Repository: dask/dask
Pull request #11822 (2025-03-07 15:50:14)

Implement map_overlap for array-expr
- [ ] Closes #xxxx
- [ ] Tests added / passed
- [ ] Passes `pre-commit run --all-files`


a-holm: Several creation functions, `repeat`, `sliding_window_view`, `__abs__`, `array_expr_enabled`, and minor refactorings in `_blockwise.py` and `_map_blocks.py` are not mentioned in the PR description. The core overlap logic in `dask/array/_array_expr/_overlap.py` is complex, involving custom layers and interactions between multiple expressions and functions and many functions has no docstring and it is unclear.  All of this is undocumented in the PR description.

There is also a discrepancy between the PR checklist ("Tests added") and the diff content. Tests seem largely covered by reusing existing tests. However, `xfail` markers indicate current limitations or missing dependencies.

Patch:
diff --git a/dask/array/__init__.py b/dask/array/__init__.py
index eb45ad86c..4dfead6fa 100644
--- a/dask/array/__init__.py
+++ b/dask/array/__init__.py
@@ -30,6 +30,11 @@ def _array_expr_enabled() -> bool:
     return use_array_expr if use_array_expr is not None else False
 
 
+def array_expr_enabled():
+    # Need a public variant for downstream libraries to check
+    return _array_expr_enabled()
+
+
 __all__ = [
     "bool",
     "complex64",
@@ -57,6 +62,7 @@ __all__ = [
     "ma",
     "overlap",
     "random",
+    "array_expr_enabled",
     "shuffle",
     "atop",
     "blockwise",
@@ -637,8 +643,9 @@ if _array_expr_enabled():
         return inner_func
 
     try:
+        from dask.array._array_expr import Array  # type: ignore
+        from dask.array._array_expr import _overlap as overlap  # type: ignore
         from dask.array._array_expr import (  # type: ignore
-            Array,
             arange,
             array,
             asanyarray,
@@ -647,15 +654,22 @@ if _array_expr_enabled():
             concatenate,
             elemwise,
             empty,
+            empty_like,
             from_array,
+            full,
+            full_like,
             linspace,
             map_blocks,
+            map_overlap,
             ones,
+            ones_like,
             random,
             rechunk,
             reduction,
+            repeat,
             stack,
             zeros,
+            zeros_like,
         )
         from dask.array.reductions import (
             all,
@@ -683,7 +697,6 @@ if _array_expr_enabled():
         lib = raise_not_implemented_error("lib")
         linalg = raise_not_implemented_error("linalg")
         ma = raise_not_implemented_error("ma")
-        overlap = raise_not_implemented_error("overlap")
         atop = raise_not_implemented_error("atop")
         register_chunk_type = raise_not_implemented_error("register_chunk_type")
         block = raise_not_implemented_error("block")
@@ -699,25 +712,19 @@ if _array_expr_enabled():
         unify_chunks = raise_not_implemented_error("unify_chunks")
         diag = raise_not_implemented_error("diag")
         diagonal = raise_not_implemented_error("diagonal")
-        empty_like = raise_not_implemented_error("empty_like")
         eye = raise_not_implemented_error("eye")
         fromfunction = raise_not_implemented_error("fromfunction")
-        full_like = raise_not_implemented_error("full_like")
         indices = raise_not_implemented_error("indices")
         meshgrid = raise_not_implemented_error("meshgrid")
-        ones_like = raise_not_implemented_error("ones_like")
         pad = raise_not_implemented_error("pad")
-        repeat = raise_not_implemented_error("repeat")
         tile = raise_not_implemented_error("tile")
         tri = raise_not_implemented_error("tri")
-        zeros_like = raise_not_implemented_error("zeros_like")
         apply_gufunc = raise_not_implemented_error("apply_gufunc")
         as_gufunc = raise_not_implemented_error("as_gufunc")
         gufunc = raise_not_implemented_error("gufunc")  # type: ignore
         moveaxis = raise_not_implemented_error("moveaxis")
         rollaxis = raise_not_implemented_error("rollaxis")
         optimize = raise_not_implemented_error("optimize")
-        map_overlap = raise_not_implemented_error("map_overlap")
         percentile = raise_not_implemented_error("percentile")
         argmax = raise_not_implemented_error("argmax")
         argmin = raise_not_implemented_error("argmin")
@@ -905,7 +912,6 @@ if _array_expr_enabled():
         tanh = raise_not_implemented_error("tanh")
         true_divide = raise_not_implemented_error("true_divide")
         trunc = raise_not_implemented_error("trunc")
-        full = raise_not_implemented_error("full")
 
         from dask.array.utils import assert_eq
         from dask.base import compute
diff --git a/dask/array/_array_expr/__init__.py b/dask/array/_array_expr/__init__.py
index 3134045d6..08ba0a5cb 100644
--- a/dask/array/_array_expr/__init__.py
+++ b/dask/array/_array_expr/__init__.py
@@ -14,6 +14,19 @@ from dask.array._array_expr._collection import (
     rechunk,
     stack,
 )
-from dask.array._array_expr._creation import arange, empty, linspace, ones, zeros
+from dask.array._array_expr._creation import (
+    arange,
+    empty,
+    empty_like,
+    full,
+    full_like,
+    linspace,
+    ones,
+    ones_like,
+    repeat,
+    zeros,
+    zeros_like,
+)
 from dask.array._array_expr._map_blocks import map_blocks
+from dask.array._array_expr._overlap import map_overlap, overlap, trim_overlap
 from dask.array._array_expr._reductions import _tree_reduce, reduction
diff --git a/dask/array/_array_expr/_blockwise.py b/dask/array/_array_expr/_blockwise.py
index dd6090003..7cc7b97f0 100644
--- a/dask/array/_array_expr/_blockwise.py
+++ b/dask/array/_array_expr/_blockwise.py
@@ -17,6 +17,7 @@ from dask.array.core import (
     is_scalar_for_elemwise,
     normalize_arg,
 )
+from dask.array.utils import meta_from_array
 from dask.blockwise import blockwise as core_blockwise
 from dask.delayed import unpack_collections
 from dask.layers import ArrayBlockwiseDep
@@ -63,9 +64,13 @@ class Blockwise(ArrayExpr):
     @cached_property
     def _meta(self):
         if self._meta_provided is not None:
-            return self._meta_provided
+            return meta_from_array(
+                self._meta_provided, ndim=self.ndim, dtype=self._meta_provided.dtype
+            )
         else:
-            return compute_meta(self.func, self.dtype, *self.args[::2], **self.kwargs)
+            return compute_meta(
+                self.func, self.operand("dtype"), *self.args[::2], **self.kwargs
+            )
 
     @cached_property
     def chunks(self):
@@ -114,13 +119,21 @@ class Blockwise(ArrayExpr):
 
     @cached_property
     def dtype(self):
-        return self.operand("dtype")
+        return super().dtype
 
     def __dask_tokenize__(self):
         if not self._determ_token:
             # TODO: Is there an actual need to overwrite this?
             self._determ_token = _tokenize_deterministic(
-                self.func, self.out_ind, self.dtype, *self.args, **self.kwargs
+                self.func,
+                self.out_ind,
+                self.dtype,
+                self.adjust_chunks,
+                self.new_axes,
+                self.align_arrays,
+                self.concatenate,
+                *self.args,
+                **self.kwargs,
             )
         return self._determ_token
 
diff --git a/dask/array/_array_expr/_collection.py b/dask/array/_array_expr/_collection.py
index 6c51cc6ff..e6e51ad57 100644
--- a/dask/array/_array_expr/_collection.py
+++ b/dask/array/_array_expr/_collection.py
@@ -191,6 +191,9 @@ class Array(DaskMethodsMixin):
     def __rfloordiv__(self, other):
         return elemwise(operator.floordiv, other, self)
 
