add padding _kwargs

kernex/__init__.py

@@ -27,4 +27,4 @@
     "offset_kernel_scan",
 )
 
-__version__ = "0.2.0"
+__version__ = "0.2.1"

kernex/_src/map.py

@@ -67,9 +67,13 @@ def kernel_map(
     relative: bool = False,
     map_kind: MapKind = "vmap",
     map_kwargs: dict[str, Any] | None = None,
+    padding_kwargs: dict[str, Any] | None = None,
 ) -> Callable:
 
     map_kwargs = map_kwargs or {}
+    padding_kwargs = padding_kwargs or {}
+    padding_kwargs.pop("pad_width", None)  # handled by border
+
     map_tranform = transform_func_map[map_kind]
     pad_width = _calculate_pad_width(border)
     args = (shape, kernel_size, strides, border)
@@ -82,7 +86,7 @@ def kernel_map(
     slices = tuple(func_map.values())
 
     def single_call_wrapper(array: jax.Array, *a, **k):
-        padded_array = jnp.pad(array, pad_width)
+        padded_array = jnp.pad(array, pad_width=pad_width, **padding_kwargs)
 
         # convert the function to a callable that takes a view and an array
         # and returns the result of the function applied to the view
@@ -98,7 +102,7 @@ def map_func(view):
         return result.reshape(*output_shape, *result.shape[1:])
 
     def multi_call_wrapper(array: jax.Array, *a, **k):
-        padded_array = jnp.pad(array, pad_width)
+        padded_array = jnp.pad(array, pad_width=pad_width, **padding_kwargs)
         # convert the functions to a callable that takes a view and an array
         # and returns the result of the function applied to the view
         # the result is a 1D array of the same length as the number of views
@@ -133,6 +137,7 @@ def offset_kernel_map(
     relative: bool = False,
     map_kind: MapKind = "vmap",
     map_kwargs: dict[str, Any] = None,
+    offset_kwargs: dict[str, Any] = None,
 ):
 
     func = kernel_map(
@@ -144,6 +149,7 @@ def offset_kernel_map(
         relative=relative,
         map_kind=map_kind,
         map_kwargs=map_kwargs,
+        padding_kwargs=offset_kwargs,
     )
     set_indices = _get_set_indices(shape, strides, offset)
 

kernex/_src/scan.py

@@ -71,9 +71,12 @@ def kernel_scan(
     relative: bool = False,
     scan_kind: ScanKind = "scan",  # dummy to make signature consistent with kernel_map
     scan_kwargs: dict[str, Any] | None = None,
+    padding_kwargs: dict[str, Any] | None = None,
 ):
 
     scan_kwargs = scan_kwargs or {}
+    padding_kwargs = padding_kwargs or {}
+    padding_kwargs.pop("pad_width", None)
     scan_transform = transform_func_map[scan_kind]
     pad_width = _calculate_pad_width(border)
     args = (shape, kernel_size, strides, border)
@@ -82,7 +85,7 @@ def kernel_scan(
     slices = tuple(func_map.values())
 
     def single_call_wrapper(array: jax.Array, *a, **k):
-        padded_array = jnp.pad(array, pad_width)
+        padded_array = jnp.pad(array, pad_width=pad_width, **padding_kwargs)
         func0 = next(iter(func_map))
         reduced_func = _transform_scan_func(func0, kernel_size, relative)(*a, **k)
 
@@ -95,7 +98,7 @@ def scan_body(padded_array: jax.Array, view: jax.Array):
         return result.reshape(output_shape)
 
     def multi_call_wrapper(array: jax.Array, *a, **k):
-        padded_array = jnp.pad(array, pad_width)
+        padded_array = jnp.pad(array, pad_width=pad_width, **padding_kwargs)
 
         reduced_funcs = tuple(
             _transform_scan_func(func, kernel_size, relative)(*a, **k)
@@ -124,6 +127,7 @@ def offset_kernel_scan(
     relative: bool = False,
     scan_kind: ScanKind = "scan",
     scan_kwargs: dict[str, Any] | None = None,
+    offset_kwargs: dict[str, Any] | None = None,
 ):
 
     func = kernel_scan(
@@ -135,6 +139,7 @@ def offset_kernel_scan(
         relative=relative,
         scan_kind=scan_kind,
         scan_kwargs=scan_kwargs,
+        padding_kwargs=offset_kwargs,
     )
     set_indices = _get_set_indices(shape, strides, offset)
 

kernex/interface/kernel_interface.py

@@ -32,10 +32,10 @@
 )
 
