
           LoopAlignment<false, false, K_aligned>{});
   }
 
-    if(MN_aligned || (tgp_bm == BM && tgp_bn == BN)) {
+  if (MN_aligned || (tgp_bm == BM && tgp_bn == BN)) {
     mma_op.store_result(C, params->ldc);
   } else {
     mma_op.store_result_safe(C, params->ldc, short2(tgp_bn, tgp_bm));
@@ -150,56 +165,89 @@ template <typename T,
 // GEMM kernel initializations
 ///////////////////////////////////////////////////////////////////////////////
 
-#define instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, aname, mn_aligned, kname, k_aligned) \
+#define instantiate_gemm(                                                \
-  template [[host_name("steel_gemm_splitk_" #tname "_"  #iname "_" #oname "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn "_MN_" #aname "_K_" #kname)]] \
+    tname,                                                               \
-  [[kernel]] void gemm_splitk<itype, otype, bm, bn, bk, wm, wn, trans_a, trans_b, mn_aligned, k_aligned>( \
+    trans_a,                                                             \
-      const device itype *A [[buffer(0)]], \
+    trans_b,                                                             \
-      const device itype *B [[buffer(1)]], \
+    iname,                                                               \
-      device otype *C [[buffer(2)]], \
+    itype,                                                               \
+    oname,                                                               \
+    otype,                                                               \
+    bm,                                                                  \
+    bn,                                                                  \
+    bk,                                                                  \
+    wm,                                                                  \
+    wn,                                                                  \
+    aname,                                                               \
+    mn_aligned,                                                          \
+    kname,                                                               \
+    k_aligned)                                                           \
+  template [[host_name("steel_gemm_splitk_" #tname "_" #iname "_" #oname \
+                       "_bm" #bm "_bn" #bn "_bk" #bk "_wm" #wm "_wn" #wn \
+                       "_MN_" #aname "_K_" #kname)]] [[kernel]] void     \
+  gemm_splitk<                                                           \
+      itype,                                                             \
+      otype,                                                             \
+      bm,                                                                \
+      bn,                                                                \
+      bk,                                                                \
+      wm,                                                                \
+      wn,                                                                \
+      trans_a,                                                           \
+      trans_b,                                                           \
+      mn_aligned,                                                        \
+      k_aligned>(                                                        \
+      const device itype* A [[buffer(0)]],                               \
+      const device itype* B [[buffer(1)]],                               \
+      device otype* C [[buffer(2)]],                                     \
       const constant GEMMSpiltKParams* params [[buffer(3)]],             \
       uint simd_lane_id [[thread_index_in_simdgroup]],                   \
       uint simd_group_id [[simdgroup_index_in_threadgroup]],             \
       uint3 tid [[threadgroup_position_in_grid]],                        \
       uint3 lid [[thread_position_in_threadgroup]]);
 
+// clang-format off
 #define instantiate_gemm_aligned_helper(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn)             \
   instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, taligned, true)  \
   instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, taligned, true, naligned, false) \
   instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, taligned, true) \
-  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, naligned, false)
+  instantiate_gemm(tname, trans_a, trans_b, iname, itype, oname, otype, bm, bn, bk, wm, wn, naligned, false, naligned, false) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_transpose_helper(iname, itype, oname, otype, bm, bn, bk, wm, wn)             \
     instantiate_gemm_aligned_helper(nn, false, false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
     instantiate_gemm_aligned_helper(nt, false, true , iname, itype, oname, otype, bm, bn, bk, wm, wn) \
     instantiate_gemm_aligned_helper(tn, true , false, iname, itype, oname, otype, bm, bn, bk, wm, wn) \
-    instantiate_gemm_aligned_helper(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn)
+    instantiate_gemm_aligned_helper(tt, true , true , iname, itype, oname, otype, bm, bn, bk, wm, wn) // clang-format on
 
+// clang-format off
 #define instantiate_gemm_shapes_helper(iname, itype, oname, otype)                  \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 16, 16, 16, 2, 2) \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 16, 32, 16, 2, 2) \
     instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 16, 16, 2, 2) \
-    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 32, 16, 2, 2)
+    instantiate_gemm_transpose_helper(iname, itype, oname, otype, 32, 32, 16, 2, 2) // clang-format on
 
+// clang-format off
 instantiate_gemm_shapes_helper(float16, half, float32, float);
 instantiate_gemm_shapes_helper(bfloat16, bfloat16_t, float32, float);
 
-instantiate_gemm_shapes_helper(float32, float, float32, float);
+instantiate_gemm_shapes_helper(float32, float, float32, float); // clang-format on
 
 ///////////////////////////////////////////////////////////////////////////////
 // Split k accumulation kernel
 ///////////////////////////////////////////////////////////////////////////////
 
-template <typename AccT,
+template <
+    typename AccT,
     typename OutT,
     typename Epilogue = TransformNone<OutT, AccT>>
 [[kernel]] void gemm_splitk_accum(
-    const device AccT *C_split [[buffer(0)]],
+    const device AccT* C_split [[buffer(0)]],
-    device OutT *D [[buffer(1)]],
+    device OutT* D [[buffer(1)]],
     const constant int& k_partitions [[buffer(2)]],
     const constant int& partition_stride [[buffer(3)]],
     const constant int& ldd [[buffer(4)]],
     uint2 gid [[thread_position_in_grid]]) {
-
   // Ajust D and C
   D += gid.x + gid.y * ldd;
   C_split += gid.x + gid.y * ldd;
@@ -207,32 +255,31 @@ template <typename AccT,
   int offset = 0;
   AccT out = 0;
 
-  for(int i = 0; i < k_partitions; i++) {
+  for (int i = 0; i < k_partitions; i++) {
     out += C_split[offset];
     offset += partition_stride;
   }
 