+    def __abs__(self):
+        return elemwise(operator.abs, self)
+
     def __array_function__(self, func, types, args, kwargs):
         # TODO(expr-soon): Not done yet, but needed for assert_eq to identify us as an Array
         raise NotImplementedError
@@ -461,6 +464,21 @@ class Array(DaskMethodsMixin):
 
         return map_blocks(func, self, *args, **kwargs)
 
+    def map_overlap(self, func, depth, boundary=None, trim=True, **kwargs):
+        """Map a function over blocks of the array with some overlap
+
+        Refer to :func:`dask.array.map_overlap` for full documentation.
+
+        See Also
+        --------
+        dask.array.map_overlap : equivalent function
+        """
+        from dask.array._array_expr._overlap import map_overlap
+
+        return map_overlap(
+            func, self, depth=depth, boundary=boundary, trim=trim, **kwargs
+        )
+
 
 def from_graph(layer, _meta, chunks, keys, name_prefix):
     return new_collection(
diff --git a/dask/array/_array_expr/_creation.py b/dask/array/_array_expr/_creation.py
index 4202bc43b..5171e9c84 100644
--- a/dask/array/_array_expr/_creation.py
+++ b/dask/array/_array_expr/_creation.py
@@ -2,19 +2,24 @@ from __future__ import annotations
 
 import functools
 from functools import partial
+from numbers import Integral
 
 import numpy as np
+from tlz import sliding_window
 
 from dask._collections import new_collection
 from dask._task_spec import Task
+from dask.array._array_expr._collection import asarray, concatenate
 from dask.array._array_expr._expr import ArrayExpr
 from dask.array.chunk import arange as _arange
 from dask.array.chunk import linspace as _linspace
 from dask.array.core import normalize_chunks
+from dask.array.creation import _get_like_function_shapes_chunks
 from dask.array.utils import meta_from_array
 from dask.array.wrap import _parse_wrap_args, broadcast_trick
 from dask.blockwise import blockwise as core_blockwise
 from dask.layers import ArrayChunkShapeDep
+from dask.utils import cached_cumsum, derived_from
 
 
 class Arange(ArrayExpr):
@@ -125,7 +130,9 @@ class BroadcastTrick(ArrayExpr):
 
     def _layer(self) -> dict:
         func = broadcast_trick(self.func)
-        func = partial(func, meta=self._meta, dtype=self.dtype, **self.kwargs)
+        k = self.kwargs.copy()
+        k.pop("meta", None)
+        func = partial(func, meta=self._meta, dtype=self.dtype, **k)
         out_ind = dep_ind = tuple(range(len(self.shape)))
         return core_blockwise(
             func,
@@ -149,6 +156,10 @@ class Empty(BroadcastTrick):
     func = staticmethod(np.empty_like)
 
 
+class Full(BroadcastTrick):
+    func = staticmethod(np.full_like)
+
+
 def wrap_func_shape_as_first_arg(*args, klass, **kwargs):
     """
     Transform np creation function into blocked version
@@ -183,6 +194,7 @@ def wrap(func, **kwargs):
 ones = wrap(wrap_func_shape_as_first_arg, klass=Ones, dtype="f8")
 zeros = wrap(wrap_func_shape_as_first_arg, klass=Zeros, dtype="f8")
 empty = wrap(wrap_func_shape_as_first_arg, klass=Empty, dtype="f8")
+_full = wrap(wrap_func_shape_as_first_arg, klass=Full, dtype="f8")
 
