Merge b3c1aaafd2 into b8022c578a

benchmarks/python/comparative/bench_mlx.py

@@ -224,6 +224,13 @@ def relu6(x):
     mx.eval(y)
 
 
+def relu_squared(x):
+    y = x
+    for i in range(100):
+        y = nn.relu_squared(y)
+    mx.eval(y)
+
+
 def softplus(x):
     y = x
     for i in range(100):
@@ -458,6 +465,9 @@ if __name__ == "__main__":
     elif args.benchmark == "relu6":
         print(bench(relu6, x))
 
+    elif args.benchmark == "relu_squared":
+        print(bench(relu_squared, x))
+
     elif args.benchmark == "celu":
         print(bench(celu, x))
 

benchmarks/python/comparative/bench_torch.py

@@ -157,6 +157,15 @@ def relu6(x):
     sync_if_needed(x)
 
 
+@torch.no_grad()
+def relu_squared(x):
+    y = x
+    for i in range(100):
+        y = torch.nn.functional.relu(y)
+        y = torch.square(y)
+    sync_if_needed(x)
+
+
 @torch.no_grad()
 def softplus(x):
     y = x
@@ -407,6 +416,9 @@ if __name__ == "__main__":
     elif args.benchmark == "relu6":
         print(bench(relu6, x))
 
+    elif args.benchmark == "relu_squared":
+        print(bench(relu_squared, x))
+
     elif args.benchmark == "softplus":
         print(bench(softplus, x))
 

benchmarks/python/comparative/compare.py

@@ -207,6 +207,8 @@ if __name__ == "__main__":
     compare_filtered("elu --size 32x16x1024 --cpu")
     compare_filtered("relu6 --size 32x16x1024")
     compare_filtered("relu6 --size 32x16x1024 --cpu")
+    compare_filtered("relu_squared --size 32x16x1024")
+    compare_filtered("relu_squared --size 32x16x1024 --cpu")
     compare_filtered("softplus --size 32x16x1024")
     compare_filtered("softplus --size 32x16x1024 --cpu")
     compare_filtered("celu --size 32x16x1024")

docs/src/python/nn/functions.rst

@@ -28,6 +28,7 @@ simple functions.
    prelu
    relu
    relu6
+   relu_squared
    selu
    sigmoid
    silu

docs/src/python/nn/layers.rst

@@ -51,6 +51,7 @@ Layers
    RMSNorm
    ReLU
    ReLU6
+   ReLUSquared
    RNN
    RoPE
    SELU

python/mlx/nn/layers/__init__.py

@@ -16,6 +16,7 @@ from mlx.nn.layers.activations import (
     PReLU,
     ReLU,
     ReLU6,
+    ReLUSquared,
     Sigmoid,
     SiLU,
     Softmax,
@@ -41,6 +42,7 @@ from mlx.nn.layers.activations import (
     prelu,
     relu,
     relu6,
+    relu_squared,
     selu,
     sigmoid,
     silu,

python/mlx/nn/layers/activations.py

@@ -71,6 +71,17 @@ def relu6(x):
     return mx.minimum(mx.maximum(x, 0), 6.0)
 
 
+@partial(mx.compile, shapeless=True)
+def relu_squared(x):
+    r"""Applies the Rectified Linear Unit squared.
+
+    Applies :math:`\max(x, 0)^2` element wise.
+
+    Reference: https://arxiv.org/abs/2109.08668v2
+    """
+    return relu(x).square()
+
+
 @partial(mx.compile, shapeless=True)
 def softmax(x, axis=-1):
     r"""Applies the Softmax function.
@@ -420,6 +431,18 @@ class ReLU6(Module):
     """
 
 
+@_make_activation_module(relu_squared)
+class ReLUSquared(Module):
+    r"""Applies the Rectified Linear Unit squared.
+
+    Applies :math:`\max(x, 0)^2` element wise.
+
+    Reference: https://arxiv.org/abs/2109.08668v2
+
+    See :func:`relu_squared` for the functional equivalent.
+    """
+
+
 @_make_activation_module(softmax)
 class Softmax(Module):
     r"""Applies the Softmax function.

python/tests/test_nn.py

@@ -855,6 +855,13 @@ class TestLayers(mlx_tests.MLXTestCase):
         self.assertEqual(y.shape, (3,))
         self.assertEqual(y.dtype, mx.float32)
 
+    def test_relu_squared(self):
+        x = mx.array([-1.0, 0.0, 1.0, 2.0, 3.0])
+        y = nn.relu_squared(x)
+        self.assertTrue(mx.array_equal(y, mx.array([0.0, 0.0, 1.0, 4.0, 9.0])))
+        self.assertEqual(y.shape, (5,))
+        self.assertEqual(y.dtype, mx.float32)
+
     def test_leaky_relu(self):
         x = mx.array([1.0, -1.0, 0.0])
         y = nn.leaky_relu(x)