Reimplement ReLu6 in the backends and frontends

ivy/array/experimental/activations.py

@@ -109,3 +109,50 @@ def prelu(
         -------
         """
         return ivy.prelu(self._data, slope, out=out)
+
+    def relu6(self, /, *, out: Optional[ivy.Array] = None) -> ivy.Array:
+        """Applies the rectified linear unit 6 function element-wise.
+
+        Parameters
+        ----------
+        x
+            input array
+        out
+            optional output array, for writing the result to.
+            It must have a shape that the inputs broadcast to.
+
+        Returns
+        -------
+        ret
+            an array containing the rectified linear unit 6 activation
+            of each element in ``x``.
+
+        Examples
+        --------
+        With :class:`ivy.Array` input:
+
+        >>> x = ivy.array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.])
+        >>> y = ivy.relu6(x)
+        >>> print(y)
+        ivy.array([0., 0., 1., 2., 3., 4., 5., 6., 6.])
+
+        >>> x = ivy.array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.])
+        >>> y = ivy.zeros(9)
+        >>> ivy.relu6(x, out = y)
+        >>> print(y)
+        ivy.array([0., 0., 1., 2., 3., 4., 5., 6., 6.])
+
+        With :class:`ivy.Container` input:
+
+        >>> x = {
+                    a: ivy.array([-3., -2., -1., 0., 1., 2., 3., 4., 5.]),
+                    b: ivy.array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+                }
+        >>> x = ivy.relu6(x, out=x)
+        >>> print(x)
+        {
+        a: ivy.array([0., 0., 0., 0., 1., 2., 3., 4., 5.]),
+        b: ivy.array([1., 2., 3., 4., 5., 6., 6., 6., 6.])
+        }
+        """
+        return ivy.relu6(self._data, out=out)

ivy/container/experimental/activations.py

@@ -323,3 +323,131 @@ def prelu(
             map_sequences=map_sequences,
             out=out,
         )
+
+    @staticmethod
+    def static_relu6(
+        x: Union[ivy.Array, ivy.NativeArray, ivy.Container],
+        /,
+        *,
+        key_chains: Optional[Union[List[str], Dict[str, str]]] = None,
+        to_apply: bool = True,
+        prune_unapplied: bool = False,
+        map_sequences: bool = False,
+        out: Optional[ivy.Container] = None,
+    ) -> ivy.Container:
+        """
+        ivy.Container static method variant of ivy.relu6.
+        This method simply wraps the function, and so the docstring
+        for ivy.relu6 also applies to this method with minimal changes.
+
+        Parameters
+        ----------
+        x
+            input container.
+        key_chains
+            The key-chains to apply or not apply the method to. Default is ``None``.
+        to_apply
+            If True, the method will be applied to key_chains, otherwise key_chains
+            will be skipped. Default is ``True``.
+        prune_unapplied
+            Whether to prune key_chains for which the function was not applied.
+            Default is ``False``.
+        map_sequences
+            Whether to also map method to sequences (lists, tuples).
+            Default is ``False``.
+        out
+            optional output container, for writing the result to. It must have a shape
+            that the inputs broadcast to.
+
+        Returns
+        -------
+        ret
+            a container with the rectified linear 6 activation unit function
+            applied element-wise.
+
+        Examples
+        --------
+        >>> x = {
+                    a: ivy.array([-3., -2., -1., 0., 1., 2., 3., 4., 5.]),
+                    b: ivy.array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+                }
+        >>> y = ivy.Container.static_relu6(x)
+        >>> print(y)
+        {
+            a: ivy.array([0., 0., 0., 0., 1., 2., 3., 4., 5.]),
+            b: ivy.array([1., 2., 3., 4., 5., 6., 6., 6., 6.])
+        }
+
+        """
+        return ContainerBase.cont_multi_map_in_function(
+            "relu6",
+            x,
+            key_chains=key_chains,
+            to_apply=to_apply,
+            prune_unapplied=prune_unapplied,
+            map_sequences=map_sequences,
+            out=out,
+        )
+
+    def relu6(
+        self: ivy.Container,
+        /,
+        *,
+        key_chains: Optional[Union[List[str], Dict[str, str]]] = None,
+        to_apply: bool = True,
+        prune_unapplied: bool = False,
+        map_sequences: bool = False,
+        out: Optional[ivy.Container] = None,
+    ) -> ivy.Container:
+        """
+        ivy.Container instance method variant of ivy.relu6.
+        This method simply wraps the function, and so the docstring
+        for ivy.relu6 also applies to this method with minimal changes.
+
+        Parameters
+        ----------
+        self
+            input container.
+        key_chains
+            The key-chains to apply or not apply the method to. Default is ``None``.
+        to_apply
+            If True, the method will be applied to key_chains, otherwise key_chains
+            will be skipped. Default is ``True``.
+        prune_unapplied
+            Whether to prune key_chains for which the function was not applied.
+            Default is ``False``.
+        map_sequences
+            Whether to also map method to sequences (lists, tuples).
+            Default is ``False``.
+        out
+            optional output container, for writing the result to. It must have a shape
+            that the inputs broadcast to.
+
+        Returns
+        -------
+        ret
+            a container with the rectified linear 6 activation unit function
+            applied element-wise.
+
+        Examples
+        --------
+        >>> x = {
+                    a: ivy.array([-3., -2., -1., 0., 1., 2., 3., 4., 5.]),
+                    b: ivy.array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+                }
+        >>> y = x.relu()
+        >>> print(y)
+        {
+            a: ivy.array([0., 0., 0., 0., 1., 2., 3., 4., 5.]),
+            b: ivy.array([1., 2., 3., 4., 5., 6., 6., 6., 6.])
+        }
+
+        """
+        return self.static_relu6(
+            self,
+            key_chains=key_chains,
+            to_apply=to_apply,
+            prune_unapplied=prune_unapplied,
+            map_sequences=map_sequences,
+            out=out,
+        )