 
 def arange(start=0, stop=None, step=1, *, chunks="auto", like=None, dtype=None):
@@ -272,3 +284,300 @@ def linspace(
         return result, result.expr.step
     else:
         return result
+
+
+def empty_like(a, dtype=None, order="C", chunks=None, name=None, shape=None):
+    """
+    Return a new array with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : array_like
+        The shape and data-type of `a` define these same attributes of the
+        returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
+    chunks : sequence of ints
+        The number of samples on each block. Note that the last block will have
+        fewer samples if ``len(array) % chunks != 0``.
+    name : str, optional
+        An optional keyname for the array. Defaults to hashing the input
+        keyword arguments.
+    shape : int or sequence of ints, optional.
+        Overrides the shape of the result.
+
+    Returns
+    -------
+    out : ndarray
+        Array of uninitialized (arbitrary) data with the same
+        shape and type as `a`.
+
+    See Also
+    --------
+    ones_like : Return an array of ones with shape and type of input.
+    zeros_like : Return an array of zeros with shape and type of input.
+    empty : Return a new uninitialized array.
+    ones : Return a new array setting values to one.
+    zeros : Return a new array setting values to zero.
+
+    Notes
+    -----
+    This function does *not* initialize the returned array; to do that use
+    `zeros_like` or `ones_like` instead.  It may be marginally faster than
+    the functions that do set the array values.
+    """
+
+    a = asarray(a, name=False)
+    shape, chunks = _get_like_function_shapes_chunks(a, chunks, shape)
+
+    # if shape is nan we cannot rely on regular empty function, we use
+    # generic map_blocks.
+    if np.isnan(shape).any():
+        return a.map_blocks(partial(np.empty_like, dtype=(dtype or a.dtype)))
+
+    return empty(
+        shape,
+        dtype=(dtype or a.dtype),
+        order=order,
+        chunks=chunks,
+        name=name,
+        meta=a._meta,
+    )
+
+
+def ones_like(a, dtype=None, order="C", chunks=None, name=None, shape=None):
+    """
+    Return an array of ones with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : array_like
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
+    chunks : sequence of ints
+        The number of samples on each block. Note that the last block will have
+        fewer samples if ``len(array) % chunks != 0``.
+    name : str, optional
+        An optional keyname for the array. Defaults to hashing the input
+        keyword arguments.
+    shape : int or sequence of ints, optional.
+        Overrides the shape of the result.
+
+    Returns
+    -------
+    out : ndarray
+        Array of ones with the same shape and type as `a`.
+
+    See Also
+    --------
+    zeros_like : Return an array of zeros with shape and type of input.
+    empty_like : Return an empty array with shape and type of input.
+    zeros : Return a new array setting values to zero.
+    ones : Return a new array setting values to one.
+    empty : Return a new uninitialized array.
+    """
+
+    a = asarray(a, name=False)
+    shape, chunks = _get_like_function_shapes_chunks(a, chunks, shape)
+
+    # if shape is nan we cannot rely on regular ones function, we use
+    # generic map_blocks.
+    if np.isnan(shape).any():
+        return a.map_blocks(partial(np.ones_like, dtype=(dtype or a.dtype)))
+
+    return ones(
+        shape,
+        dtype=(dtype or a.dtype),
+        order=order,
+        chunks=chunks,
+        name=name,
+        meta=a._meta,
+    )
+
+
+def zeros_like(a, dtype=None, order="C", chunks=None, name=None, shape=None):
+    """
+    Return an array of zeros with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : array_like
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
+    chunks : sequence of ints
+        The number of samples on each block. Note that the last block will have
+        fewer samples if ``len(array) % chunks != 0``.
+    name : str, optional
+        An optional keyname for the array. Defaults to hashing the input
+        keyword arguments.
+    shape : int or sequence of ints, optional.
+        Overrides the shape of the result.
+
+    Returns
+    -------
+    out : ndarray
+        Array of zeros with the same shape and type as `a`.
+
+    See Also
+    --------
+    ones_like : Return an array of ones with shape and type of input.
+    empty_like : Return an empty array with shape and type of input.
+    zeros : Return a new array setting values to zero.
+    ones : Return a new array setting values to one.
+    empty : Return a new uninitialized array.
+    """
+
+    a = asarray(a, name=False)
+    shape, chunks = _get_like_function_shapes_chunks(a, chunks, shape)
+
+    # if shape is nan we cannot rely on regular zeros function, we use
+    # generic map_blocks.
+    if np.isnan(shape).any():
+        return a.map_blocks(partial(np.zeros_like, dtype=(dtype or a.dtype)))
+
+    return zeros(
+        shape,
+        dtype=(dtype or a.dtype),
+        order=order,
+        chunks=chunks,
+        name=name,
+        meta=a._meta,
+    )
+
+
+def full(shape, fill_value, *args, **kwargs):
+    # np.isscalar has somewhat strange behavior:
+    # https://docs.scipy.org/doc/numpy/reference/generated/numpy.isscalar.html
+    if np.ndim(fill_value) != 0:
+        raise ValueError(
+            f"fill_value must be scalar. Received {type(fill_value).__name__} instead."
+        )
+    if kwargs.get("dtype", None) is None:
+        if hasattr(fill_value, "dtype"):
+            kwargs["dtype"] = fill_value.dtype
+        else:
+            kwargs["dtype"] = type(fill_value)
+    return _full(*args, shape=shape, fill_value=fill_value, **kwargs)
+
+
+def full_like(a, fill_value, order="C", dtype=None, chunks=None, name=None, shape=None):
+    """
+    Return a full array with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : array_like
+        The shape and data-type of `a` define these same attributes of
+        the returned array.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory.
+    chunks : sequence of ints
+        The number of samples on each block. Note that the last block will have
+        fewer samples if ``len(array) % chunks != 0``.
+    name : str, optional
+        An optional keyname for the array. Defaults to hashing the input
+        keyword arguments.
+    shape : int or sequence of ints, optional.
+        Overrides the shape of the result.
+
+    Returns
+    -------
+    out : ndarray
+        Array of `fill_value` with the same shape and type as `a`.
+
+    See Also
+    --------
+    zeros_like : Return an array of zeros with shape and type of input.
+    ones_like : Return an array of ones with shape and type of input.
+    empty_like : Return an empty array with shape and type of input.
+    zeros : Return a new array setting values to zero.
+    ones : Return a new array setting values to one.
+    empty : Return a new uninitialized array.
+    full : Fill a new array.
+    """
+
+    a = asarray(a, name=False)
+    shape, chunks = _get_like_function_shapes_chunks(a, chunks, shape)
+
+    # if shape is nan we cannot rely on regular full function, we use
+    # generic map_blocks.
+    if np.isnan(shape).any():
+        return a.map_blocks(partial(np.full_like, dtype=(dtype or a.dtype)), fill_value)
+
+    return full(
+        shape,
+        fill_value,
+        dtype=(dtype or a.dtype),
+        order=order,
+        chunks=chunks,
+        name=name,
+        meta=a._meta,
+    )
+
+
+@derived_from(np)
+def repeat(a, repeats, axis=None):
+    if axis is None:
+        if a.ndim == 1:
+            axis = 0
+        else:
+            raise NotImplementedError("Must supply an integer axis value")
+
+    if not isinstance(repeats, Integral):
+        raise NotImplementedError("Only integer valued repeats supported")
+
+    if -a.ndim <= axis < 0:
+        axis += a.ndim
+    elif not 0 <= axis <= a.ndim - 1:
+        raise ValueError("axis(=%d) out of bounds" % axis)
+
+    if repeats == 0:
+        return a[tuple(slice(None) if d != axis else slice(0) for d in range(a.ndim))]
+    elif repeats == 1:
+        return a
+
+    cchunks = cached_cumsum(a.chunks[axis], initial_zero=True)
+    slices = []
+    for c_start, c_stop in sliding_window(2, cchunks):
+        ls = np.linspace(c_start, c_stop, repeats).round(0)
+        for ls_start, ls_stop in sliding_window(2, ls):
+            if ls_start != ls_stop:
+                slices.append(slice(ls_start, ls_stop))
+
+    all_slice = slice(None, None, None)
+    slices = [
+        (all_slice,) * axis + (s,) + (all_slice,) * (a.ndim - axis - 1) for s in slices
+    ]
+
+    slabs = [a[slc] for slc in slices]
+
+    out = []
+    for slab in slabs:
+        chunks = list(slab.chunks)
+        assert len(chunks[axis]) == 1
+        chunks[axis] = (chunks[axis][0] * repeats,)
+        chunks = tuple(chunks)
+        result = slab.map_blocks(
+            np.repeat, repeats, axis=axis, chunks=chunks, dtype=slab.dtype
+        )
+        out.append(result)
+
+    return concatenate(out, axis=axis)
diff --git a/dask/array/_array_expr/_map_blocks.py b/dask/array/_array_expr/_map_blocks.py
index f79d2e814..e82e50533 100644
--- a/dask/array/_array_expr/_map_blocks.py
+++ b/dask/array/_array_expr/_map_blocks.py
@@ -480,7 +480,6 @@ def map_blocks(
             None,
             *concat(extra_argpairs),
             *concat(argpairs),
-            name=out.name,
             dtype=out.dtype,
             concatenate=True,
             align_arrays=False,
diff --git a/dask/array/_array_expr/_overlap.py b/dask/array/_array_expr/_overlap.py
new file mode 100644
index 000000000..4a3a2249e
--- /dev/null
+++ b/dask/array/_array_expr/_overlap.py
@@ -0,0 +1,912 @@
+from __future__ import annotations
+
+import functools
+import warnings
+from functools import reduce
+from numbers import Integral, Number
+from operator import mul
+
+import numpy as np
+from tlz import concat, get, partial
+from tlz.curried import map
+
+from dask._collections import new_collection
+from dask.array import chunk
+from dask.array._array_expr._collection import Array, concatenate
+from dask.array._array_expr._creation import empty_like, full_like, repeat
+from dask.array._array_expr._expr import ArrayExpr, unify_chunks_expr
+from dask.array._array_expr._map_blocks import map_blocks
+from dask.array._shuffle import _calculate_new_chunksizes
+from dask.array.numpy_compat import normalize_axis_tuple