   // Write output
   D[0] = Epilogue::apply(out);
-
 }
 
-template <typename AccT,
+template <
+    typename AccT,
     typename OutT,
     typename Epilogue = TransformAxpby<OutT, AccT>>
 [[kernel]] void gemm_splitk_accum_axpby(
-    const device AccT *C_split [[buffer(0)]],
+    const device AccT* C_split [[buffer(0)]],
-    device OutT *D [[buffer(1)]],
+    device OutT* D [[buffer(1)]],
     const constant int& k_partitions [[buffer(2)]],
     const constant int& partition_stride [[buffer(3)]],
     const constant int& ldd [[buffer(4)]],
-    const device OutT *C [[buffer(5)]],
+    const device OutT* C [[buffer(5)]],
     const constant int& ldc [[buffer(6)]],
     const constant int& fdc [[buffer(7)]],
     const constant float& alpha [[buffer(8)]],
     const constant float& beta [[buffer(9)]],
     uint2 gid [[thread_position_in_grid]]) {
-
   // Ajust D and C
   C += gid.x * fdc + gid.y * ldc;
   D += gid.x + gid.y * ldd;
@@ -241,7 +288,7 @@ template <typename AccT,
   int offset = 0;
   AccT out = 0;
 
-  for(int i = 0; i < k_partitions; i++) {
+  for (int i = 0; i < k_partitions; i++) {
     out += C_split[offset];
     offset += partition_stride;
   }
@@ -249,32 +296,34 @@ template <typename AccT,
   // Write output
   Epilogue op(alpha, beta);
   D[0] = op.apply(out, *C);
-
 }
 
 #define instantiate_accum(oname, otype, aname, atype)               \
-  template [[host_name("steel_gemm_splitk_accum_" #oname "_"  #aname)]] \
+  template [[host_name("steel_gemm_splitk_accum_" #oname            \
-  [[kernel]] void gemm_splitk_accum<atype, otype>(                                    \
+                       "_" #aname)]] [[kernel]] void                \
-      const device atype *C_split [[buffer(0)]],                         \
+  gemm_splitk_accum<atype, otype>(                                  \
-      device otype *D [[buffer(1)]],                                     \
+      const device atype* C_split [[buffer(0)]],                    \
+      device otype* D [[buffer(1)]],                                \
       const constant int& k_partitions [[buffer(2)]],               \
       const constant int& partition_stride [[buffer(3)]],           \
       const constant int& ldd [[buffer(4)]],                        \
       uint2 gid [[thread_position_in_grid]]);                       \
-  template [[host_name("steel_gemm_splitk_accum_" #oname "_"  #aname "_axpby")]] \
+  template [[host_name("steel_gemm_splitk_accum_" #oname "_" #aname \
-  [[kernel]] void gemm_splitk_accum_axpby<atype, otype>( \
+                       "_axpby")]] [[kernel]] void                  \
-      const device atype *C_split [[buffer(0)]], \
+  gemm_splitk_accum_axpby<atype, otype>(                            \
-      device otype *D [[buffer(1)]], \
+      const device atype* C_split [[buffer(0)]],                    \
+      device otype* D [[buffer(1)]],                                \
       const constant int& k_partitions [[buffer(2)]],               \
       const constant int& partition_stride [[buffer(3)]],           \
       const constant int& ldd [[buffer(4)]],                        \
-      const device otype *C [[buffer(5)]],  \
+      const device otype* C [[buffer(5)]],                          \
       const constant int& ldc [[buffer(6)]],                        \
       const constant int& fdc [[buffer(7)]],                        \
       const constant float& alpha [[buffer(8)]],                    \
       const constant float& beta [[buffer(9)]],                     \
       uint2 gid [[thread_position_in_grid]]);
 
+// clang-format off
 instantiate_accum(bfloat16, bfloat16_t, float32, float);
 instantiate_accum(float16, half, float32, float);
-instantiate_accum(float32, float, float32, float);
+instantiate_accum(float32, float, float32, float); // clang-format on

mlx/backend/metal/kernels/ternary.metal

@@ -3,9 +3,9 @@
 #include <metal_integer>
 #include <metal_math>
 
-#include "mlx/backend/metal/kernels/utils.h"
 #include "mlx/backend/metal/kernels/bf16.h"
 #include "mlx/backend/metal/kernels/ternary.h"
+#include "mlx/backend/metal/kernels/utils.h"
 
 template <typename T, typename Op>
 [[kernel]] void ternary_op_v(
@@ -65,7 +65,8 @@ template <typename T, typename Op>
   auto a_idx = elem_to_loc_3(index, a_strides);
   auto b_idx = elem_to_loc_3(index, b_strides);
   auto c_idx = elem_to_loc_3(index, c_strides);
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   d[out_idx] = Op()(a[a_idx], b[b_idx], c[c_idx]);
 }
 
@@ -81,8 +82,10 @@ template <typename T, typename Op, int DIM>
     constant const size_t c_strides[DIM],
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  auto idx = elem_to_loc_3_nd<DIM>(index, shape, a_strides, b_strides, c_strides);
+  auto idx =
-  size_t out_idx = index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
+      elem_to_loc_3_nd<DIM>(index, shape, a_strides, b_strides, c_strides);
+  size_t out_idx =
+      index.x + (size_t)grid_dim.x * (index.y + (size_t)grid_dim.y * index.z);
   d[out_idx] = Op()(a[idx.x], b[idx.y], c[idx.z]);
 }
 
@@ -99,23 +102,22 @@ template <typename T, typename Op>
     constant const int& ndim,
     uint3 index [[thread_position_in_grid]],
     uint3 grid_dim [[threads_per_grid]]) {
-  auto idx = elem_to_loc_3_nd(index, shape, a_strides, b_strides, c_strides, ndim);
+  auto idx =
+      elem_to_loc_3_nd(index, shape, a_strides, b_strides, c_strides, ndim);
   size_t out_idx = index.x + grid_dim.x * (index.y + grid_dim.y * index.z);
   d[out_idx] = Op()(a[idx.x], b[idx.y], c[idx.z]);
 }
 