 BorderType = Union[
-    int,
-    Tuple[int, ...],
-    Tuple[Tuple[int, int], ...],
-    Literal["valid", "same", "SAME", "VALID"],
+    int,  # single int to pad all axes before and after the array
+    Tuple[int, ...],  # tuple of ints to pad before and after each axis
+    Tuple[Tuple[int, int], ...],  # tuple of tuples to pad before and after each axis
+    Literal["valid", "same", "SAME", "VALID"],  # string to use a predefined padding
 ]
 
 StridesType = Union[Tuple[int, ...], int]
@@ -56,6 +56,7 @@ def __init__(
         container: dict[Callable, slice | int] | None = None,
         transform_kind: MapKind | ScanKind | None = None,
         transform_kwargs: dict[str, Any] | None = None,
+        border_kwargs: dict[str, Any] | None = None,
     ):
         self.kernel_size = kernel_size
         self.strides = strides
@@ -71,6 +72,7 @@ def __init__(
         )
         self.transform_kind = transform_kind
         self.transform_kwargs = transform_kwargs
+        self.border_kwargs = border_kwargs
 
     def __repr__(self) -> str:
         return (
@@ -124,6 +126,7 @@ def _wrap_mesh(self, array: jax.Array, *a, **k):
             self.relative,
             self.transform_kind,
             self.transform_kwargs,
+            self.border_kwargs,
         )(array, *a, **k)
 
     def _wrap_decorator(self, func):
@@ -154,6 +157,7 @@ def call(array, *args, **kwargs):
                 self.relative,
                 self.transform_kind,
                 self.transform_kwargs,
+                self.border_kwargs,
             )(array, *args, **kwargs)
 
         return call
@@ -252,6 +256,7 @@ def __init__(
             named_axis=named_axis,
             transform_kind=scan_kind,
             transform_kwargs=scan_kwargs,
+            border_kwargs=None,
         )
 
 
@@ -337,6 +342,7 @@ def __init__(
             named_axis=named_axis,
             transform_kind=map_kind,
             transform_kwargs=map_kwargs,
+            border_kwargs=None,
         )
 
 
@@ -350,6 +356,7 @@ def __init__(
         named_axis: dict[int, str] = None,
         scan_kind: ScanKind = "scan",
         scan_kwargs: dict[str, Any] | None = None,
+        padding_kwargs: dict[str, Any] | None = None,
     ):
         """Apply a function to a sliding window of the input array sequentially.
 
@@ -368,6 +375,9 @@ def __init__(
             scan_kwargs: optional kwargs to be passed to the scan function.
                 for example, `scan_kwargs={'reverse': True}` will reverse the
                 application of the function.
+            padding_kwargs: optional kwargs to be passed to the padding function.
+                for example, `padding_kwargs=dict(constant_values=10)` will pad
+                the input array with 10 for same padding.
 
         Returns:
             A function that takes an array as input and returns the result of
@@ -400,6 +410,7 @@ def __init__(
             named_axis=named_axis,
             transform_kind=scan_kind,
             transform_kwargs=scan_kwargs,
+            border_kwargs=padding_kwargs,
         )
 
 
@@ -413,6 +424,7 @@ def __init__(
         named_axis: dict[int, str] = None,
         map_kind: MapKind = "vmap",
         map_kwargs: dict = None,
+        padding_kwargs: dict = None,
     ):
         """Apply a function to a sliding window of the input array in parallel.
 
@@ -432,6 +444,9 @@ def __init__(
             map_kwargs: optional kwargs to be passed to the map function.
                 for example, `map_kwargs={'axis_name': 'i'}` will apply the
                 function along the axis named `i` for `pmap`.
+            padding_kwargs: optional kwargs to be passed to the padding function.
+                for example, `padding_kwargs=dict(constant_values=10)` will pad
+                the input array with 10 for same padding.
 
         Returns:
             A function that takes an array as input and applies the kernel
@@ -464,4 +479,5 @@ def __init__(
             named_axis=named_axis,
             transform_kind=map_kind,
             transform_kwargs=map_kwargs,
+            border_kwargs=padding_kwargs,
         )

tests/test_interface.py

@@ -346,3 +346,21 @@ def kex_conv2d(x, w):
     pred_grad = jax.grad(lambda w: jnp.sum(kex_conv2d(x, w)))(w)
 
     np.testing.assert_allclose(true_grad[0], pred_grad, atol=1e-3)
+
+
+def test_padding_kwargs():
+    @kex.kmap(
+        kernel_size=(3,),
+        padding=("same"),
+        relative=False,
+        padding_kwargs=dict(constant_values=10),
+    )
+    def f(x):
+        return x
+
+    x = jnp.array([1, 2, 3, 4, 5])
+
+    np.testing.assert_allclose(
+        f(x),
+        np.array([[10, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 10]]),
+    )