+from dask.array.utils import meta_from_array
+from dask.layers import ArrayOverlapLayer
+from dask.utils import derived_from, ensure_dict
+
+
+def _overlap_internal_chunks(original_chunks, axes):
+    """Get new chunks for array with overlap."""
+    chunks = []
+    for i, bds in enumerate(original_chunks):
+        depth = axes.get(i, 0)
+        if isinstance(depth, tuple):
+            left_depth = depth[0]
+            right_depth = depth[1]
+        else:
+            left_depth = depth
+            right_depth = depth
+
+        if len(bds) == 1:
+            chunks.append(bds)
+        else:
+            left = [bds[0] + right_depth]
+            right = [bds[-1] + left_depth]
+            mid = []
+            for bd in bds[1:-1]:
+                mid.append(bd + left_depth + right_depth)
+            chunks.append(left + mid + right)
+    return chunks
+
+
+def overlap_internal(x, axes):
+    """Share boundaries between neighboring blocks
+
+    Parameters
+    ----------
+
+    x: da.Array
+        A dask array
+    axes: dict
+        The size of the shared boundary per axis
+
+    The axes input informs how many cells to overlap between neighboring blocks
+    {0: 2, 2: 5} means share two cells in 0 axis, 5 cells in 2 axis
+    """
+    return new_collection(MapOverlap(x, axes))
+
+
+class MapOverlap(ArrayExpr):
+    _parameters = ["array", "axes"]
+
+    @functools.cached_property
+    def _meta(self):
+        return meta_from_array(self.array)
+
+    @functools.cached_property
+    def chunks(self):
+        return tuple(map(tuple, _overlap_internal_chunks(self.array.chunks, self.axes)))
+
+    @functools.cached_property
+    def _name(self) -> str:
+        return f"overlap-{super()._name}"
+
+    def _layer(self) -> dict:
+        x = self.array
+        graph = ArrayOverlapLayer(
+            name=x.name,
+            axes=self.axes,
+            chunks=x.chunks,
+            numblocks=x.numblocks,
+            token="-".join(self._name.split("-")[1:]),
+        )
+        return ensure_dict(graph)
+
+
+def trim_overlap(x, depth, boundary=None):
+    """Trim sides from each block.
+
+    This couples well with the ``map_overlap`` operation which may leave
+    excess data on each block.
+
+    See also
+    --------
+    dask.array.overlap.map_overlap
+
+    """
+
+    # parameter to be passed to trim_internal
+    axes = coerce_depth(x.ndim, depth)
+    return trim_internal(x, axes=axes, boundary=boundary)
+
+
+def trim_internal(x, axes, boundary=None):
+    """Trim sides from each block
+
+    This couples well with the overlap operation, which may leave excess data on
+    each block
+
+    See also
+    --------
+    dask.array.chunk.trim
+    dask.array.map_blocks
+    """
+    boundary = coerce_boundary(x.ndim, boundary)
+
+    olist = []
+    for i, bd in enumerate(x.chunks):
+        bdy = boundary.get(i, "none")
+        overlap = axes.get(i, 0)
+        ilist = []
+        for j, d in enumerate(bd):
+            if bdy != "none":
+                if isinstance(overlap, tuple):
+                    d = d - sum(overlap)
+                else:
+                    d = d - overlap * 2
+
+            else:
+                if isinstance(overlap, tuple):
+                    d = d - overlap[0] if j != 0 else d
+                    d = d - overlap[1] if j != len(bd) - 1 else d
+                else:
+                    d = d - overlap if j != 0 else d
+                    d = d - overlap if j != len(bd) - 1 else d
+
+            ilist.append(d)
+        olist.append(tuple(ilist))
+    chunks = tuple(olist)
+
+    return map_blocks(
+        partial(_trim, axes=axes, boundary=boundary),
+        x,
+        chunks=chunks,
+        dtype=x.dtype,
+        meta=x._meta,
+    )
+
+
+def _trim(x, axes, boundary, _overlap_trim_info):
+    """Similar to dask.array.chunk.trim but requires one to specify the
+    boundary condition.
+
+    ``axes``, and ``boundary`` are assumed to have been coerced.
+
+    """
+    chunk_location = _overlap_trim_info[0]
+    num_chunks = _overlap_trim_info[1]
+    axes = [axes.get(i, 0) for i in range(x.ndim)]
+    axes_front = (ax[0] if isinstance(ax, tuple) else ax for ax in axes)
+    axes_back = (
+        (
+            -ax[1]
+            if isinstance(ax, tuple) and ax[1]
+            else -ax if isinstance(ax, Integral) and ax else None
+        )
+        for ax in axes
+    )
+
+    trim_front = (
+        0 if (chunk_location == 0 and boundary.get(i, "none") == "none") else ax
+        for i, (chunk_location, ax) in enumerate(zip(chunk_location, axes_front))
+    )
+    trim_back = (
+        (
+            None
+            if (chunk_location == chunks - 1 and boundary.get(i, "none") == "none")
+            else ax
+        )
+        for i, (chunks, chunk_location, ax) in enumerate(
+            zip(num_chunks, chunk_location, axes_back)
+        )
+    )
+    ind = tuple(slice(front, back) for front, back in zip(trim_front, trim_back))
+    return x[ind]
+
+
+def periodic(x, axis, depth):
+    """Copy a slice of an array around to its other side
+
+    Useful to create periodic boundary conditions for overlap
+    """
+
+    left = (
+        (slice(None, None, None),) * axis
+        + (slice(0, depth),)
+        + (slice(None, None, None),) * (x.ndim - axis - 1)
+    )
+    right = (
+        (slice(None, None, None),) * axis
+        + (slice(-depth, None),)
+        + (slice(None, None, None),) * (x.ndim - axis - 1)
+    )
+    l = x[left]
+    r = x[right]
+
+    l, r = _remove_overlap_boundaries(l, r, axis, depth)
+
+    return concatenate([r, x, l], axis=axis)
+
+
+def reflect(x, axis, depth):
+    """Reflect boundaries of array on the same side
+
+    This is the converse of ``periodic``
+    """
+    if depth == 1:
+        left = (
+            (slice(None, None, None),) * axis
+            + (slice(0, 1),)
+            + (slice(None, None, None),) * (x.ndim - axis - 1)
+        )
+    else:
+        left = (
+            (slice(None, None, None),) * axis
+            + (slice(depth - 1, None, -1),)
+            + (slice(None, None, None),) * (x.ndim - axis - 1)
+        )
+    right = (
+        (slice(None, None, None),) * axis
+        + (slice(-1, -depth - 1, -1),)
+        + (slice(None, None, None),) * (x.ndim - axis - 1)
+    )
+    l = x[left]
+    r = x[right]
+
+    l, r = _remove_overlap_boundaries(l, r, axis, depth)
+
+    return concatenate([l, x, r], axis=axis)
+
+
+def nearest(x, axis, depth):
+    """Each reflect each boundary value outwards
+
+    This mimics what the skimage.filters.gaussian_filter(... mode="nearest")
+    does.
+    """
+    left = (
+        (slice(None, None, None),) * axis
+        + (slice(0, 1),)
+        + (slice(None, None, None),) * (x.ndim - axis - 1)
+    )
+    right = (
+        (slice(None, None, None),) * axis
+        + (slice(-1, -2, -1),)
+        + (slice(None, None, None),) * (x.ndim - axis - 1)
+    )
+
+    l = repeat(x[left], depth, axis=axis)
+    r = repeat(x[right], depth, axis=axis)
+
+    l, r = _remove_overlap_boundaries(l, r, axis, depth)
+
+    return concatenate([l, x, r], axis=axis)
+
+
+def constant(x, axis, depth, value):
+    """Add constant slice to either side of array"""
+    chunks = list(x.chunks)
+    chunks[axis] = (depth,)
+
+    c = full_like(
+        x,
+        value,
+        shape=tuple(map(sum, chunks)),
+        chunks=tuple(chunks),
+        dtype=x.dtype,
+    )
+
+    return concatenate([c, x, c], axis=axis)
+
+
+def _remove_overlap_boundaries(l, r, axis, depth):
+    lchunks = list(l.chunks)
+    lchunks[axis] = (depth,)
+    rchunks = list(r.chunks)
+    rchunks[axis] = (depth,)
+
+    l = l.rechunk(tuple(lchunks))
+    r = r.rechunk(tuple(rchunks))
+    return l, r
+
+
+def boundaries(x, depth=None, kind=None):
+    """Add boundary conditions to an array before overlapping
+
+    See Also
+    --------
+    periodic
+    constant
+    """
+    if not isinstance(kind, dict):
+        kind = {i: kind for i in range(x.ndim)}
+    if not isinstance(depth, dict):
+        depth = {i: depth for i in range(x.ndim)}
+
+    for i in range(x.ndim):
+        d = depth.get(i, 0)
+        if d == 0:
+            continue
+
+        this_kind = kind.get(i, "none")
+        if this_kind == "none":
+            continue
+        elif this_kind == "periodic":
+            x = periodic(x, i, d)
+        elif this_kind == "reflect":
+            x = reflect(x, i, d)
+        elif this_kind == "nearest":
+            x = nearest(x, i, d)
+        elif i in kind:
+            x = constant(x, i, d, kind[i])
+
+    return x
+
+
+def ensure_minimum_chunksize(size, chunks):
+    """Determine new chunks to ensure that every chunk >= size
+
+    Parameters
+    ----------
+    size: int
+        The maximum size of any chunk.
+    chunks: tuple
+        Chunks along one axis, e.g. ``(3, 3, 2)``
+
+    Examples
+    --------
+    >>> ensure_minimum_chunksize(10, (20, 20, 1))
+    (20, 11, 10)
+    >>> ensure_minimum_chunksize(3, (1, 1, 3))
+    (5,)
+
+    See Also
+    --------
+    overlap
+    """
+    if size <= min(chunks):
+        return chunks
+
+    # add too-small chunks to chunks before them
+    output = []
+    new = 0
+    for c in chunks:
+        if c < size:
+            if new > size + (size - c):
+                output.append(new - (size - c))
+                new = size
+            else:
+                new += c
+        if new >= size:
+            output.append(new)
+            new = 0
+        if c >= size:
+            new += c
+    if new >= size:
+        output.append(new)
+    elif len(output) >= 1:
+        output[-1] += new
+    else:
+        raise ValueError(
+            f"The overlapping depth {size} is larger than your array {sum(chunks)}."
+        )
+
+    return tuple(output)
+
+
+def _get_overlap_rechunked_chunks(x, depth2):
+    depths = [max(d) if isinstance(d, tuple) else d for d in depth2.values()]
+    # rechunk if new chunks are needed to fit depth in every chunk
+    return tuple(ensure_minimum_chunksize(size, c) for size, c in zip(depths, x.chunks))
+
+
+def overlap(x, depth, boundary, *, allow_rechunk=True):
+    """Share boundaries between neighboring blocks
+
+    Parameters
+    ----------
+
+    x: da.Array
+        A dask array
+    depth: dict
+        The size of the shared boundary per axis
+    boundary: dict
+        The boundary condition on each axis. Options are 'reflect', 'periodic',
+        'nearest', 'none', or an array value.  Such a value will fill the
+        boundary with that value.
+    allow_rechunk: bool, keyword only