 #define instantiate_ternary_v(name, type, op)                          \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void ternary_op_v<type, op>( \
-  [[kernel]] void ternary_op_v<type, op>( \
       device const bool* a,                                            \
       device const type* b,                                            \
       device const type* c,                                            \
       device type* d,                                                  \
-      uint index [[thread_position_in_grid]]); \
+      uint index [[thread_position_in_grid]]);
 
 #define instantiate_ternary_g(name, type, op)                          \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void ternary_op_g<type, op>( \
-  [[kernel]] void ternary_op_g<type, op>( \
       device const bool* a,                                            \
       device const type* b,                                            \
       device const type* c,                                            \
@@ -126,11 +128,11 @@ template <typename T, typename Op>
       constant const size_t* c_strides,                                \
       constant const int& ndim,                                        \
       uint3 index [[thread_position_in_grid]],                         \
-      uint3 grid_dim [[threads_per_grid]]); \
+      uint3 grid_dim [[threads_per_grid]]);
 
 #define instantiate_ternary_g_dim(name, type, op, dims)  \
-  template [[host_name(name "_" #dims)]] \
+  template [[host_name(name "_" #dims)]] [[kernel]] void \
-  [[kernel]] void ternary_op_g_nd<type, op, dims>( \
+  ternary_op_g_nd<type, op, dims>(                       \
       device const bool* a,                              \
       device const type* b,                              \
       device const type* c,                              \
@@ -140,11 +142,11 @@ template <typename T, typename Op>
       constant const size_t b_strides[dims],             \
       constant const size_t c_strides[dims],             \
       uint3 index [[thread_position_in_grid]],           \
-      uint3 grid_dim [[threads_per_grid]]); \
+      uint3 grid_dim [[threads_per_grid]]);
 
 #define instantiate_ternary_g_nd(name, type, op)    \
-  template [[host_name(name "_1")]] \
+  template [[host_name(name "_1")]] [[kernel]] void \
-  [[kernel]] void ternary_op_g_nd1<type, op>( \
+  ternary_op_g_nd1<type, op>(                       \
       device const bool* a,                         \
       device const type* b,                         \
       device const type* c,                         \
@@ -153,8 +155,8 @@ template <typename T, typename Op>
       constant const size_t& b_strides,             \
       constant const size_t& c_strides,             \
       uint index [[thread_position_in_grid]]);      \
-  template [[host_name(name "_2")]] \
+  template [[host_name(name "_2")]] [[kernel]] void \
-  [[kernel]] void ternary_op_g_nd2<type, op>( \
+  ternary_op_g_nd2<type, op>(                       \
       device const bool* a,                         \
       device const type* b,                         \
       device const type* c,                         \
@@ -164,8 +166,8 @@ template <typename T, typename Op>
       constant const size_t c_strides[2],           \
       uint2 index [[thread_position_in_grid]],      \
       uint2 grid_dim [[threads_per_grid]]);         \
-  template [[host_name(name "_3")]] \
+  template [[host_name(name "_3")]] [[kernel]] void \
-  [[kernel]] void ternary_op_g_nd3<type, op>( \
+  ternary_op_g_nd3<type, op>(                       \
       device const bool* a,                         \
       device const type* b,                         \
       device const type* c,                         \
@@ -176,13 +178,15 @@ template <typename T, typename Op>
       uint3 index [[thread_position_in_grid]],      \
       uint3 grid_dim [[threads_per_grid]]);         \
   instantiate_ternary_g_dim(name, type, op, 4)      \
-  instantiate_ternary_g_dim(name, type, op, 5) \
+      instantiate_ternary_g_dim(name, type, op, 5)
 
+// clang-format off
 #define instantiate_ternary_all(name, tname, type, op) \
   instantiate_ternary_v("v" #name #tname, type, op)    \
   instantiate_ternary_g("g" #name #tname, type, op)    \
-  instantiate_ternary_g_nd("g" #name #tname, type, op) \
+  instantiate_ternary_g_nd("g" #name #tname, type, op) // clang-format on
 
+// clang-format off
 #define instantiate_ternary_types(name, op)               \
   instantiate_ternary_all(name, bool_, bool, op)          \
   instantiate_ternary_all(name, uint8, uint8_t, op)       \
@@ -196,6 +200,6 @@ template <typename T, typename Op>
   instantiate_ternary_all(name, float16, half, op)        \
   instantiate_ternary_all(name, float32, float, op)       \
   instantiate_ternary_all(name, bfloat16, bfloat16_t, op) \
-  instantiate_ternary_all(name, complex64, complex64_t, op) \
+  instantiate_ternary_all(name, complex64, complex64_t, op) // clang-format on
 
 instantiate_ternary_types(select, Select)

mlx/backend/metal/kernels/unary.metal

@@ -23,15 +23,13 @@ template <typename T, typename Op>
 }
 
 #define instantiate_unary_v(name, type, op)                          \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void unary_op_v<type, op>( \
-  [[kernel]] void unary_op_v<type, op>( \
       device const type* in,                                         \
       device type* out,                                              \
       uint index [[thread_position_in_grid]]);
 
 #define instantiate_unary_g(name, type, op)                          \
-  template [[host_name(name)]] \
+  template [[host_name(name)]] [[kernel]] void unary_op_g<type, op>( \
-  [[kernel]] void unary_op_g<type, op>( \
       device const type* in,                                         \
       device type* out,                                              \
       device const int* in_shape,                                    \
@@ -39,15 +37,18 @@ template <typename T, typename Op>
       device const int& ndim,                                        \
       uint index [[thread_position_in_grid]]);
 
+// clang-format off
 #define instantiate_unary_all(name, tname, type, op) \
   instantiate_unary_v("v" #name #tname, type, op)    \
-  instantiate_unary_g("g" #name #tname, type, op)
+  instantiate_unary_g("g" #name #tname, type, op) // clang-format on
 
