Changes to _set_functions_sync.py for test suite to pass

Changes to sync implementation of set functions so that test
suite passes with sync implementation as it does for async
implementation.

dpctl/tensor/_set_functions_sync.py

@@ -75,12 +75,14 @@ def unique_values(x: dpt.usm_ndarray) -> dpt.usm_ndarray:
     if x.ndim == 1:
         fx = x
     else:
-        fx = dpt.reshape(x, (x.size,), order="C", copy=False)
+        fx = dpt.reshape(x, (x.size,), order="C")
+    if fx.size == 0:
+        return fx
     s = dpt.sort(fx)
     unique_mask = dpt.empty(fx.shape, dtype="?", sycl_queue=exec_q)
     dpt.not_equal(s[:-1], s[1:], out=unique_mask[1:])
     unique_mask[0] = True
-    cumsum = dpt.empty(s.shape, dtype=dpt.int64)
+    cumsum = dpt.empty(s.shape, dtype=dpt.int64, sycl_queue=exec_q)
     n_uniques = mask_positions(unique_mask, cumsum, sycl_queue=exec_q)
     if n_uniques == fx.size:
         return s
@@ -127,13 +129,15 @@ def unique_counts(x: dpt.usm_ndarray) -> UniqueCountsResult:
     if x.ndim == 1:
         fx = x
     else:
-        fx = dpt.reshape(x, (x.size,), order="C", copy=False)
-    s = dpt.sort(x)
+        fx = dpt.reshape(x, (x.size,), order="C")
+    ind_dt = default_device_index_type(exec_q)
+    if fx.size == 0:
+        return UniqueCountsResult(fx, dpt.empty_like(fx, dtype=ind_dt))
+    s = dpt.sort(fx)
     unique_mask = dpt.empty(s.shape, dtype="?", sycl_queue=exec_q)
     dpt.not_equal(s[:-1], s[1:], out=unique_mask[1:])
     unique_mask[0] = True
-    ind_dt = default_device_index_type(exec_q)
-    cumsum = dpt.empty(unique_mask.shape, dtype=dpt.int64)
+    cumsum = dpt.empty(unique_mask.shape, dtype=dpt.int64, sycl_queue=exec_q)
     # synchronizing call
     n_uniques = mask_positions(unique_mask, cumsum, sycl_queue=exec_q)
     if n_uniques == fx.size:
@@ -195,18 +199,20 @@ def unique_inverse(x):
         raise TypeError(f"Expected dpctl.tensor.usm_ndarray, got {type(x)}")
     array_api_dev = x.device
     exec_q = array_api_dev.sycl_queue
+    ind_dt = default_device_index_type(exec_q)
     if x.ndim == 1:
         fx = x
     else:
-        fx = dpt.reshape(x, (x.size,), order="C", copy=False)
-    ind_dt = default_device_index_type(exec_q)
+        fx = dpt.reshape(x, (x.size,), order="C")
     sorting_ids = dpt.argsort(fx)
     unsorting_ids = dpt.argsort(sorting_ids)
+    if fx.size == 0:
+        return UniqueInverseResult(fx, dpt.reshape(unsorting_ids, x.shape))
     s = fx[sorting_ids]
     unique_mask = dpt.empty(fx.shape, dtype="?", sycl_queue=exec_q)
     unique_mask[0] = True
     dpt.not_equal(s[:-1], s[1:], out=unique_mask[1:])
-    cumsum = dpt.empty(unique_mask.shape, dtype=dpt.int64)
+    cumsum = dpt.empty(unique_mask.shape, dtype=dpt.int64, sycl_queue=exec_q)
     # synchronizing call
     n_uniques = mask_positions(unique_mask, cumsum, sycl_queue=exec_q)
     if n_uniques == fx.size:
@@ -251,7 +257,9 @@ def unique_inverse(x):
         ht_ev, _ = _full_usm_ndarray(fill_value=i, dst=_dst, sycl_queue=exec_q)
         ht_ev.wait()
         pos = pos_next
-    return UniqueInverseResult(unique_vals, inv[unsorting_ids])
+    return UniqueInverseResult(
+        unique_vals, dpt.reshape(inv[unsorting_ids], x.shape)
+    )
 
 
 def unique_all(x: dpt.usm_ndarray) -> UniqueAllResult:
@@ -289,22 +297,39 @@ def unique_all(x: dpt.usm_ndarray) -> UniqueAllResult:
         raise TypeError(f"Expected dpctl.tensor.usm_ndarray, got {type(x)}")
     array_api_dev = x.device
     exec_q = array_api_dev.sycl_queue
+    ind_dt = default_device_index_type(exec_q)
     if x.ndim == 1:
         fx = x
     else:
-        fx = dpt.reshape(x, (x.size,), order="C", copy=False)
-    ind_dt = default_device_index_type(exec_q)
+        fx = dpt.reshape(x, (x.size,), order="C")
     sorting_ids = dpt.argsort(fx)
     unsorting_ids = dpt.argsort(sorting_ids)
+    if fx.size == 0:
+        # original array contains no data
+        # so it can be safely returned as values
+        return UniqueAllResult(
+            fx,
+            sorting_ids,
+            dpt.reshape(unsorting_ids, x.shape),
+            dpt.empty_like(fx, dtype=ind_dt),
+        )
     s = fx[sorting_ids]
     unique_mask = dpt.empty(fx.shape, dtype="?", sycl_queue=exec_q)
     dpt.not_equal(s[:-1], s[1:], out=unique_mask[1:])
     unique_mask[0] = True
-    cumsum = dpt.empty(unique_mask.shape, dtype=dpt.int64)
+    cumsum = dpt.empty(unique_mask.shape, dtype=dpt.int64, sycl_queue=exec_q)
     # synchronizing call
     n_uniques = mask_positions(unique_mask, cumsum, sycl_queue=exec_q)
     if n_uniques == fx.size:
-        return UniqueInverseResult(s, unsorting_ids)
+        _counts = dpt.ones(
+            n_uniques, dtype=ind_dt, usm_type=x.usm_type, sycl_queue=exec_q
+        )
+        return UniqueAllResult(
+            s,
+            sorting_ids,
+            dpt.reshape(unsorting_ids, x.shape),
+            _counts,
+        )
     unique_vals = dpt.empty(
         n_uniques, dtype=x.dtype, usm_type=x.usm_type, sycl_queue=exec_q
     )
@@ -346,6 +371,6 @@ def unique_all(x: dpt.usm_ndarray) -> UniqueAllResult:
     return UniqueAllResult(
         unique_vals,
         sorting_ids[cum_unique_counts[:-1]],
-        inv[unsorting_ids],
+        dpt.reshape(inv[unsorting_ids], x.shape),
         _counts,
     )