+        Allows rechunking, otherwise chunk sizes need to match and core
+        dimensions are to consist only of one chunk.
+
+    The depth input informs how many cells to overlap between neighboring
+    blocks ``{0: 2, 2: 5}`` means share two cells in 0 axis, 5 cells in 2 axis.
+    Axes missing from this input will not be overlapped.
+
+    Any axis containing chunks smaller than depth will be rechunked if
+    possible, provided the keyword ``allow_rechunk`` is True (recommended).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import dask.array as da
+
+    >>> x = np.arange(64).reshape((8, 8))
+    >>> d = da.from_array(x, chunks=(4, 4))
+    >>> d.chunks
+    ((4, 4), (4, 4))
+
+    >>> g = da.overlap.overlap(d, depth={0: 2, 1: 1},
+    ...                       boundary={0: 100, 1: 'reflect'})
+    >>> g.chunks
+    ((8, 8), (6, 6))
+
+    >>> np.array(g)
+    array([[100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
+           [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
+           [  0,   0,   1,   2,   3,   4,   3,   4,   5,   6,   7,   7],
+           [  8,   8,   9,  10,  11,  12,  11,  12,  13,  14,  15,  15],
+           [ 16,  16,  17,  18,  19,  20,  19,  20,  21,  22,  23,  23],
+           [ 24,  24,  25,  26,  27,  28,  27,  28,  29,  30,  31,  31],
+           [ 32,  32,  33,  34,  35,  36,  35,  36,  37,  38,  39,  39],
+           [ 40,  40,  41,  42,  43,  44,  43,  44,  45,  46,  47,  47],
+           [ 16,  16,  17,  18,  19,  20,  19,  20,  21,  22,  23,  23],
+           [ 24,  24,  25,  26,  27,  28,  27,  28,  29,  30,  31,  31],
+           [ 32,  32,  33,  34,  35,  36,  35,  36,  37,  38,  39,  39],
+           [ 40,  40,  41,  42,  43,  44,  43,  44,  45,  46,  47,  47],
+           [ 48,  48,  49,  50,  51,  52,  51,  52,  53,  54,  55,  55],
+           [ 56,  56,  57,  58,  59,  60,  59,  60,  61,  62,  63,  63],
+           [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100],
+           [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100]])
+    """
+    depth2 = coerce_depth(x.ndim, depth)
+    boundary2 = coerce_boundary(x.ndim, boundary)
+
+    depths = [max(d) if isinstance(d, tuple) else d for d in depth2.values()]
+    if allow_rechunk:
+        # rechunk if new chunks are needed to fit depth in every chunk
+        x1 = x.rechunk(
+            _get_overlap_rechunked_chunks(x, depth2)
+        )  # this is a no-op if x.chunks == new_chunks
+
+    else:
+        original_chunks_too_small = any([min(c) < d for d, c in zip(depths, x.chunks)])
+        if original_chunks_too_small:
+            raise ValueError(
+                "Overlap depth is larger than smallest chunksize.\n"
+                "Please set allow_rechunk=True to rechunk automatically.\n"
+                f"Overlap depths required: {depths}\n"
+                f"Input chunks: {x.chunks}\n"
+            )
+        x1 = x
+
+    x2 = boundaries(x1, depth2, boundary2)
+    x3 = overlap_internal(x2, depth2)
+    trim = {
+        k: v * 2 if boundary2.get(k, "none") != "none" else 0 for k, v in depth2.items()
+    }
+    x4 = chunk.trim(x3, trim)
+    return x4
+
+
+def add_dummy_padding(x, depth, boundary):
+    """
+    Pads an array which has 'none' as the boundary type.
+    Used to simplify trimming arrays which use 'none'.
+
+    >>> import dask.array as da
+    >>> x = da.arange(6, chunks=3)
+    >>> add_dummy_padding(x, {0: 1}, {0: 'none'}).compute()  # doctest: +NORMALIZE_WHITESPACE
+    array([..., 0, 1, 2, 3, 4, 5, ...])
+    """
+    for k, v in boundary.items():
+        d = depth.get(k, 0)
+        if v == "none" and d > 0:
+            empty_shape = list(x.shape)
+            empty_shape[k] = d
+
+            empty_chunks = list(x.chunks)
+            empty_chunks[k] = (d,)
+
+            empty = empty_like(
+                getattr(x, "_meta", x),
+                shape=empty_shape,
+                chunks=empty_chunks,
+                dtype=x.dtype,
+            )
+
+            out_chunks = list(x.chunks)
+            ax_chunks = list(out_chunks[k])
+            ax_chunks[0] += d
+            ax_chunks[-1] += d
+            out_chunks[k] = tuple(ax_chunks)
+
+            x = concatenate([empty, x, empty], axis=k)
+            x = x.rechunk(out_chunks)
+    return x
+
+
+def map_overlap(
+    func,
+    *args,
+    depth=None,
+    boundary=None,
+    trim=True,
+    align_arrays=True,
+    allow_rechunk=True,
+    **kwargs,
+):
+    """Map a function over blocks of arrays with some overlap
+
+    We share neighboring zones between blocks of the array, map a
+    function, and then trim away the neighboring strips. If depth is
+    larger than any chunk along a particular axis, then the array is
+    rechunked.
+
+    Note that this function will attempt to automatically determine the output
+    array type before computing it, please refer to the ``meta`` keyword argument
+    in ``map_blocks`` if you expect that the function will not succeed when
+    operating on 0-d arrays.
+
+    Parameters
+    ----------
+    func: function
+        The function to apply to each extended block.
+        If multiple arrays are provided, then the function should expect to
+        receive chunks of each array in the same order.
+    args : dask arrays
+    depth: int, tuple, dict or list, keyword only
+        The number of elements that each block should share with its neighbors
+        If a tuple or dict then this can be different per axis.
+        If a list then each element of that list must be an int, tuple or dict
+        defining depth for the corresponding array in `args`.
+        Asymmetric depths may be specified using a dict value of (-/+) tuples.
+        Note that asymmetric depths are currently only supported when
+        ``boundary`` is 'none'.
+        The default value is 0.
+    boundary: str, tuple, dict or list, keyword only
+        How to handle the boundaries.
+        Values include 'reflect', 'periodic', 'nearest', 'none',
+        or any constant value like 0 or np.nan.
+        If a list then each element must be a str, tuple or dict defining the
+        boundary for the corresponding array in `args`.
+        The default value is 'reflect'.
+    trim: bool, keyword only
+        Whether or not to trim ``depth`` elements from each block after
+        calling the map function.
+        Set this to False if your mapping function already does this for you
+    align_arrays: bool, keyword only
+        Whether or not to align chunks along equally sized dimensions when
+        multiple arrays are provided.  This allows for larger chunks in some
+        arrays to be broken into smaller ones that match chunk sizes in other
+        arrays such that they are compatible for block function mapping. If
+        this is false, then an error will be thrown if arrays do not already
+        have the same number of blocks in each dimension.
+    allow_rechunk: bool, keyword only
+        Allows rechunking, otherwise chunk sizes need to match and core
+        dimensions are to consist only of one chunk.
+    **kwargs:
+        Other keyword arguments valid in ``map_blocks``
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import dask.array as da
+
+    >>> x = np.array([1, 1, 2, 3, 3, 3, 2, 1, 1])
+    >>> x = da.from_array(x, chunks=5)
+    >>> def derivative(x):
+    ...     return x - np.roll(x, 1)
+
+    >>> y = x.map_overlap(derivative, depth=1, boundary=0)
+    >>> y.compute()
+    array([ 1,  0,  1,  1,  0,  0, -1, -1,  0])
+
+    >>> x = np.arange(16).reshape((4, 4))
+    >>> d = da.from_array(x, chunks=(2, 2))
+    >>> d.map_overlap(lambda x: x + x.size, depth=1, boundary='reflect').compute()
+    array([[16, 17, 18, 19],
+           [20, 21, 22, 23],
+           [24, 25, 26, 27],
+           [28, 29, 30, 31]])
+
+    >>> func = lambda x: x + x.size
+    >>> depth = {0: 1, 1: 1}
+    >>> boundary = {0: 'reflect', 1: 'none'}
+    >>> d.map_overlap(func, depth, boundary).compute()  # doctest: +NORMALIZE_WHITESPACE
+    array([[12,  13,  14,  15],
+           [16,  17,  18,  19],
+           [20,  21,  22,  23],
+           [24,  25,  26,  27]])
+
+    The ``da.map_overlap`` function can also accept multiple arrays.
+
+    >>> func = lambda x, y: x + y
+    >>> x = da.arange(8).reshape(2, 4).rechunk((1, 2))
+    >>> y = da.arange(4).rechunk(2)
+    >>> da.map_overlap(func, x, y, depth=1, boundary='reflect').compute() # doctest: +NORMALIZE_WHITESPACE
+    array([[ 0,  2,  4,  6],
+           [ 4,  6,  8,  10]])
+
+    When multiple arrays are given, they do not need to have the
+    same number of dimensions but they must broadcast together.
+    Arrays are aligned block by block (just as in ``da.map_blocks``)
+    so the blocks must have a common chunk size.  This common chunking
+    is determined automatically as long as ``align_arrays`` is True.
+
+    >>> x = da.arange(8, chunks=4)
+    >>> y = da.arange(8, chunks=2)
+    >>> r = da.map_overlap(func, x, y, depth=1, boundary='reflect', align_arrays=True)
+    >>> len(r.to_delayed())
+    4
+
+    >>> da.map_overlap(func, x, y, depth=1, boundary='reflect', align_arrays=False).compute()
+    Traceback (most recent call last):
+        ...
+    ValueError: Shapes do not align {'.0': {2, 4}}
+
+    Note also that this function is equivalent to ``map_blocks``
+    by default.  A non-zero ``depth`` must be defined for any
+    overlap to appear in the arrays provided to ``func``.
+
+    >>> func = lambda x: x.sum()
+    >>> x = da.ones(10, dtype='int')
+    >>> block_args = dict(chunks=(), drop_axis=0)
+    >>> da.map_blocks(func, x, **block_args).compute()
+    np.int64(10)
+    >>> da.map_overlap(func, x, **block_args, boundary='reflect').compute()
+    np.int64(10)
+    >>> da.map_overlap(func, x, **block_args, depth=1, boundary='reflect').compute()
+    np.int64(12)
+
+    For functions that may not handle 0-d arrays, it's also possible to specify
+    ``meta`` with an empty array matching the type of the expected result. In
+    the example below, ``func`` will result in an ``IndexError`` when computing
+    ``meta``:
+
+    >>> x = np.arange(16).reshape((4, 4))
+    >>> d = da.from_array(x, chunks=(2, 2))
+    >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=np.array(()))
+    >>> y
+    dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=numpy.ndarray>
+    >>> y.compute()
+    array([[ 4,  6,  8, 10],
+           [ 8, 10, 12, 14],
+           [20, 22, 24, 26],
+           [24, 26, 28, 30]])
+
+    Similarly, it's possible to specify a non-NumPy array to ``meta``:
+