+// clang-format off
 #define instantiate_unary_float(name, op)               \
   instantiate_unary_all(name, float16, half, op)        \
   instantiate_unary_all(name, float32, float, op)       \
-  instantiate_unary_all(name, bfloat16, bfloat16_t, op) \
+  instantiate_unary_all(name, bfloat16, bfloat16_t, op) // clang-format on
 
+// clang-format off
 #define instantiate_unary_types(name, op)           \
   instantiate_unary_all(name, bool_, bool, op)      \
   instantiate_unary_all(name, uint8, uint8_t, op)   \
@@ -58,8 +59,9 @@ template <typename T, typename Op>
   instantiate_unary_all(name, int16, int16_t, op)   \
   instantiate_unary_all(name, int32, int32_t, op)   \
   instantiate_unary_all(name, int64, int64_t, op)   \
-  instantiate_unary_float(name, op)
+  instantiate_unary_float(name, op) // clang-format on
 
+// clang-format off
 instantiate_unary_types(abs, Abs)
 instantiate_unary_float(arccos, ArcCos)
 instantiate_unary_float(arccosh, ArcCosh)
@@ -102,4 +104,4 @@ instantiate_unary_all(tan, complex64, complex64_t, Tan)
 instantiate_unary_all(tanh, complex64, complex64_t, Tanh)
 instantiate_unary_all(round, complex64, complex64_t, Round)
 
-instantiate_unary_all(lnot, bool_, bool, LogicalNot)
+instantiate_unary_all(lnot, bool_, bool, LogicalNot) // clang-format on

mlx/device.h

@@ -13,7 +13,7 @@ struct Device {
   static constexpr DeviceType cpu = DeviceType::cpu;
   static constexpr DeviceType gpu = DeviceType::gpu;
 
-  Device(DeviceType type, int index = 0) : type(type), index(index){};
+  Device(DeviceType type, int index = 0) : type(type), index(index) {};
 
   DeviceType type;
   int index;

mlx/dtype.h

@@ -51,7 +51,7 @@ struct Dtype {
 
   Val val;
   const uint8_t size;
-  constexpr explicit Dtype(Val val, uint8_t size) : val(val), size(size){};
+  constexpr explicit Dtype(Val val, uint8_t size) : val(val), size(size) {};
   constexpr operator Val() const {
     return val;
   };

mlx/event.h

@@ -10,7 +10,7 @@ namespace mlx::core {
 
 class Event {
  public:
-  Event(){};
+  Event() {};
 
   Event(const Stream& steam);
 

mlx/fast_primitives.h

@@ -12,7 +12,7 @@ class Custom : public Primitive {
   explicit Custom(
       Stream stream,
       std::function<std::vector<array>(std::vector<array>)> fallback)
-      : Primitive(stream), fallback_(fallback){};
+      : Primitive(stream), fallback_(fallback) {};
 
   virtual std::pair<std::vector<array>, std::vector<int>> vmap(
       const std::vector<array>& inputs,
@@ -39,7 +39,7 @@ class RMSNorm : public Custom {
       Stream stream,
       std::function<std::vector<array>(std::vector<array>)> fallback,
       float eps)
-      : Custom(stream, fallback), eps_(eps){};
+      : Custom(stream, fallback), eps_(eps) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override {
@@ -68,7 +68,7 @@ class RMSNormVJP : public Custom {
       Stream stream,
       std::function<std::vector<array>(std::vector<array>)> fallback,
       float eps)
-      : Custom(stream, fallback), eps_(eps){};
+      : Custom(stream, fallback), eps_(eps) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override {
@@ -91,7 +91,7 @@ class LayerNorm : public Custom {
       Stream stream,
       std::function<std::vector<array>(std::vector<array>)> fallback,
       float eps)
-      : Custom(stream, fallback), eps_(eps){};
+      : Custom(stream, fallback), eps_(eps) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override {
@@ -120,7 +120,7 @@ class LayerNormVJP : public Custom {
       Stream stream,
       std::function<std::vector<array>(std::vector<array>)> fallback,
       float eps)
-      : Custom(stream, fallback), eps_(eps){};
+      : Custom(stream, fallback), eps_(eps) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override {
@@ -154,7 +154,7 @@ class RoPE : public Custom {
         base_(base),
         scale_(scale),
         offset_(offset),
-        forward_(forward){};
+        forward_(forward) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override {
@@ -189,7 +189,7 @@ class ScaledDotProductAttention : public Custom {
       std::function<std::vector<array>(std::vector<array>)> fallback,
       const float scale,
       const bool needs_mask)
-      : Custom(stream, fallback), scale_(scale), needs_mask_(needs_mask){};
+      : Custom(stream, fallback), scale_(scale), needs_mask_(needs_mask) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override {

mlx/primitives.h

@@ -154,7 +154,7 @@ class UnaryPrimitive : public Primitive {
 
 class Abs : public UnaryPrimitive {
  public:
-  explicit Abs(Stream stream) : UnaryPrimitive(stream){};
+  explicit Abs(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -171,7 +171,7 @@ class Abs : public UnaryPrimitive {
 
 class Add : public UnaryPrimitive {
  public:
-  explicit Add(Stream stream) : UnaryPrimitive(stream){};
+  explicit Add(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -189,7 +189,7 @@ class Add : public UnaryPrimitive {
 class AddMM : public UnaryPrimitive {
  public:
   explicit AddMM(Stream stream, float alpha, float beta)
-      : UnaryPrimitive(stream), alpha_(alpha), beta_(beta){};
+      : UnaryPrimitive(stream), alpha_(alpha), beta_(beta) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -213,7 +213,7 @@ class AddMM : public UnaryPrimitive {
 class Arange : public UnaryPrimitive {
  public:
   explicit Arange(Stream stream, double start, double stop, double step)
-      : UnaryPrimitive(stream), start_(start), stop_(stop), step_(step){};
+      : UnaryPrimitive(stream), start_(start), stop_(stop), step_(step) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -231,7 +231,7 @@ class Arange : public UnaryPrimitive {
 
