refactor: remove infer_device and rename handle_device_shifting decorator

ivy/func_wrapper.py

@@ -16,8 +16,7 @@
 # for wrapping (sequence matters)
 FN_DECORATORS = [
     "handle_complex_input",
-    "infer_device",
-    "handle_device_shifting",
+    "handle_device",
     "infer_dtype",
     "handle_array_function",
     "outputs_to_ivy_arrays",
@@ -778,42 +777,9 @@ def _infer_dtype(*args, dtype=None, **kwargs):
 # ----------------#
 
 
-def infer_device(fn: Callable) -> Callable:
+def handle_device(fn: Callable) -> Callable:
     @functools.wraps(fn)
-    def _infer_device(*args, device=None, **kwargs):
-        """
-        Determine the correct `device`, and then calls the function with the `device`
-        passed explicitly.
-
-        Parameters
-        ----------
-        args
-            The arguments to be passed to the function.
-
-        device
-            The device for the function.
-
-        kwargs
-            The keyword arguments to be passed to the function.
-
-        Returns
-        -------
-            The return of the function, with `device` passed explicitly.
-        """
-        # find the first array argument, if required
-        arr = None if ivy.exists(device) else _get_first_array(*args, **kwargs)
-        # infer the correct device
-        device = ivy.default_device(device, item=arr, as_native=True)
-        # call the function with device provided explicitly
-        return fn(*args, device=device, **kwargs)
-
-    _infer_device.infer_device = True
-    return _infer_device
-
-
-def handle_device_shifting(fn: Callable) -> Callable:
-    @functools.wraps(fn)
-    def _handle_device_shifting(*args, **kwargs):
+    def _handle_device(*args, **kwargs):
         """
         Move all array inputs of the function to `ivy.default_device()`.
 
@@ -856,8 +822,8 @@ def _handle_device_shifting(*args, **kwargs):
             )
         return fn(*args, **kwargs)
 
-    _handle_device_shifting.handle_device_shifting = True
-    return _handle_device_shifting
+    _handle_device.handle_device = True
+    return _handle_device
 
 
 # Inplace Update Handling #
@@ -1071,9 +1037,9 @@ def _wrap_function(
     """
     Apply wrapping to backend implementation `to_wrap` if the original implementation
     `original` is also wrapped, and if `to_wrap` is not already wrapped. Attributes
-    `handle_nestable`, `infer_device` etc are set during wrapping, hence indicate to us
-    whether a certain function has been wrapped or not. Also handles wrapping of the
-    `linalg` namespace.
+    `handle_nestable` etc are set during wrapping, hence indicate to us whether a
+    certain function has been wrapped or not. Also handles wrapping of the `linalg`
+    namespace.
 
     Parameters
     ----------

ivy/functional/backends/jax/creation.py

@@ -34,7 +34,7 @@ def arange(
     step: float = 1,
     *,
     dtype: Optional[jnp.dtype] = None,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     if dtype:
@@ -69,7 +69,7 @@ def asarray(
     *,
     copy: Optional[bool] = None,
     dtype: Optional[jnp.dtype] = None,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     ivy.utils.assertions._check_jax_x64_flag(dtype)
@@ -83,7 +83,7 @@ def empty(
     shape: Union[ivy.NativeShape, Sequence[int]],
     *,
     dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     return jnp.empty(shape, dtype)
@@ -94,7 +94,7 @@ def empty_like(
     /,
     *,
     dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     return jnp.empty_like(x, dtype=dtype)
@@ -108,7 +108,7 @@ def eye(
     k: int = 0,
     batch_shape: Optional[Union[int, Sequence[int]]] = None,
     dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     if n_cols is None:
@@ -135,7 +135,7 @@ def full(
     fill_value: Union[int, float, bool],
     *,
     dtype: Optional[Union[ivy.Dtype, jnp.dtype]] = None,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     dtype = ivy.default_dtype(dtype=dtype, item=fill_value, as_native=True)
@@ -148,7 +148,7 @@ def full_like(
     fill_value: Number,
     *,
     dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     return jnp.full_like(x, fill_value, dtype=dtype)
@@ -165,7 +165,7 @@ def linspace(
     axis: Optional[int] = None,
     endpoint: bool = True,
     dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     if axis is None:
@@ -239,7 +239,7 @@ def ones(
     shape: Union[ivy.NativeShape, Sequence[int]],
     *,
     dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     return jnp.ones(shape, dtype)
@@ -250,7 +250,7 @@ def ones_like(
     /,
     *,
     dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     return jnp.ones_like(x, dtype=dtype)
@@ -268,7 +268,7 @@ def zeros(
     shape: Union[ivy.NativeShape, Sequence[int]],
     *,
     dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     return jnp.zeros(shape, dtype)
@@ -279,7 +279,7 @@ def zeros_like(
     /,
     *,
     dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     return jnp.zeros_like(x, dtype=dtype)
@@ -314,7 +314,7 @@ def one_hot(
     off_value: Optional[Number] = None,
     axis: Optional[int] = None,
     dtype: Optional[jnp.dtype] = None,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
     on_none = on_value is None
@@ -356,6 +356,6 @@ def triu_indices(
     k: int = 0,
     /,
     *,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
 ) -> Tuple[JaxArray]:
     return jnp.triu_indices(n=n_rows, k=k, m=n_cols)