+    >>> import cupy  # doctest: +SKIP
+    >>> x = cupy.arange(16).reshape((4, 4))  # doctest: +SKIP
+    >>> d = da.from_array(x, chunks=(2, 2))  # doctest: +SKIP
+    >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=cupy.array(()))  # doctest: +SKIP
+    >>> y  # doctest: +SKIP
+    dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=cupy.ndarray>
+    >>> y.compute()  # doctest: +SKIP
+    array([[ 4,  6,  8, 10],
+           [ 8, 10, 12, 14],
+           [20, 22, 24, 26],
+           [24, 26, 28, 30]])
+    """
+    # Look for invocation using deprecated single-array signature
+    # map_overlap(x, func, depth, boundary=None, trim=True, **kwargs)
+    if isinstance(func, Array) and callable(args[0]):
+        warnings.warn(
+            "The use of map_overlap(array, func, **kwargs) is deprecated since dask 2.17.0 "
+            "and will be an error in a future release. To silence this warning, use the syntax "
+            "map_overlap(func, array0,[ array1, ...,] **kwargs) instead.",
+            FutureWarning,
+        )
+        sig = ["func", "depth", "boundary", "trim"]
+        depth = get(sig.index("depth"), args, depth)
+        boundary = get(sig.index("boundary"), args, boundary)
+        trim = get(sig.index("trim"), args, trim)
+        func, args = args[0], [func]
+
+    if not callable(func):
+        raise TypeError(
+            "First argument must be callable function, not {}\n"
+            "Usage:   da.map_overlap(function, x)\n"
+            "   or:   da.map_overlap(function, x, y, z)".format(type(func).__name__)
+        )
+    if not all(isinstance(x, Array) for x in args):
+        raise TypeError(
+            "All variadic arguments must be arrays, not {}\n"
+            "Usage:   da.map_overlap(function, x)\n"
+            "   or:   da.map_overlap(function, x, y, z)".format(
+                [type(x).__name__ for x in args]
+            )
+        )
+
+    # Coerce depth and boundary arguments to lists of individual
+    # specifications for each array argument
+    def coerce(xs, arg, fn):
+        if not isinstance(arg, list):
+            arg = [arg] * len(xs)
+        return [fn(x.ndim, a) for x, a in zip(xs, arg)]
+
+    depth = coerce(args, depth, coerce_depth)
+    boundary = coerce(args, boundary, coerce_boundary)
+
+    # Align chunks in each array to a common size
+    if align_arrays:
+        # Reverse unification order to allow block broadcasting
+        inds = [list(reversed(range(x.ndim))) for x in args]
+        args = [a.expr for a in args]
+        _, args, _ = unify_chunks_expr(*list(concat(zip(args, inds))))
+        args = [new_collection(a) for a in args]
+
+    # Escape to map_blocks if depth is zero (a more efficient computation)
+    if all([all(depth_val == 0 for depth_val in d.values()) for d in depth]):
+        return map_blocks(func, *args, **kwargs)
+
+    for i, x in enumerate(args):
+        for j in range(x.ndim):
+            if isinstance(depth[i][j], tuple) and boundary[i][j] != "none":
+                raise NotImplementedError(
+                    "Asymmetric overlap is currently only implemented "
+                    "for boundary='none', however boundary for dimension "
+                    "{} in array argument {} is {}".format(j, i, boundary[i][j])
+                )
+
+    def assert_int_chunksize(xs):
+        assert all(type(c) is int for x in xs for cc in x.chunks for c in cc)
+
+    assert_int_chunksize(args)
+    if not trim and "chunks" not in kwargs:
+        if allow_rechunk:
+            # Adjust chunks based on the rechunking result
+            kwargs["chunks"] = _get_overlap_rechunked_chunks(
+                args[0], coerce_depth(args[0].ndim, depth[0])
+            )
+        else:
+            kwargs["chunks"] = args[0].chunks
+    args = [
+        overlap(x, depth=d, boundary=b, allow_rechunk=allow_rechunk)
+        for x, d, b in zip(args, depth, boundary)
+    ]
+    assert_int_chunksize(args)
+    x = map_blocks(func, *args, **kwargs)
+    assert_int_chunksize([x])
+    if trim:
+        # Find index of array argument with maximum rank and break ties by choosing first provided
+        i = sorted(enumerate(args), key=lambda v: (v[1].ndim, -v[0]))[-1][0]
+        # Trim using depth/boundary setting for array of highest rank
+        depth = depth[i]
+        boundary = boundary[i]
+        # remove any dropped axes from depth and boundary variables
+        drop_axis = kwargs.pop("drop_axis", None)
+        if drop_axis is not None:
+            if isinstance(drop_axis, Number):
+                drop_axis = [drop_axis]
+
+            # convert negative drop_axis to equivalent positive value
+            ndim_out = max(a.ndim for a in args if isinstance(a, Array))
+            drop_axis = [d % ndim_out for d in drop_axis]
+
+            kept_axes = tuple(ax for ax in range(args[i].ndim) if ax not in drop_axis)
+            # note that keys are relabeled to match values in range(x.ndim)
+            depth = {n: depth[ax] for n, ax in enumerate(kept_axes)}
+            boundary = {n: boundary[ax] for n, ax in enumerate(kept_axes)}
+
+        # add any new axes to depth and boundary variables
+        new_axis = kwargs.pop("new_axis", None)
+        if new_axis is not None:
+            if isinstance(new_axis, Number):
+                new_axis = [new_axis]
+
+            # convert negative new_axis to equivalent positive value
+            ndim_out = max(a.ndim for a in args if isinstance(a, Array))
+            new_axis = [d % ndim_out for d in new_axis]
+
+            for axis in new_axis:
+                for existing_axis in list(depth.keys()):
+                    if existing_axis >= axis:
+                        # Shuffle existing axis forward to give room to insert new_axis
+                        depth[existing_axis + 1] = depth[existing_axis]
+                        boundary[existing_axis + 1] = boundary[existing_axis]
+
+                depth[axis] = 0
+                boundary[axis] = "none"
+
+        return trim_internal(x, depth, boundary)
+    else:
+        return x
+
+
+def coerce_depth(ndim, depth):
+    default = 0
+    if depth is None:
+        depth = default
+    if isinstance(depth, Integral):
+        depth = (depth,) * ndim
+    if isinstance(depth, tuple):
+        depth = dict(zip(range(ndim), depth))
+    if isinstance(depth, dict):
+        depth = {ax: depth.get(ax, default) for ax in range(ndim)}
+    return coerce_depth_type(ndim, depth)
+
+
+def coerce_depth_type(ndim, depth):
+    for i in range(ndim):
+        if isinstance(depth[i], tuple):
+            depth[i] = tuple(int(d) for d in depth[i])
+        else:
+            depth[i] = int(depth[i])
+    return depth
+
+
+def coerce_boundary(ndim, boundary):
+    default = "none"
+    if boundary is None:
+        boundary = default
+    if not isinstance(boundary, (tuple, dict)):
+        boundary = (boundary,) * ndim
+    if isinstance(boundary, tuple):
+        boundary = dict(zip(range(ndim), boundary))
+    if isinstance(boundary, dict):
+        boundary = {ax: boundary.get(ax, default) for ax in range(ndim)}
+    return boundary
+
+
+@derived_from(np.lib.stride_tricks)
+def sliding_window_view(x, window_shape, axis=None, automatic_rechunk=True):
+    window_shape = tuple(window_shape) if np.iterable(window_shape) else (window_shape,)
+
+    window_shape_array = np.array(window_shape)
+    if np.any(window_shape_array <= 0):
+        raise ValueError("`window_shape` must contain values > 0")
+
+    if axis is None:
+        axis = tuple(range(x.ndim))
+        if len(window_shape) != len(axis):
+            raise ValueError(
+                f"Since axis is `None`, must provide "
+                f"window_shape for all dimensions of `x`; "
+                f"got {len(window_shape)} window_shape elements "
+                f"and `x.ndim` is {x.ndim}."
+            )
+    else:
+        axis = normalize_axis_tuple(axis, x.ndim, allow_duplicate=True)
+        if len(window_shape) != len(axis):
+            raise ValueError(
+                f"Must provide matching length window_shape and "
+                f"axis; got {len(window_shape)} window_shape "
+                f"elements and {len(axis)} axes elements."
+            )
+
+    depths = [0] * x.ndim
+    for ax, window in zip(axis, window_shape):
+        depths[ax] += window - 1
+
+    # Ensure that each chunk is big enough to leave at least a size-1 chunk
+    # after windowing (this is only really necessary for the last chunk).
+    safe_chunks = list(
+        ensure_minimum_chunksize(d + 1, c) for d, c in zip(depths, x.chunks)
+    )
+    if automatic_rechunk:
+        safe_chunks = [
+            s if d != 0 else c for d, c, s in zip(depths, x.chunks, safe_chunks)
+        ]
+        # safe chunks is our output chunks, so add the new dimensions
+        safe_chunks.extend([(w,) for w in window_shape])
+        max_chunk = reduce(mul, map(max, x.chunks))
+        new_chunks = _calculate_new_chunksizes(
+            x.chunks,
+            safe_chunks.copy(),
+            {i for i, d in enumerate(depths) if d == 0},
+            max_chunk,
+        )
+        x = x.rechunk(tuple(new_chunks))
+    else:
+        x = x.rechunk(tuple(safe_chunks))
+
+    # result.shape = x_shape_trimmed + window_shape,
+    # where x_shape_trimmed is x.shape with every entry
+    # reduced by one less than the corresponding window size.
+    # trim chunks to match x_shape_trimmed
+    newchunks = tuple(c[:-1] + (c[-1] - d,) for d, c in zip(depths, x.chunks)) + tuple(
+        (window,) for window in window_shape
+    )
+
+    return map_overlap(
+        np.lib.stride_tricks.sliding_window_view,
+        x,
+        depth=tuple((0, d) for d in depths),  # Overlap on +ve side only
+        boundary="none",
+        meta=x._meta,
+        new_axis=range(x.ndim, x.ndim + len(axis)),
+        chunks=newchunks,
+        trim=False,
+        align_arrays=False,
+        window_shape=window_shape,
+        axis=axis,
+    )
diff --git a/pyproject.toml b/pyproject.toml
index f17b7cb68..f84ed1add 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -124,6 +124,7 @@ markers = [
     "skip_with_pyarrow_strings: Tests that should be skipped when pyarrow string conversion is turned on",
     "xfail_with_pyarrow_strings: Tests that should be xfailed when pyarrow string conversion is turned on",
     "array_expr: Tests that are array-expr specific",
+    "normal_and_array_expr: Tests that should run with and without array-expr",
 ]
 addopts = "-v -rsfE --durations=10 --color=yes --cov-config=pyproject.toml"
 filterwarnings = [