 class ArcCos : public UnaryPrimitive {
  public:
-  explicit ArcCos(Stream stream) : UnaryPrimitive(stream){};
+  explicit ArcCos(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -248,7 +248,7 @@ class ArcCos : public UnaryPrimitive {
 
 class ArcCosh : public UnaryPrimitive {
  public:
-  explicit ArcCosh(Stream stream) : UnaryPrimitive(stream){};
+  explicit ArcCosh(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -265,7 +265,7 @@ class ArcCosh : public UnaryPrimitive {
 
 class ArcSin : public UnaryPrimitive {
  public:
-  explicit ArcSin(Stream stream) : UnaryPrimitive(stream){};
+  explicit ArcSin(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -282,7 +282,7 @@ class ArcSin : public UnaryPrimitive {
 
 class ArcSinh : public UnaryPrimitive {
  public:
-  explicit ArcSinh(Stream stream) : UnaryPrimitive(stream){};
+  explicit ArcSinh(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -299,7 +299,7 @@ class ArcSinh : public UnaryPrimitive {
 
 class ArcTan : public UnaryPrimitive {
  public:
-  explicit ArcTan(Stream stream) : UnaryPrimitive(stream){};
+  explicit ArcTan(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -316,7 +316,7 @@ class ArcTan : public UnaryPrimitive {
 
 class ArcTanh : public UnaryPrimitive {
  public:
-  explicit ArcTanh(Stream stream) : UnaryPrimitive(stream){};
+  explicit ArcTanh(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -334,7 +334,7 @@ class ArcTanh : public UnaryPrimitive {
 class ArgPartition : public UnaryPrimitive {
  public:
   explicit ArgPartition(Stream stream, int kth, int axis)
-      : UnaryPrimitive(stream), kth_(kth), axis_(axis){};
+      : UnaryPrimitive(stream), kth_(kth), axis_(axis) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -359,7 +359,7 @@ class ArgReduce : public UnaryPrimitive {
   };
 
   explicit ArgReduce(Stream stream, ReduceType reduce_type, int axis)
-      : UnaryPrimitive(stream), reduce_type_(reduce_type), axis_(axis){};
+      : UnaryPrimitive(stream), reduce_type_(reduce_type), axis_(axis) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -380,7 +380,7 @@ class ArgReduce : public UnaryPrimitive {
 class ArgSort : public UnaryPrimitive {
  public:
   explicit ArgSort(Stream stream, int axis)
-      : UnaryPrimitive(stream), axis_(axis){};
+      : UnaryPrimitive(stream), axis_(axis) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -399,7 +399,7 @@ class ArgSort : public UnaryPrimitive {
 class AsType : public UnaryPrimitive {
  public:
   explicit AsType(Stream stream, Dtype dtype)
-      : UnaryPrimitive(stream), dtype_(dtype){};
+      : UnaryPrimitive(stream), dtype_(dtype) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -426,7 +426,7 @@ class AsStrided : public UnaryPrimitive {
       : UnaryPrimitive(stream),
         shape_(std::move(shape)),
         strides_(std::move(strides)),
-        offset_(offset){};
+        offset_(offset) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -448,7 +448,7 @@ class BitwiseBinary : public UnaryPrimitive {
   enum Op { And, Or, Xor, LeftShift, RightShift };
 
   explicit BitwiseBinary(Stream stream, Op op)
-      : UnaryPrimitive(stream), op_(op){};
+      : UnaryPrimitive(stream), op_(op) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -465,7 +465,7 @@ class BitwiseBinary : public UnaryPrimitive {
 class BlockMaskedMM : public UnaryPrimitive {
  public:
   explicit BlockMaskedMM(Stream stream, int block_size)
-      : UnaryPrimitive(stream), block_size_(block_size){};
+      : UnaryPrimitive(stream), block_size_(block_size) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -488,7 +488,7 @@ class BlockMaskedMM : public UnaryPrimitive {
 class Broadcast : public UnaryPrimitive {
  public:
   explicit Broadcast(Stream stream, const std::vector<int>& shape)
-      : UnaryPrimitive(stream), shape_(shape){};
+      : UnaryPrimitive(stream), shape_(shape) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -506,7 +506,7 @@ class Broadcast : public UnaryPrimitive {
 
 class Ceil : public UnaryPrimitive {
  public:
-  explicit Ceil(Stream stream) : UnaryPrimitive(stream){};
+  explicit Ceil(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -567,7 +567,7 @@ class Compiled : public Primitive {
 class Concatenate : public UnaryPrimitive {
  public:
   explicit Concatenate(Stream stream, int axis)
-      : UnaryPrimitive(stream), axis_(axis){};
+      : UnaryPrimitive(stream), axis_(axis) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -599,7 +599,7 @@ class Convolution : public UnaryPrimitive {
         kernel_dilation_(kernel_dilation),
         input_dilation_(input_dilation),
         groups_(groups),
-        flip_(flip){};
+        flip_(flip) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -626,7 +626,7 @@ class Convolution : public UnaryPrimitive {
 
 class Copy : public UnaryPrimitive {
  public:
-  explicit Copy(Stream stream) : UnaryPrimitive(stream){};
+  explicit Copy(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -643,7 +643,7 @@ class Copy : public UnaryPrimitive {
 
 class Cos : public UnaryPrimitive {
  public:
-  explicit Cos(Stream stream) : UnaryPrimitive(stream){};
+  explicit Cos(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -660,7 +660,7 @@ class Cos : public UnaryPrimitive {
 
 class Cosh : public UnaryPrimitive {
  public:
-  explicit Cosh(Stream stream) : UnaryPrimitive(stream){};
+  explicit Cosh(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -731,7 +731,7 @@ class Depends : public Primitive {
 
 class Divide : public UnaryPrimitive {
  public:
-  explicit Divide(Stream stream) : UnaryPrimitive(stream){};
+  explicit Divide(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -748,7 +748,7 @@ class Divide : public UnaryPrimitive {
 
