ENH: Dask: sort and argsort

array_api_compat/dask/array/_aliases.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
-from ...common import _aliases
+from typing import Callable
+
+from ...common import _aliases, array_namespace
 
 from ..._internal import get_xp
 
@@ -29,24 +31,32 @@
 )
 
 from typing import TYPE_CHECKING
+
 if TYPE_CHECKING:
     from typing import Optional, Union
 
-    from ...common._typing import Device, Dtype, Array, NestedSequence, SupportsBufferProtocol
+    from ...common._typing import (
+        Device,
+        Dtype,
+        Array,
+        NestedSequence,
+        SupportsBufferProtocol,
+    )
 
 import dask.array as da
 
 isdtype = get_xp(np)(_aliases.isdtype)
 unstack = get_xp(da)(_aliases.unstack)
 
+
 # da.astype doesn't respect copy=True
 def astype(
     x: Array,
     dtype: Dtype,
     /,
     *,
     copy: bool = True,
-    device: Optional[Device] = None
+    device: Optional[Device] = None,
 ) -> Array:
     """
     Array API compatibility wrapper for astype().
@@ -61,8 +71,10 @@ def astype(
     x = x.astype(dtype)
     return x.copy() if copy else x
 
+
 # Common aliases
 
+
 # This arange func is modified from the common one to
 # not pass stop/step as keyword arguments, which will cause
 # an error with dask
@@ -189,6 +201,7 @@ def asarray(
     concatenate as concat,
 )
 
+
 # dask.array.clip does not work unless all three arguments are provided.
 # Furthermore, the masking workaround in common._aliases.clip cannot work with
 # dask (meaning uint64 promoting to float64 is going to just be unfixed for
@@ -205,8 +218,10 @@ def clip(
     See the corresponding documentation in the array library and/or the array API
     specification for more details.
     """
+
     def _isscalar(a):
         return isinstance(a, (int, float, type(None)))
+
     min_shape = () if _isscalar(min) else min.shape
     max_shape = () if _isscalar(max) else max.shape
 
@@ -228,10 +243,98 @@ def _isscalar(a):
 
     return astype(da.minimum(da.maximum(x, min), max), x.dtype)
 
-# exclude these from all since dask.array has no sorting functions
-_da_unsupported = ['sort', 'argsort']
 
-_common_aliases = [alias for alias in _aliases.__all__ if alias not in _da_unsupported]
+def _ensure_single_chunk(x: Array, axis: int) -> tuple[Array, Callable[[Array], Array]]:
+    """
+    Make sure that Array is not broken into multiple chunks along axis.
+
+    Returns
+    -------
+    x : Array
+        The input Array with a single chunk along axis.
+    restore : Callable[Array, Array]
+        function to apply to the output to rechunk it back into reasonable chunks
+    """
+    if axis < 0:
+        axis += x.ndim
+    if x.numblocks[axis] < 2:
+        return x, lambda x: x
+
+    # Break chunks on other axes in an attempt to keep chunk size low
+    x = x.rechunk({i: -1 if i == axis else "auto" for i in range(x.ndim)})
+
+    # Rather than reconstructing the original chunks, which can be a
+    # very expensive affair, just break down oversized chunks without
+    # incurring in any transfers over the network.
+    # This has the downside of a risk of overchunking if the array is
+    # then used in operations against other arrays that match the
+    # original chunking pattern.
+    return x, lambda x: x.rechunk()
+
+
+def sort(
+    x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
+) -> Array:
+    """
+    Array API compatibility layer around the lack of sort() in Dask.
+
+    Warnings
+    --------
+    This function temporarily rechunks the array along `axis` to a single chunk.
+    This can be extremely inefficient and can lead to out-of-memory errors.
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+    """
+    x, restore = _ensure_single_chunk(x, axis)
+
+    meta_xp = array_namespace(x._meta)
+    x = da.map_blocks(
+        meta_xp.sort,
+        x,
+        axis=axis,
+        meta=x._meta,
+        dtype=x.dtype,
+        descending=descending,
+        stable=stable,
+    )
+
+    return restore(x)
+
+
+def argsort(
+    x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True
+) -> Array:
+    """
+    Array API compatibility layer around the lack of argsort() in Dask.
+
+    See the corresponding documentation in the array library and/or the array API
+    specification for more details.
+
+    Warnings
+    --------
+    This function temporarily rechunks the array along `axis` into a single chunk.
+    This can be extremely inefficient and can lead to out-of-memory errors.
+    """
+    x, restore = _ensure_single_chunk(x, axis)
+
+    meta_xp = array_namespace(x._meta)
+    dtype = meta_xp.argsort(x._meta).dtype
+    meta = meta_xp.astype(x._meta, dtype)
+    x = da.map_blocks(
+        meta_xp.argsort,
+        x,
+        axis=axis,
+        meta=meta,
+        dtype=dtype,
+        descending=descending,
+        stable=stable,
+    )
+
+    return restore(x)
+
+
+_common_aliases = _aliases.__all__
 