Test patch:
diff --git a/conftest.py b/conftest.py
index 3125e6115..fd48df2a0 100644
--- a/conftest.py
+++ b/conftest.py
@@ -65,7 +65,8 @@ def pytest_runtest_setup(item):
     if "array_expr" in item.keywords and not item.config.getoption("--runarrayexpr"):
         pytest.skip("need --runarrayexpr option to run")
     elif "array_expr" not in item.keywords and item.config.getoption("--runarrayexpr"):
-        pytest.skip("only array-expr tests are being run")
+        if "normal_and_array_expr" not in item.keywords:
+            pytest.skip("only array-expr tests are being run")
 
 
 def pytest_assertrepr_compare(op, left, right):
diff --git a/dask/array/tests/test_overlap.py b/dask/array/tests/test_overlap.py
index f588ca832..e29c7050b 100644
--- a/dask/array/tests/test_overlap.py
+++ b/dask/array/tests/test_overlap.py
@@ -8,24 +8,42 @@ from dask.base import collections_to_expr
 pytest.importorskip("numpy")
 
 import numpy as np
-from numpy.testing import assert_array_almost_equal, assert_array_equal
+from numpy.testing import assert_array_almost_equal
 
 import dask.array as da
-from dask.array.lib.stride_tricks import sliding_window_view
-from dask.array.overlap import (
-    boundaries,
-    constant,
-    ensure_minimum_chunksize,
-    nearest,
-    overlap,
-    overlap_internal,
-    periodic,
-    push,
-    reflect,
-    trim_internal,
-)
+
+if da._array_expr_enabled():
+    from dask.array._array_expr._overlap import (
+        boundaries,
+        constant,
+        ensure_minimum_chunksize,
+        nearest,
+        overlap,
+        overlap_internal,
+        periodic,
+        reflect,
+        sliding_window_view,
+        trim_internal,
+    )
+else:
+    from dask.array.lib.stride_tricks import sliding_window_view
+    from dask.array.overlap import (
+        boundaries,
+        constant,
+        ensure_minimum_chunksize,
+        nearest,
+        overlap,
+        overlap_internal,
+        periodic,
+        push,
+        reflect,
+        trim_internal,
+    )
+
 from dask.array.utils import assert_eq, same_keys
 