 class DivMod : public Primitive {
  public:
-  explicit DivMod(Stream stream) : Primitive(stream){};
+  explicit DivMod(Stream stream) : Primitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override;
@@ -770,7 +770,7 @@ class DivMod : public Primitive {
 
 class Select : public UnaryPrimitive {
  public:
-  explicit Select(Stream stream) : UnaryPrimitive(stream){};
+  explicit Select(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -787,7 +787,7 @@ class Select : public UnaryPrimitive {
 
 class Remainder : public UnaryPrimitive {
  public:
-  explicit Remainder(Stream stream) : UnaryPrimitive(stream){};
+  explicit Remainder(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -805,7 +805,7 @@ class Remainder : public UnaryPrimitive {
 class Equal : public UnaryPrimitive {
  public:
   explicit Equal(Stream stream, bool equal_nan = false)
-      : UnaryPrimitive(stream), equal_nan_(equal_nan){};
+      : UnaryPrimitive(stream), equal_nan_(equal_nan) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -830,7 +830,7 @@ class Equal : public UnaryPrimitive {
 
 class Erf : public UnaryPrimitive {
  public:
-  explicit Erf(Stream stream) : UnaryPrimitive(stream){};
+  explicit Erf(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -847,7 +847,7 @@ class Erf : public UnaryPrimitive {
 
 class ErfInv : public UnaryPrimitive {
  public:
-  explicit ErfInv(Stream stream) : UnaryPrimitive(stream){};
+  explicit ErfInv(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -864,7 +864,7 @@ class ErfInv : public UnaryPrimitive {
 
 class Exp : public UnaryPrimitive {
  public:
-  explicit Exp(Stream stream) : UnaryPrimitive(stream){};
+  explicit Exp(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -881,7 +881,7 @@ class Exp : public UnaryPrimitive {
 
 class Expm1 : public UnaryPrimitive {
  public:
-  explicit Expm1(Stream stream) : UnaryPrimitive(stream){};
+  explicit Expm1(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -902,7 +902,7 @@ class FFT : public UnaryPrimitive {
       const std::vector<size_t>& axes,
       bool inverse,
       bool real)
-      : UnaryPrimitive(stream), axes_(axes), inverse_(inverse), real_(real){};
+      : UnaryPrimitive(stream), axes_(axes), inverse_(inverse), real_(real) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -923,7 +923,7 @@ class FFT : public UnaryPrimitive {
 
 class Floor : public UnaryPrimitive {
  public:
-  explicit Floor(Stream stream) : UnaryPrimitive(stream){};
+  explicit Floor(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -940,7 +940,7 @@ class Floor : public UnaryPrimitive {
 
 class Full : public UnaryPrimitive {
  public:
-  explicit Full(Stream stream) : UnaryPrimitive(stream){};
+  explicit Full(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -960,7 +960,7 @@ class Gather : public UnaryPrimitive {
       Stream stream,
       const std::vector<int>& axes,
       const std::vector<int>& slice_sizes)
-      : UnaryPrimitive(stream), axes_(axes), slice_sizes_(slice_sizes){};
+      : UnaryPrimitive(stream), axes_(axes), slice_sizes_(slice_sizes) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -978,7 +978,7 @@ class Gather : public UnaryPrimitive {
 
 class Greater : public UnaryPrimitive {
  public:
-  explicit Greater(Stream stream) : UnaryPrimitive(stream){};
+  explicit Greater(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -995,7 +995,7 @@ class Greater : public UnaryPrimitive {
 
 class GreaterEqual : public UnaryPrimitive {
  public:
-  explicit GreaterEqual(Stream stream) : UnaryPrimitive(stream){};
+  explicit GreaterEqual(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1012,7 +1012,7 @@ class GreaterEqual : public UnaryPrimitive {
 
 class Less : public UnaryPrimitive {
  public:
-  explicit Less(Stream stream) : UnaryPrimitive(stream){};
+  explicit Less(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1029,7 +1029,7 @@ class Less : public UnaryPrimitive {
 
 class LessEqual : public UnaryPrimitive {
  public:
-  explicit LessEqual(Stream stream) : UnaryPrimitive(stream){};
+  explicit LessEqual(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1054,7 +1054,7 @@ class Load : public UnaryPrimitive {
       : UnaryPrimitive(stream),
         reader_(reader),
         offset_(offset),
-        swap_endianness_(swap_endianness){};
+        swap_endianness_(swap_endianness) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1073,7 +1073,7 @@ class Log : public UnaryPrimitive {
   enum Base { two, ten, e };
 
   explicit Log(Stream stream, Base base)
-      : UnaryPrimitive(stream), base_(base){};
+      : UnaryPrimitive(stream), base_(base) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1104,7 +1104,7 @@ class Log : public UnaryPrimitive {
 
 class Log1p : public UnaryPrimitive {
  public:
-  explicit Log1p(Stream stream) : UnaryPrimitive(stream){};
+  explicit Log1p(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1120,7 +1120,7 @@ class Log1p : public UnaryPrimitive {
 
 class LogicalNot : public UnaryPrimitive {
  public:
-  explicit LogicalNot(Stream stream) : UnaryPrimitive(stream){};
+  explicit LogicalNot(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1137,7 +1137,7 @@ class LogicalNot : public UnaryPrimitive {
 
 class LogicalAnd : public UnaryPrimitive {
  public:
-  explicit LogicalAnd(Stream stream) : UnaryPrimitive(stream){};
+  explicit LogicalAnd(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1154,7 +1154,7 @@ class LogicalAnd : public UnaryPrimitive {
 
 class LogicalOr : public UnaryPrimitive {
  public:
-  explicit LogicalOr(Stream stream) : UnaryPrimitive(stream){};
+  explicit LogicalOr(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1171,7 +1171,7 @@ class LogicalOr : public UnaryPrimitive {
 
 class LogAddExp : public UnaryPrimitive {
  public:
-  explicit LogAddExp(Stream stream) : UnaryPrimitive(stream){};
+  explicit LogAddExp(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1188,7 +1188,7 @@ class LogAddExp : public UnaryPrimitive {
 
