issues: 1655290694

I tried to run where with chunked cupy arrays: python In [1]: import xarray as xr ...: import cupy ...: import dask.array as da ...: ...: arr = xr.DataArray(cupy.arange(4), dims="x") ...: mask = xr.DataArray(cupy.array([False, True, True, False]), dims="x") this works: python In [2]: arr.where(mask) Out[2]: <xarray.DataArray (x: 4)> array([nan, 1., 2., nan]) Dimensions without coordinates: x this fails: ```python In [4]: arr.chunk().where(mask).compute()

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TypeError Traceback (most recent call last) Cell In[4], line 1 ----> 1 arr.chunk().where(mask).compute()

File ~/repos/xarray/xarray/core/dataarray.py:1095, in DataArray.compute(self, kwargs) 1076 """Manually trigger loading of this array's data from disk or a 1077 remote source into memory and return a new array. The original is 1078 left unaltered. (...) 1092 dask.compute 1093 """ 1094 new = self.copy(deep=False) -> 1095 return new.load(kwargs)

File ~/repos/xarray/xarray/core/dataarray.py:1069, in DataArray.load(self, kwargs) 1051 def load(self: T_DataArray, kwargs) -> T_DataArray: 1052 """Manually trigger loading of this array's data from disk or a 1053 remote source into memory and return this array. 1054 (...) 1067 dask.compute 1068 """ -> 1069 ds = self._to_temp_dataset().load(**kwargs) 1070 new = self._from_temp_dataset(ds) 1071 self._variable = new._variable

File ~/repos/xarray/xarray/core/dataset.py:752, in Dataset.load(self, kwargs) 749 import dask.array as da 751 # evaluate all the dask arrays simultaneously --> 752 evaluated_data = da.compute(*lazy_data.values(), kwargs) 754 for k, data in zip(lazy_data, evaluated_data): 755 self.variables[k].data = data

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/base.py:600, in compute(traverse, optimize_graph, scheduler, get, args, kwargs) 597 keys.append(x.dask_keys()) 598 postcomputes.append(x.dask_postcompute()) --> 600 results = schedule(dsk, keys, kwargs) 601 return repack([f(r, a) for r, (f, a) in zip(results, postcomputes)])

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/threaded.py:89, in get(dsk, keys, cache, num_workers, pool, kwargs) 86 elif isinstance(pool, multiprocessing.pool.Pool): 87 pool = MultiprocessingPoolExecutor(pool) ---> 89 results = get_async( 90 pool.submit, 91 pool._max_workers, 92 dsk, 93 keys, 94 cache=cache, 95 get_id=_thread_get_id, 96 pack_exception=pack_exception, 97 kwargs, 98 ) 100 # Cleanup pools associated to dead threads 101 with pools_lock:

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/local.py:511, in get_async(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs) 509 _execute_task(task, data) # Re-execute locally 510 else: --> 511 raise_exception(exc, tb) 512 res, worker_id = loads(res_info) 513 state["cache"][key] = res

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/local.py:319, in reraise(exc, tb) 317 if exc.traceback is not tb: 318 raise exc.with_traceback(tb) --> 319 raise exc

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/local.py:224, in execute_task(key, task_info, dumps, loads, get_id, pack_exception) 222 try: 223 task, data = loads(task_info) --> 224 result = _execute_task(task, data) 225 id = get_id() 226 result = dumps((result, id))

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk) 115 func, args = arg[0], arg[1:] 116 # Note: Don't assign the subtask results to a variable. numpy detects 117 # temporaries by their reference count and can execute certain 118 # operations in-place. --> 119 return func(*(_execute_task(a, cache) for a in args)) 120 elif not ishashable(arg): 121 return arg

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/optimization.py:990, in SubgraphCallable.call(self, *args) 988 if not len(args) == len(self.inkeys): 989 raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args))) --> 990 return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args)))

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/core.py:149, in get(dsk, out, cache) 147 for key in toposort(dsk): 148 task = dsk[key] --> 149 result = _execute_task(task, cache) 150 cache[key] = result 151 result = _execute_task(out, cache)

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/dask/core.py:119, in _execute_task(arg, cache, dsk) 115 func, args = arg[0], arg[1:] 116 # Note: Don't assign the subtask results to a variable. numpy detects 117 # temporaries by their reference count and can execute certain 118 # operations in-place. --> 119 return func(*(_execute_task(a, cache) for a in args)) 120 elif not ishashable(arg): 121 return arg

File <array_function internals>:180, in where(args, *kwargs)

File cupy/_core/core.pyx:1723, in cupy._core.core._ndarray_base.array_function()

File ~/.local/opt/mambaforge/envs/xarray/lib/python3.10/site-packages/cupy/_sorting/search.py:211, in where(condition, x, y) 209 if fusion._is_fusing(): 210 return fusion._call_ufunc(_where_ufunc, condition, x, y) --> 211 return _where_ufunc(condition.astype('?'), x, y)

File cupy/_core/_kernel.pyx:1287, in cupy._core._kernel.ufunc.call()

File cupy/_core/_kernel.pyx:160, in cupy._core._kernel._preprocess_args()

File cupy/_core/_kernel.pyx:146, in cupy._core._kernel._preprocess_arg()

TypeError: Unsupported type <class 'numpy.ndarray'> this works again:python In [7]: arr.chunk().where(mask.chunk(), cupy.array(cupy.nan)).compute() Out[7]: <xarray.DataArray (x: 4)> array([nan, 1., 2., nan]) Dimensions without coordinates: x `` And other methods likefillna` show similar behavior.

I think the reason is that this: https://github.com/pydata/xarray/blob/d4db16699f30ad1dc3e6861601247abf4ac96567/xarray/core/duck_array_ops.py#L195 is not sufficient to detect cupy beneath other layers of duckarrays (most commonly dask, pint, or both). In this specific case we could extend the condition to also match chunked cupy arrays (like arr.cupy.is_cupy does, but using is_duck_dask_array), but this will still break for other duckarray layers or if dask is not involved, and we're also in the process of moving away from special-casing dask. So short of asking cupy to treat 0d arrays like scalars I'm not sure how to fix this.

cc @jacobtomlinson