+pytestmark = pytest.mark.normal_and_array_expr
+
 
 def test_overlap_internal():
     x = np.arange(64).reshape((8, 8))
@@ -122,6 +140,7 @@ def test_overlap_internal_asymmetric_small():
     assert same_keys(overlap_internal(d, {0: (0, 0), 1: (1, 1)}), result)
 
 
+@pytest.mark.xfail(da._array_expr_enabled(), reason="blockwise fusion needed")  # type: ignore
 def test_trim_internal():
     d = da.ones((40, 60), chunks=(10, 10))
     e = trim_internal(d, axes={0: 1, 1: 2}, boundary="reflect")
@@ -355,6 +374,7 @@ def test_map_overlap_no_depth(boundary):
     assert_eq(y, x)
 
 
+@pytest.mark.xfail(da._array_expr_enabled(), reason="reshape needed")  # type: ignore
 def test_map_overlap_multiarray():
     # Same ndim, same numblocks, same chunks
     x = da.arange(10, chunks=5)
@@ -460,6 +480,7 @@ def test_map_overlap_multiarray_block_broadcast():
     assert_eq(z.sum(), 4.0 * (10 * 8 + 8))
 
 
+@pytest.mark.xfail(da._array_expr_enabled(), reason="__array_function__ needed")  # type: ignore
 def test_map_overlap_multiarray_variadic():
     # Test overlapping row slices from 3D arrays
     xs = [
@@ -526,7 +547,7 @@ def test_map_overlap_trim_using_drop_axis_and_different_depths(drop_axis):
     y = da.map_overlap(
         _mean, x, depth=depth, boundary=boundary, drop_axis=drop_axis, dtype=float
     ).compute()
-    assert_array_almost_equal(expected, y)
+    assert_array_almost_equal(expected.compute(), y)
 
 
 def test_map_overlap_assumes_shape_matches_first_array_if_trim_is_false():
@@ -585,7 +606,7 @@ def test_nearest_overlap():
     darr = da.from_array(a, chunks=(6, 6))
     garr = overlap(darr, depth={0: 5, 1: 5}, boundary={0: "nearest", 1: "nearest"})
     tarr = trim_internal(garr, {0: 5, 1: 5}, boundary="nearest")
-    assert_array_almost_equal(tarr, a)
+    assert_eq(tarr, a)
 
 
 @pytest.mark.parametrize(
@@ -607,16 +628,16 @@ def test_different_depths_and_boundary_combinations(depth):
     constant = overlap(darr, depth=depth, boundary=42)
 
     result = trim_internal(reflected, depth, boundary="reflect")
-    assert_array_equal(result, expected)
+    assert_eq(result, expected)
 
     result = trim_internal(nearest, depth, boundary="nearest")
-    assert_array_equal(result, expected)
+    assert_eq(result, expected)
 
     result = trim_internal(periodic, depth, boundary="periodic")
-    assert_array_equal(result, expected)
+    assert_eq(result, expected)
 
     result = trim_internal(constant, depth, boundary=42)
-    assert_array_equal(result, expected)
+    assert_eq(result, expected)
 
 
 def test_one_chunk_along_axis():
@@ -738,6 +759,7 @@ def test_overlap_few_dimensions():
     assert len(c.dask) < 10 * len(a.dask)
 
 
+@pytest.mark.xfail(da._array_expr_enabled(), reason="push needed")  # type: ignore
 def test_push():
     bottleneck = pytest.importorskip("bottleneck")
 
@@ -761,15 +783,15 @@ def test_trim_boundary(boundary):
     x_trimmed = da.overlap.trim_overlap(
         x_overlaped, 2, boundary={0: "reflect", 1: boundary}
     )
-    assert np.all(x == x_trimmed)
+    assert_eq(x, x_trimmed)
 
     x_overlaped = da.overlap.overlap(x, 2, boundary={1: boundary})
     x_trimmed = da.overlap.trim_overlap(x_overlaped, 2, boundary={1: boundary})
-    assert np.all(x == x_trimmed)
+    assert_eq(x, x_trimmed)
 
     x_overlaped = da.overlap.overlap(x, 2, boundary=boundary)
     x_trimmed = da.overlap.trim_overlap(x_overlaped, 2, boundary=boundary)
-    assert np.all(x == x_trimmed)
+    assert_eq(x, x_trimmed)
 
 
 def test_map_overlap_rechunks_array_if_needed():
@@ -952,6 +974,7 @@ def test_map_overlap_new_axis():
     assert_eq(expected, actual, check_shape=False, check_chunks=False)
 
 
+@pytest.mark.xfail(da._array_expr_enabled(), reason="xarray doesn't work yet")  # type: ignore
 def test_overlap_not_blowing_up_graph():
     xr = pytest.importorskip("xarray")