 __all__ = _common_aliases + ['__array_namespace_info__', 'asarray', 'acos',
                     'acosh', 'asin', 'asinh', 'atan', 'atan2',
@@ -242,4 +345,4 @@ def _isscalar(a):
                     'complex64', 'complex128', 'iinfo', 'finfo',
                     'can_cast', 'result_type']
 
-_all_ignore = ["get_xp", "da", "np"]
+_all_ignore = ["Callable", "array_namespace", "get_xp", "da", "np"]

dask-xfails.txt

@@ -23,17 +23,13 @@ array_api_tests/test_array_object.py::test_setitem_masking
 # Various indexing errors
 array_api_tests/test_array_object.py::test_getitem_masking
 
-# asarray(copy=False) is not yet implemented
-# copied from numpy xfails, TODO: should this pass with dask?
-array_api_tests/test_creation_functions.py::test_asarray_arrays
-
 # zero division error, and typeerror: tuple indices must be integers or slices not tuple
 array_api_tests/test_creation_functions.py::test_eye
 
 # finfo(float32).eps returns float32 but should return float
 array_api_tests/test_data_type_functions.py::test_finfo[float32]
 
-# out[-1]=dask.aray<getitem ...> but should be some floating number
+# out[-1]=dask.array<getitem ...> but should be some floating number
 # (I think the test is not forcing the op to be computed?)
 array_api_tests/test_creation_functions.py::test_linspace
 
@@ -48,15 +44,7 @@ array_api_tests/test_special_cases.py::test_iop[__ipow__(x1_i is -0 and x2_i > 0
 array_api_tests/test_special_cases.py::test_iop[__ipow__(x1_i is -infinity and x2_i > 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +infinity]
 array_api_tests/test_special_cases.py::test_binary[__pow__(x1_i is -infinity and x2_i > 0 and not (x2_i.is_integer() and x2_i % 2 == 1)) -> +infinity]
 
-# No sorting in dask
-array_api_tests/test_has_names.py::test_has_names[sorting-argsort]
-array_api_tests/test_has_names.py::test_has_names[sorting-sort]
-array_api_tests/test_sorting_functions.py::test_argsort
-array_api_tests/test_sorting_functions.py::test_sort
-array_api_tests/test_signatures.py::test_func_signature[argsort]
-array_api_tests/test_signatures.py::test_func_signature[sort]
-
-# Array methods and attributes not already on np.ndarray cannot be wrapped
+# Array methods and attributes not already on da.Array cannot be wrapped
 array_api_tests/test_has_names.py::test_has_names[array_method-__array_namespace__]
 array_api_tests/test_has_names.py::test_has_names[array_method-to_device]
 array_api_tests/test_has_names.py::test_has_names[array_attribute-device]
@@ -76,6 +64,7 @@ array_api_tests/test_set_functions.py::test_unique_values
 # fails for ndim > 2
 array_api_tests/test_linalg.py::test_svdvals
 array_api_tests/test_linalg.py::test_cholesky
+
 # dtype mismatch got uint64, but should be uint8, NPY_PROMOTION_STATE=weak doesn't help :(
 array_api_tests/test_linalg.py::test_tensordot
 
@@ -105,6 +94,8 @@ array_api_tests/test_linalg.py::test_cross
 array_api_tests/test_linalg.py::test_det
 array_api_tests/test_linalg.py::test_eigh
 array_api_tests/test_linalg.py::test_eigvalsh
+array_api_tests/test_linalg.py::test_matrix_norm
+array_api_tests/test_linalg.py::test_matrix_rank
 array_api_tests/test_linalg.py::test_pinv
 array_api_tests/test_linalg.py::test_slogdet
 array_api_tests/test_has_names.py::test_has_names[linalg-cross]
@@ -115,9 +106,6 @@ array_api_tests/test_has_names.py::test_has_names[linalg-matrix_power]
 array_api_tests/test_has_names.py::test_has_names[linalg-pinv]
 array_api_tests/test_has_names.py::test_has_names[linalg-slogdet]
 