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

@@ -72,7 +72,7 @@ def tril_indices(
     k: int = 0,
     /,
     *,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
 ) -> Tuple[JaxArray, ...]:
     return jnp.tril_indices(n=n_rows, k=k, m=n_cols)
 

ivy/functional/backends/jax/random.py

@@ -44,7 +44,7 @@ def random_uniform(
     low: Union[float, JaxArray] = 0.0,
     high: Union[float, JaxArray] = 1.0,
     shape: Optional[Union[ivy.NativeShape, Sequence[int]]] = None,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     dtype: jnp.dtype,
     seed: Optional[int] = None,
     out: Optional[JaxArray] = None,
@@ -66,7 +66,7 @@ def random_normal(
     mean: Union[float, JaxArray] = 0.0,
     std: Union[float, JaxArray] = 1.0,
     shape: Optional[Union[ivy.NativeShape, Sequence[int]]] = None,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     dtype: jnp.dtype,
     seed: Optional[int] = None,
     out: Optional[JaxArray] = None,
@@ -91,7 +91,7 @@ def multinomial(
     batch_size: int = 1,
     probs: Optional[JaxArray] = None,
     replace: bool = True,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     seed: Optional[int] = None,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
@@ -132,7 +132,7 @@ def randint(
     /,
     *,
     shape: Optional[Union[ivy.NativeShape, Sequence[int]]] = None,
-    device: jaxlib.xla_extension.Device,
+    device: jaxlib.xla_extension.Device = None,
     dtype: Optional[Union[jnp.dtype, ivy.Dtype]] = None,
     seed: Optional[int] = None,
     out: Optional[JaxArray] = None,

ivy/functional/backends/numpy/creation.py

@@ -30,7 +30,7 @@ def arange(
     step: float = 1,
     *,
     dtype: Optional[np.dtype] = None,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     if dtype:
@@ -57,7 +57,7 @@ def asarray(
     *,
     copy: Optional[bool] = None,
     dtype: Optional[np.dtype] = None,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     ret = _to_device(np.asarray(obj, dtype=dtype), device=device)
@@ -68,14 +68,19 @@ def empty(
     shape: Union[ivy.NativeShape, Sequence[int]],
     *,
     dtype: np.dtype,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     return _to_device(np.empty(shape, dtype), device=device)
 
 
 def empty_like(
-    x: np.ndarray, /, *, dtype: np.dtype, device: str, out: Optional[np.ndarray] = None
+    x: np.ndarray,
+    /,
+    *,
+    dtype: np.dtype,
+    device: str = None,
+    out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     return _to_device(np.empty_like(x, dtype=dtype), device=device)
 
@@ -88,7 +93,7 @@ def eye(
     k: int = 0,
     batch_shape: Optional[Union[int, Sequence[int]]] = None,
     dtype: np.dtype,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     if n_cols is None:
@@ -116,7 +121,7 @@ def full(
     fill_value: Union[int, float, bool],
     *,
     dtype: Optional[Union[ivy.Dtype, np.dtype]] = None,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     dtype = ivy.default_dtype(dtype=dtype, item=fill_value, as_native=True)
@@ -132,7 +137,7 @@ def full_like(
     fill_value: Number,
     *,
     dtype: np.dtype,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     return _to_device(np.full_like(x, fill_value, dtype=dtype), device=device)
@@ -147,7 +152,7 @@ def linspace(
     axis: Optional[int] = None,
     endpoint: bool = True,
     dtype: np.dtype,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     if axis is None:
@@ -176,14 +181,19 @@ def ones(
     shape: Union[ivy.NativeShape, Sequence[int]],
     *,
     dtype: np.dtype,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     return _to_device(np.ones(shape, dtype), device=device)
 
 
 def ones_like(
-    x: np.ndarray, /, *, dtype: np.dtype, device: str, out: Optional[np.ndarray] = None
+    x: np.ndarray,
+    /,
+    *,
+    dtype: np.dtype,
+    device: str = None,
+    out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     return _to_device(np.ones_like(x, dtype=dtype), device=device)
 
@@ -204,14 +214,19 @@ def zeros(
     shape: Union[ivy.NativeShape, Sequence[int]],
     *,
     dtype: np.dtype,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     return _to_device(np.zeros(shape, dtype), device=device)
 
 
 def zeros_like(
-    x: np.ndarray, /, *, dtype: np.dtype, device: str, out: Optional[np.ndarray] = None
+    x: np.ndarray,
+    /,
+    *,
+    dtype: np.dtype,
+    device: str = None,
+    out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     return _to_device(np.zeros_like(x, dtype=dtype), device=device)
 
@@ -243,7 +258,7 @@ def one_hot(
     off_value: Optional[Number] = None,
     axis: Optional[int] = None,
     dtype: Optional[np.dtype] = None,
-    device: str,
+    device: str = None,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     on_none = on_value is None
@@ -287,7 +302,7 @@ def triu_indices(
     k: int = 0,
     /,
     *,
-    device: str,
+    device: str = None,
 ) -> Tuple[np.ndarray]:
     return tuple(
         _to_device(np.asarray(np.triu_indices(n=n_rows, k=k, m=n_cols)), device=device)

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

@@ -33,7 +33,7 @@ def tril_indices(
     k: int = 0,
     /,
     *,
-    device: str,
+    device: str = None,
 ) -> Tuple[np.ndarray, ...]:
     return tuple(
         _to_device(np.asarray(np.tril_indices(n=n_rows, k=k, m=n_cols)), device=device)