 class Matmul : public UnaryPrimitive {
  public:
-  explicit Matmul(Stream stream) : UnaryPrimitive(stream){};
+  explicit Matmul(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1206,7 +1206,7 @@ class Matmul : public UnaryPrimitive {
 
 class Maximum : public UnaryPrimitive {
  public:
-  explicit Maximum(Stream stream) : UnaryPrimitive(stream){};
+  explicit Maximum(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1223,7 +1223,7 @@ class Maximum : public UnaryPrimitive {
 
 class Minimum : public UnaryPrimitive {
  public:
-  explicit Minimum(Stream stream) : UnaryPrimitive(stream){};
+  explicit Minimum(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1240,7 +1240,7 @@ class Minimum : public UnaryPrimitive {
 
 class Multiply : public UnaryPrimitive {
  public:
-  explicit Multiply(Stream stream) : UnaryPrimitive(stream){};
+  explicit Multiply(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1257,7 +1257,7 @@ class Multiply : public UnaryPrimitive {
 
 class Negative : public UnaryPrimitive {
  public:
-  explicit Negative(Stream stream) : UnaryPrimitive(stream){};
+  explicit Negative(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1274,7 +1274,7 @@ class Negative : public UnaryPrimitive {
 
 class NotEqual : public UnaryPrimitive {
  public:
-  explicit NotEqual(Stream stream) : UnaryPrimitive(stream){};
+  explicit NotEqual(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1330,7 +1330,7 @@ class Pad : public UnaryPrimitive {
       : UnaryPrimitive(stream),
         axes_(axes),
         low_pad_size_(low_pad_size),
-        high_pad_size_(high_pad_size){};
+        high_pad_size_(high_pad_size) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1351,7 +1351,7 @@ class Pad : public UnaryPrimitive {
 class Partition : public UnaryPrimitive {
  public:
   explicit Partition(Stream stream, int kth, int axis)
-      : UnaryPrimitive(stream), kth_(kth), axis_(axis){};
+      : UnaryPrimitive(stream), kth_(kth), axis_(axis) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1371,7 +1371,7 @@ class Partition : public UnaryPrimitive {
 
 class Power : public UnaryPrimitive {
  public:
-  explicit Power(Stream stream) : UnaryPrimitive(stream){};
+  explicit Power(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1396,7 +1396,7 @@ class QuantizedMatmul : public UnaryPrimitive {
       : UnaryPrimitive(stream),
         group_size_(group_size),
         bits_(bits),
-        transpose_(transpose){};
+        transpose_(transpose) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1417,7 +1417,7 @@ class QuantizedMatmul : public UnaryPrimitive {
 class RandomBits : public UnaryPrimitive {
  public:
   explicit RandomBits(Stream stream, const std::vector<int>& shape, int width)
-      : UnaryPrimitive(stream), shape_(shape), width_(width){};
+      : UnaryPrimitive(stream), shape_(shape), width_(width) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1436,7 +1436,7 @@ class RandomBits : public UnaryPrimitive {
 class Reshape : public UnaryPrimitive {
  public:
   explicit Reshape(Stream stream, const std::vector<int>& shape)
-      : UnaryPrimitive(stream), shape_(shape){};
+      : UnaryPrimitive(stream), shape_(shape) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1468,7 +1468,7 @@ class Reduce : public UnaryPrimitive {
       Stream stream,
       ReduceType reduce_type,
       const std::vector<int>& axes)
-      : UnaryPrimitive(stream), reduce_type_(reduce_type), axes_(axes){};
+      : UnaryPrimitive(stream), reduce_type_(reduce_type), axes_(axes) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1517,7 +1517,7 @@ class Reduce : public UnaryPrimitive {
 
 class Round : public UnaryPrimitive {
  public:
-  explicit Round(Stream stream) : UnaryPrimitive(stream){};
+  explicit Round(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1546,7 +1546,7 @@ class Scan : public UnaryPrimitive {
         reduce_type_(reduce_type),
         axis_(axis),
         reverse_(reverse),
-        inclusive_(inclusive){};
+        inclusive_(inclusive) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1591,7 +1591,7 @@ class Scatter : public UnaryPrimitive {
       Stream stream,
       ReduceType reduce_type,
       const std::vector<int>& axes)
-      : UnaryPrimitive(stream), reduce_type_(reduce_type), axes_(axes){};
+      : UnaryPrimitive(stream), reduce_type_(reduce_type), axes_(axes) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1626,7 +1626,7 @@ class Scatter : public UnaryPrimitive {
 
 class Sigmoid : public UnaryPrimitive {
  public:
-  explicit Sigmoid(Stream stream) : UnaryPrimitive(stream){};
+  explicit Sigmoid(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1643,7 +1643,7 @@ class Sigmoid : public UnaryPrimitive {
 
 class Sign : public UnaryPrimitive {
  public:
-  explicit Sign(Stream stream) : UnaryPrimitive(stream){};
+  explicit Sign(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1660,7 +1660,7 @@ class Sign : public UnaryPrimitive {
 
 class Sin : public UnaryPrimitive {
  public:
-  explicit Sin(Stream stream) : UnaryPrimitive(stream){};
+  explicit Sin(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1677,7 +1677,7 @@ class Sin : public UnaryPrimitive {
 