ivy/functional/backends/jax/experimental/activations.py

@@ -1,6 +1,7 @@
 from typing import Optional, Union
 
 # global
+import jax
 import jax.numpy as jnp
 from ivy.functional.backends.jax import JaxArray
 
@@ -13,6 +14,10 @@ def logit(x: JaxArray, /, *, eps: Optional[float] = None, out=None):
     return jnp.log(x / (1 - x))
 
 
+def relu6(x: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
+    return jax.nn.relu6(x)
+
+
 def thresholded_relu(
     x: JaxArray,
     /,

ivy/functional/backends/numpy/experimental/activations.py

@@ -31,3 +31,11 @@ def thresholded_relu(
 
 
 thresholded_relu.support_native_out = True
+
+
+@_scalar_output_to_0d_array
+def relu6(x: np.ndarray, /, *, out: Optional[np.ndarray] = None) -> np.ndarray:
+    return np.minimum(np.maximum(x, 0, dtype=x.dtype), 6, out=out, dtype=x.dtype)
+
+
+relu6.support_native_out = True

ivy/functional/backends/tensorflow/experimental/activations.py

@@ -30,3 +30,8 @@ def thresholded_relu(
     out: Optional[Tensor] = None,
 ) -> Tensor:
     return tf.where(x > threshold, x, 0)
+
+
+@with_unsupported_dtypes({"2.9.1 and below": ("complex",)}, backend_version)
+def relu6(x: Tensor, /, *, out: Optional[Tensor] = None) -> Tensor:
+    return tf.nn.relu6(x)

ivy/functional/backends/torch/experimental/activations.py

@@ -23,3 +23,8 @@ def thresholded_relu(
     out: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
     return torch.threshold(x, threshold=threshold, value=0)
+
+
+@with_unsupported_dtypes({"1.11.0 and below": ("bfloat16", "float16")}, backend_version)
+def relu6(x: torch.Tensor, /, *, out: Optional[torch.Tensor] = None) -> torch.Tensor:
+    return torch.nn.functional.relu6(x)

ivy/functional/frontends/jax/nn/non_linear_activations.py

@@ -269,7 +269,7 @@ def relu(x):
 
 @to_ivy_arrays_and_back
 def relu6(x):
-    res = ivy.minimum(ivy.maximum(x, 0.0), 6.0)
+    res = ivy.relu6(x)
     return _type_conversion_64(res)
 
 

ivy/functional/frontends/tensorflow/nn.py

@@ -421,6 +421,11 @@ def relu(features, name=None):
     return ivy.relu(features)
 
 
+@to_ivy_arrays_and_back
+def relu6(features, name=None):
+    return ivy.relu6(features)
+
+
 @to_ivy_arrays_and_back
 def softmax(logits, axis=None, name=None):
     return ivy.softmax(logits, axis=axis)

ivy/functional/frontends/tensorflow/raw_ops.py

@@ -487,7 +487,7 @@ def Pow(*, x, y, name="Pow"):
 
