Adjust dtype conversions

src/finch/julia.py

@@ -17,7 +17,7 @@
     ):
         juliapkg.add(_FINCH_NAME, _FINCH_HASH, version=_FINCH_VERSION)
 
-import juliacall  # noqa
+import juliacall as jc  # noqa
 
 juliapkg.resolve()
 from juliacall import Main as jl  # noqa

src/finch/tensor.py

@@ -7,7 +7,7 @@
 
 from . import dtypes as jl_dtypes
 from .errors import PerformanceWarning
-from .julia import jl
+from .julia import jc, jl
 from .levels import (
     _Display,
     Dense,
@@ -338,7 +338,7 @@ def todense(self) -> np.ndarray:
         else:
             # create materialized dense array
             shape = jl.size(obj)
-            dense_lvls = jl.Element(jl.default(obj))
+            dense_lvls = jl.Element(jc.convert(self.dtype, jl.default(obj)))
             for _ in range(self.ndim):
                 dense_lvls = jl.Dense(dense_lvls)
             dense_tensor = jl.Tensor(dense_lvls, obj).lvl  # materialize
@@ -748,7 +748,7 @@ def astype(x: Tensor, dtype: DType, /, *, copy: bool = True):
     else:
         finch_tns = x._obj.body
         result = jl.copyto_b(
-            jl.similar(finch_tns, jl.default(finch_tns), dtype), finch_tns
+            jl.similar(finch_tns, jc.convert(dtype, jl.default(finch_tns)), dtype), finch_tns
         )
         return Tensor(jl.swizzle(result, *x.get_order(zero_indexing=False)))
 
@@ -785,16 +785,7 @@ def _reduce(x: Tensor, fn: Callable, axis, dtype=None):
         result = fn(x._obj, dims=axis)
     else:
         result = fn(x._obj)
-
-    if (
-        jl.isa(result, jl.Finch.SwizzleArray) or
-        jl.isa(result, jl.Finch.Tensor) or
-        jl.isa(result, jl.Finch.LazyTensor)
-    ):
-        result = Tensor(result)
-    else:
-        result = np.array(result)
-    return result
+    return Tensor(result)
 
 
 def sum(

tests/test_ops.py

@@ -167,10 +167,7 @@ def test_reductions(arr3d, func_name, axis, dtype):
     actual = getattr(finch, func_name)(A_finch, axis=axis)
     expected = getattr(np, func_name)(arr3d, axis=axis)
 
-    if isinstance(actual, finch.Tensor):
-        actual = actual.todense()
-
-    assert_equal(actual, expected)
+    assert_equal(actual.todense(), expected)
 
 
 @pytest.mark.parametrize(

tests/test_sparse.py

@@ -142,23 +142,29 @@ def test_permute_dims(arr3d, permutation, order):
 
 @pytest.mark.parametrize("order", ["C", "F"])
 def test_astype(arr3d, order):
-    arr = np.array(arr3d, order=order)
+    arr = np.array(arr3d, order=order, dtype=np.int64)
     storage = finch.Storage(
         finch.Dense(finch.SparseList(finch.SparseList(finch.Element(np.int64(0))))),
         order=order,
     )
     arr_finch = finch.Tensor(arr).to_device(storage)
 
     result = finch.astype(arr_finch, finch.int64)
-    assert_equal(result.todense(), arr)
-    assert not arr_finch is result
+    assert not result is arr_finch
+    result = result.todense()
+    assert_equal(result, arr)
+    assert result.dtype == arr.dtype
 
     result = finch.astype(arr_finch, finch.int64, copy=False)
-    assert_equal(result.todense(), arr)
-    assert arr_finch is result
-
-    result = finch.astype(arr_finch, finch.float32)
-    assert_equal(result.todense(), arr.astype(np.float32))
+    assert result is arr_finch
+    result = result.todense()
+    assert_equal(result, arr)
+    assert result.dtype == arr.dtype
+
+    result = finch.astype(arr_finch, finch.float32).todense()
+    arr = arr.astype(np.float32)
+    assert_equal(result, arr)
+    assert result.dtype == arr.dtype
 
     with pytest.raises(
         ValueError, match="Unable to avoid a copy while casting in no-copy mode."