Fix build on Xcode 14 (#116)

* Fix build on Xcode 14

* Style fixes

mlx/backend/metal/kernels/binary.metal

@@ -18,7 +18,7 @@ struct Remainder {
   template <typename T> T operator()(T x, T y) { return x % y; }
   template <> float operator()(float x, float y) { return fmod(x, y); }
   template <> half operator()(half x, half y) { return fmod(x, y); }
-  template <> bfloat operator()(bfloat x, bfloat y) { return fmod(x, y); }
+  template <> bfloat16_t operator()(bfloat16_t x, bfloat16_t y) { return fmod(x, y); }
 };
 
 struct Equal {

mlx/backend/metal/kernels/complex.h

@@ -112,5 +112,7 @@ constexpr complex64_t operator*(complex64_t a, complex64_t b) {
 }
 
 constexpr complex64_t operator%(complex64_t a, complex64_t b) {
-  return {fmod(a.real, b.real), fmod(a.imag, b.imag)};
+  auto real = a.real - (b.real * static_cast<int64_t>(a.real / b.real));
+  auto imag = a.imag - (b.imag * static_cast<int64_t>(a.imag / b.imag));
+  return {real, imag};
 }

python/mlx/nn/losses.py

@@ -2,6 +2,7 @@
 
 import mlx.core as mx
 
+
 def cross_entropy(
     logits: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none"
 ) -> mx.array:
@@ -24,7 +25,9 @@ def cross_entropy(
     return _reduce(loss, reduction)
 
 
-def l1_loss(predictions: mx.array, targets: mx.array, reduction: str = "none") -> mx.array:
+def l1_loss(
+    predictions: mx.array, targets: mx.array, reduction: str = "none"
+) -> mx.array:
     """
     Computes the L1 loss between predictions and targets.
 
@@ -38,11 +41,13 @@ def l1_loss(predictions: mx.array, targets: mx.array, reduction: str = "none") -
         mx.array: The computed L1 loss.
     """
     loss = mx.mean(mx.abs(predictions - targets))
-    
+
     return _reduce(loss, reduction)
 
 
-def mse_loss(predictions: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none") -> mx.array:
+def mse_loss(
+    predictions: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none"
+) -> mx.array:
     """
     Computes the mean squared error loss between predictions and targets.
 
@@ -57,11 +62,13 @@ def mse_loss(predictions: mx.array, targets: mx.array, axis: int = -1, reduction
         mx.array: The computed mean squared error loss.
     """
     loss = mx.mean(mx.square(predictions - targets), axis)
-    
+
     return _reduce(loss, reduction)
 
 
-def nll_loss(logits: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none") -> mx.array:
+def nll_loss(
+    logits: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none"
+) -> mx.array:
     """
     Computes the negative log likelihood loss between logits and targets.
 
@@ -80,7 +87,9 @@ def nll_loss(logits: mx.array, targets: mx.array, axis: int = -1, reduction: str
     return _reduce(loss, reduction)
 
 
-def kl_div_loss(logits: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none") -> mx.array:
+def kl_div_loss(
+    logits: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none"
+) -> mx.array:
     """
     Computes the Kullback-Leibler divergence loss between logits and targets.
 
@@ -98,7 +107,8 @@ def kl_div_loss(logits: mx.array, targets: mx.array, axis: int = -1, reduction:
 
     return _reduce(loss, reduction)
 
-def _reduce(loss: mx.array, reduction: str = 'none'):
+
+def _reduce(loss: mx.array, reduction: str = "none"):
     if reduction == "mean":
         return mx.mean(loss)
     elif reduction == "sum":

python/tests/test_nn.py

@@ -42,11 +42,11 @@ class TestNN(mlx_tests.MLXTestCase):
         targets = mx.array([0.5, 0.2, 0.9, 0.0])
         losses = nn.losses.l1_loss(predictions, targets, reduction="none")
         self.assertEqual(losses, 0.0)
-    
+
     def test_mse_loss(self):
         predictions = mx.array([0.5, 0.2, 0.9, 0.0])
         targets = mx.array([0.7, 0.1, 0.8, 0.2])
-        
+
         # Test with reduction 'none'
         losses_none = nn.losses.mse_loss(predictions, targets, reduction="none")
         expected_none = mx.array([0.04, 0.01, 0.01, 0.04])
@@ -62,7 +62,6 @@ class TestNN(mlx_tests.MLXTestCase):
         expected_sum = mx.sum(expected_none)
         self.assertEqual(losses_sum, expected_sum)
 
-
     def test_nll_loss(self):
         logits = mx.array([[0.0, -float("inf")], [-float("inf"), 0.0]])
         targets = mx.array([0, 1])
@@ -82,7 +81,6 @@ class TestNN(mlx_tests.MLXTestCase):
         expected_sum = mx.sum(expected_none)
         self.assertEqual(losses_sum, expected_sum)
 
-
     def test_kl_div_loss(self):
         p_logits = mx.array([[1.0, 2.0], [0.5, 1.0]])
         q_logits = mx.array([[0.8, 1.5], [0.4, 1.2]])