 class Sinh : public UnaryPrimitive {
  public:
-  explicit Sinh(Stream stream) : UnaryPrimitive(stream){};
+  explicit Sinh(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1702,7 +1702,7 @@ class Slice : public UnaryPrimitive {
       : UnaryPrimitive(stream),
         start_indices_(start_indices),
         end_indices_(end_indices),
-        strides_(strides){};
+        strides_(strides) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1738,7 +1738,7 @@ class SliceUpdate : public UnaryPrimitive {
       : UnaryPrimitive(stream),
         start_indices_(start_indices),
         end_indices_(end_indices),
-        strides_(strides){};
+        strides_(strides) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1761,7 +1761,7 @@ class SliceUpdate : public UnaryPrimitive {
 class Softmax : public UnaryPrimitive {
  public:
   explicit Softmax(Stream stream, bool precise)
-      : UnaryPrimitive(stream), precise_(precise){};
+      : UnaryPrimitive(stream), precise_(precise) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1781,7 +1781,7 @@ class Softmax : public UnaryPrimitive {
 class Sort : public UnaryPrimitive {
  public:
   explicit Sort(Stream stream, int axis)
-      : UnaryPrimitive(stream), axis_(axis){};
+      : UnaryPrimitive(stream), axis_(axis) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1801,7 +1801,7 @@ class Sort : public UnaryPrimitive {
 class Split : public Primitive {
  public:
   explicit Split(Stream stream, const std::vector<int>& indices, int axis)
-      : Primitive(stream), indices_(indices), axis_(axis){};
+      : Primitive(stream), indices_(indices), axis_(axis) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override;
@@ -1822,7 +1822,7 @@ class Split : public Primitive {
 
 class Square : public UnaryPrimitive {
  public:
-  explicit Square(Stream stream) : UnaryPrimitive(stream){};
+  explicit Square(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1840,7 +1840,7 @@ class Square : public UnaryPrimitive {
 class Sqrt : public UnaryPrimitive {
  public:
   explicit Sqrt(Stream stream, bool recip = false)
-      : UnaryPrimitive(stream), recip_(recip){};
+      : UnaryPrimitive(stream), recip_(recip) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1865,7 +1865,7 @@ class Sqrt : public UnaryPrimitive {
 
 class StopGradient : public UnaryPrimitive {
  public:
-  explicit StopGradient(Stream stream) : UnaryPrimitive(stream){};
+  explicit StopGradient(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1881,7 +1881,7 @@ class StopGradient : public UnaryPrimitive {
 
 class Subtract : public UnaryPrimitive {
  public:
-  explicit Subtract(Stream stream) : UnaryPrimitive(stream){};
+  explicit Subtract(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1898,7 +1898,7 @@ class Subtract : public UnaryPrimitive {
 
 class Tan : public UnaryPrimitive {
  public:
-  explicit Tan(Stream stream) : UnaryPrimitive(stream){};
+  explicit Tan(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1915,7 +1915,7 @@ class Tan : public UnaryPrimitive {
 
 class Tanh : public UnaryPrimitive {
  public:
-  explicit Tanh(Stream stream) : UnaryPrimitive(stream){};
+  explicit Tanh(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1932,7 +1932,7 @@ class Tanh : public UnaryPrimitive {
 
 class Uniform : public UnaryPrimitive {
  public:
-  explicit Uniform(Stream stream) : UnaryPrimitive(stream){};
+  explicit Uniform(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1948,7 +1948,7 @@ class Uniform : public UnaryPrimitive {
 class Transpose : public UnaryPrimitive {
  public:
   explicit Transpose(Stream stream, const std::vector<int>& axes)
-      : UnaryPrimitive(stream), axes_(axes){};
+      : UnaryPrimitive(stream), axes_(axes) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& out) override;
   void eval_gpu(const std::vector<array>& inputs, array& out) override;
@@ -1967,7 +1967,7 @@ class Transpose : public UnaryPrimitive {
 /* QR Factorization primitive. */
 class QRF : public Primitive {
  public:
-  explicit QRF(Stream stream) : Primitive(stream){};
+  explicit QRF(Stream stream) : Primitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override;
@@ -1983,7 +1983,7 @@ class QRF : public Primitive {
 /* SVD primitive. */
 class SVD : public Primitive {
  public:
-  explicit SVD(Stream stream) : Primitive(stream){};
+  explicit SVD(Stream stream) : Primitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
       override;
@@ -2000,7 +2000,7 @@ class SVD : public Primitive {
 /* Matrix inversion primitive. */
 class Inverse : public UnaryPrimitive {
  public:
-  explicit Inverse(Stream stream) : UnaryPrimitive(stream){};
+  explicit Inverse(Stream stream) : UnaryPrimitive(stream) {};
 
   void eval_cpu(const std::vector<array>& inputs, array& output) override;
   void eval_gpu(const std::vector<array>& inputs, array& output) override;

mlx/transforms.cpp

@@ -22,7 +22,7 @@ namespace mlx::core {
  * for synchronizing with the main thread. */
 class Synchronizer : public Primitive {
  public:
-  explicit Synchronizer(Stream stream) : Primitive(stream){};
+  explicit Synchronizer(Stream stream) : Primitive(stream) {};
 
   void eval_cpu(const std::vector<array>&, std::vector<array>&) override {};
   void eval_gpu(const std::vector<array>&, std::vector<array>&) override {};

mlx/types/complex.h

@@ -14,8 +14,8 @@ inline constexpr bool can_convert_to_complex128 =
     !std::is_same_v<T, complex128_t> && std::is_convertible_v<T, double>;
 
 struct complex128_t : public std::complex<double> {
-  complex128_t(double v, double u) : std::complex<double>(v, u){};
+  complex128_t(double v, double u) : std::complex<double>(v, u) {};
-  complex128_t(std::complex<double> v) : std::complex<double>(v){};
+  complex128_t(std::complex<double> v) : std::complex<double>(v) {};
 
   template <
       typename T,
@@ -32,8 +32,8 @@ inline constexpr bool can_convert_to_complex64 =
     !std::is_same_v<T, complex64_t> && std::is_convertible_v<T, float>;
 
 struct complex64_t : public std::complex<float> {
-  complex64_t(float v, float u) : std::complex<float>(v, u){};
+  complex64_t(float v, float u) : std::complex<float>(v, u) {};
-  complex64_t(std::complex<float> v) : std::complex<float>(v){};
+  complex64_t(std::complex<float> v) : std::complex<float>(v) {};
 
   template <
       typename T,