 @to_ivy_arrays_and_back
 def Relu6(features, name="Relu6"):
-    return ivy.clip(features, 0, 6)
+    return ivy.relu6(features)
 
 
 Sigmoid = to_ivy_arrays_and_back(

ivy/functional/frontends/torch/nn/functional/non_linear_activation_functions.py

@@ -172,7 +172,7 @@ def threshold_(input, threshold, value):
 
 
 def relu6(input, inplace=False):
-    ret = ivy.minimum(ivy.maximum(input, 0), 6)
+    ret = ivy.relu6(input)
     if inplace:
         ivy.inplace_update(input, ret)
         return input
@@ -307,7 +307,7 @@ def leaky_relu_(input, negative_slope=0.01):
 
 
 def hardswish(input, inplace=False):
-    relu6_val = ivy.minimum(ivy.maximum(ivy.add(input, 3), 0), 6)
+    relu6_val = ivy.relu6(ivy.add(input, 3))
     ret = ivy.multiply(input, ivy.divide(relu6_val, 6))
     if inplace:
         ivy.inplace_update(input, ret)

ivy/functional/ivy/experimental/activations.py

@@ -6,6 +6,7 @@
 from ivy.backend_handler import current_backend
 from ivy.exceptions import handle_exceptions
 from ivy.func_wrapper import (
+    handle_array_function,
     handle_nestable,
     to_native_arrays_and_back,
     handle_array_like_without_promotion,
@@ -173,3 +174,59 @@ def thresholded_relu(
     }
     """
     return current_backend(x).thresholded_relu(x, threshold=threshold, out=out)
+
+
+@to_native_arrays_and_back
+@handle_out_argument
+@handle_nestable
+@handle_exceptions
+@handle_array_like_without_promotion
+@handle_array_function
+def relu6(
+    x: Union[ivy.Array, ivy.NativeArray], /, *, out: Optional[ivy.Array] = None
+) -> ivy.Array:
+    """Applies the rectified linear unit 6 function element-wise.
+
+    Parameters
+    ----------
+    x
+        input array
+    out
+        optional output array, for writing the result to. It must have a shape that the
+        inputs broadcast to.
+
+    Returns
+    -------
+    ret
+        an array containing the rectified linear unit 6 activation of each element in
+        ``x``.
+
+    Examples
+    --------
+    With :class:`ivy.Array` input:
+
+    >>> x = ivy.array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.])
+    >>> y = ivy.relu6(x)
+    >>> print(y)
+    ivy.array([0., 0., 1., 2., 3., 4., 5., 6., 6.])
+
+    >>> x = ivy.array([-1.,  0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.])
+    >>> y = ivy.zeros(9)
+    >>> ivy.relu6(x, out = y)
+    >>> print(y)
+    ivy.array([0., 0., 1., 2., 3., 4., 5., 6., 6.])
+
+    With :class:`ivy.Container` input:
+
+    >>> x = {
+                a: ivy.array([-3., -2., -1., 0., 1., 2., 3., 4., 5.]),
+                b: ivy.array([1., 2., 3., 4., 5., 6., 7., 8., 9.])
+            }
+    >>> x = ivy.relu6(x, out=x)
+    >>> print(x)
+    {
+    a: ivy.array([0., 0., 0., 0., 1., 2., 3., 4., 5.]),
+    b: ivy.array([1., 2., 3., 4., 5., 6., 6., 6., 6.])
+    }
+    """
+    return current_backend(x).relu6(x, out=out)

ivy_tests/test_ivy/test_frontends/test_tensorflow/test_nn.py

@@ -1079,6 +1079,36 @@ def test_tensorflow_relu(
     )
 
 
+# relu6
+@handle_frontend_test(
+    fn_tree="tensorflow.nn.relu6",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("numeric"),
+        num_arrays=1,
+        min_value=-20,
+        max_value=20,
+    ),
+    test_with_out=st.just(False),
+)
+def test_tensorflow_relu6(
+    *,
+    dtype_and_x,
+    test_flags,
+    frontend,
+    fn_tree,
+    on_device,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        test_flags=test_flags,
+        frontend=frontend,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        features=x[0],
+    )
+
+
 # softmax
 @handle_frontend_test(
     fn_tree="tensorflow.nn.softmax",