-array_api_tests/test_linalg.py::test_matrix_norm
-array_api_tests/test_linalg.py::test_matrix_rank
-
 # missing mode kw
 # https://github.com/dask/dask/issues/10388
 array_api_tests/test_linalg.py::test_qr

tests/test_dask.py

@@ -1,5 +1,6 @@
 from contextlib import contextmanager
 
+import array_api_strict
 import dask
 import numpy as np
 import pytest
@@ -20,9 +21,10 @@ def assert_no_compute():
     Context manager that raises if at any point inside it anything calls compute()
     or persist(), e.g. as it can be triggered implicitly by __bool__, __array__, etc.
     """
+
     def get(dsk, *args, **kwargs):
         raise AssertionError("Called compute() or persist()")
-    
+
     with dask.config.set(scheduler=get):
         yield
 
@@ -40,6 +42,7 @@ def test_assert_no_compute():
 
 # Test no_compute for functions that use generic _aliases with xp=np
 
+
 def test_unary_ops_no_compute(xp):
     with assert_no_compute():
         a = xp.asarray([1.5, -1.5])
@@ -59,6 +62,7 @@ def test_matmul_tensordot_no_compute(xp):
 
 # Test no_compute for functions that are fully bespoke for dask
 
+
 def test_asarray_no_compute(xp):
     with assert_no_compute():
         a = xp.arange(10)
@@ -88,6 +92,14 @@ def test_clip_no_compute(xp):
         xp.clip(a, 1, 8)
 
 
+@pytest.mark.parametrize("chunks", (5, 10))
+def test_sort_argsort_nocompute(xp, chunks):
+    with assert_no_compute():
+        a = xp.arange(10, chunks=chunks)
+        xp.sort(a)
+        xp.argsort(a)
+
+
 def test_generators_are_lazy(xp):
     """
     Test that generator functions are fully lazy, e.g. that
@@ -106,3 +118,62 @@ def test_generators_are_lazy(xp):
         xp.ones_like(a)
         xp.empty_like(a)
         xp.full_like(a, fill_value=123)
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize("func", ["sort", "argsort"])
+def test_sort_argsort_chunks(xp, func, axis):
+    """Test that sort and argsort are functionally correct when
+    the array is chunked along the sort axis, e.g. the sort is
+    not just local to each chunk.
+    """
+    a = da.random.random((10, 10), chunks=(5, 5))
+    actual = getattr(xp, func)(a, axis=axis)
+    expect = getattr(np, func)(a.compute(), axis=axis)
+    np.testing.assert_array_equal(actual, expect)
+
+
+@pytest.mark.parametrize(
+    "shape,chunks",
+    [
+        # 3 GiB; 128 MiB per chunk; must rechunk before sorting.
+        # Sort chunks can be 128 MiB each; no need for final rechunk.
+        ((20_000, 20_000), "auto"),
+        # 3 GiB; 128 MiB per chunk; must rechunk before sorting.
+        # Must sort on two 1.5 GiB chunks; benefits from final rechunk.
+        ((2, 2**30 * 3 // 16), "auto"),
+        # 3 GiB; 1.5 GiB per chunk; no need to rechunk before sorting.
+        # Surely the user must know what they're doing, so don't
+        # perform the final rechunk.
+        ((2, 2**30 * 3 // 16), (1, -1)),
+    ],
+)
+@pytest.mark.parametrize("func", ["sort", "argsort"])
+def test_sort_argsort_chunk_size(xp, func, shape, chunks):
+    """
+    Test that sort and argsort produce reasonably-sized chunks
+    in the output array, even if they had to go through a singular
+    huge one to perform the operation.
+    """
+    a = da.random.random(shape, chunks=chunks)
+    b = getattr(xp, func)(a)
+    max_chunk_size = max(b.chunks[0]) * max(b.chunks[1]) * b.dtype.itemsize
+    assert (
+        max_chunk_size <= 128 * 1024 * 1024  # 128 MiB
+        or b.chunks == a.chunks
+    )
+
+
+@pytest.mark.parametrize("func", ["sort", "argsort"])
+def test_sort_argsort_meta(xp, func):
+    """Test meta-namespace other than numpy"""
+    typ = type(array_api_strict.asarray(0))
+    a = da.random.random(10)
+    b = a.map_blocks(array_api_strict.asarray)
+    assert isinstance(b._meta, typ)
+    c = getattr(xp, func)(b)
+    assert isinstance(c._meta, typ)
+    d = c.compute()
+    # Note: np.sort(array_api_strict.asarray(0)) would return a numpy array
+    assert isinstance(d, typ)
+    np.testing.assert_array_equal(d, getattr(np, func)(a.compute()))