Improvements to the clip wrapper

- Ensure the arrays that are created are created on the same device as x.
  (fixes #177)

- Make clip() work with dask.array. The workaround avoid uint64 -> float64
  promotion does not work here. (fixes #176)

- Fix loss of precision when clipping a float64 tensor with torch due to the
  scalar being converted to a float32 tensor.

array_api_compat/common/_aliases.py

@@ -12,7 +12,7 @@
 from typing import NamedTuple
 import inspect
 
-from ._helpers import array_namespace, _check_device
+from ._helpers import array_namespace, _check_device, device, is_torch_array
 
 # These functions are modified from the NumPy versions.
 
@@ -281,10 +281,11 @@ 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
-    result_shape = xp.broadcast_shapes(x.shape, min_shape, max_shape)
 
     wrapped_xp = array_namespace(x)
 
+    result_shape = xp.broadcast_shapes(x.shape, min_shape, max_shape)
+
     # np.clip does type promotion but the array API clip requires that the
     # output have the same dtype as x. We do this instead of just downcasting
     # the result of xp.clip() to handle some corner cases better (e.g.,
@@ -305,20 +306,26 @@ def _isscalar(a):
 
     # At least handle the case of Python integers correctly (see
     # https://github.com/numpy/numpy/pull/26892).
-    if type(min) is int and min <= xp.iinfo(x.dtype).min:
+    if type(min) is int and min <= wrapped_xp.iinfo(x.dtype).min:
         min = None
-    if type(max) is int and max >= xp.iinfo(x.dtype).max:
+    if type(max) is int and max >= wrapped_xp.iinfo(x.dtype).max:
         max = None
 
     if out is None:
-        out = wrapped_xp.asarray(xp.broadcast_to(x, result_shape), copy=True)
+        out = wrapped_xp.asarray(xp.broadcast_to(x, result_shape),
+                                 copy=True, device=device(x))
     if min is not None:
-        a = xp.broadcast_to(xp.asarray(min), result_shape)
+        if is_torch_array(x) and x.dtype == xp.float64 and _isscalar(min):
+            # Avoid loss of precision due to torch defaulting to float32
+            min = wrapped_xp.asarray(min, dtype=xp.float64)
+        a = xp.broadcast_to(wrapped_xp.asarray(min, device=device(x)), result_shape)
         ia = (out < a) | xp.isnan(a)
         # torch requires an explicit cast here
         out[ia] = wrapped_xp.astype(a[ia], out.dtype)
     if max is not None:
-        b = xp.broadcast_to(xp.asarray(max), result_shape)
+        if is_torch_array(x) and x.dtype == xp.float64 and _isscalar(max):
+            max = wrapped_xp.asarray(max, dtype=xp.float64)
+        b = xp.broadcast_to(wrapped_xp.asarray(max, device=device(x)), result_shape)
         ib = (out > b) | xp.isnan(b)
         out[ib] = wrapped_xp.astype(b[ib], out.dtype)
     # Return a scalar for 0-D

array_api_compat/dask/array/_aliases.py

@@ -89,7 +89,6 @@ def _dask_arange(
 permute_dims = get_xp(da)(_aliases.permute_dims)
 std = get_xp(da)(_aliases.std)
 var = get_xp(da)(_aliases.var)
-clip = get_xp(da)(_aliases.clip)
 empty = get_xp(da)(_aliases.empty)
 empty_like = get_xp(da)(_aliases.empty_like)
 full = get_xp(da)(_aliases.full)
@@ -167,6 +166,43 @@ 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
+# now).
+@get_xp(da)
+def clip(
+    x: Array,
+    /,
+    min: Optional[Union[int, float, Array]] = None,
+    max: Optional[Union[int, float, Array]] = None,
+    *,
+    xp,
+) -> Array:
+    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
+
+    # TODO: This won't handle dask unknown shapes
+    import numpy as np
+    result_shape = np.broadcast_shapes(x.shape, min_shape, max_shape)
+
+    if min is not None:
+        min = xp.broadcast_to(xp.asarray(min), result_shape)
+    if max is not None:
+        max = xp.broadcast_to(xp.asarray(max), result_shape)
+
+    if min is None and max is None:
+        return xp.positive(x)
+
+    if min is None:
+        return astype(xp.minimum(x, max), x.dtype)
+    if max is None:
+        return astype(xp.maximum(x, min), x.dtype)
+
+    return astype(xp.minimum(xp.maximum(x, min), max), x.dtype)
+
 # exclude these from all since
 _da_unsupported = ['sort', 'argsort']