Merge branch 'main' into bugs/1232-_Bug_Ensure_NumPy-compatibility_of…

…_test_statistics_py

heat/core/linalg/qr.py

@@ -16,7 +16,7 @@
 
 def qr(
     a: DNDarray,
-    tiles_per_proc: Union[int, torch.Tensor] = 1,
+    tiles_per_proc: Union[int, torch.Tensor] = 2,
     calc_q: bool = True,
     overwrite_a: bool = False,
 ) -> Tuple[DNDarray, DNDarray]:
@@ -30,7 +30,8 @@ def qr(
     a : DNDarray
         Array which will be decomposed
     tiles_per_proc : int or torch.Tensor, optional
-        Number of tiles per process to operate on,
+        Number of tiles per process to operate on
+        We highly recommend to use tiles_per_proc > 1, as the choice 1 might result in an error in certain situations (in particular for split=0).
     calc_q : bool, optional
         Whether or not to calculate Q.
         If ``True``, function returns ``(Q, R)``.
@@ -89,6 +90,11 @@ def qr(
     if len(a.shape) != 2:
         raise ValueError("Array 'a' must be 2 dimensional")
 
+    if a.split == 0 and tiles_per_proc == 1:
+        raise Warning(
+            "Using tiles_per_proc=1 with split=0 can result in an error. We highly recommend to use tiles_per_proc > 1."
+        )
+
     QR = collections.namedtuple("QR", "Q, R")
 
     if a.split is None:
@@ -898,7 +904,7 @@ def __split1_qr_loop(
         except AttributeError:
             q1, r1 = r_tiles[dcol, dcol].qr(some=False)
 
-        r_tiles.arr.comm.Bcast(q1.clone(), root=diag_process)
+        r_tiles.arr.comm.Bcast(q1.clone(memory_format=torch.contiguous_format), root=diag_process)
         r_tiles[dcol, dcol] = r1
         # apply q1 to the trailing matrix (other processes)
 

heat/core/linalg/tests/test_qr.py

@@ -22,7 +22,7 @@ def test_qr_sp0_ext(self):
         sp = 0
         for m in range(50, st_whole.shape[0] + 1, 1):
             for n in range(50, st_whole.shape[1] + 1, 1):
-                for t in range(1, 3):
+                for t in range(2, 3):
                     st = st_whole[:m, :n].clone()
                     a_comp = ht.array(st, split=0)
                     a = ht.array(st, split=sp)
@@ -37,7 +37,7 @@ def test_qr_sp1_ext(self):
         sp = 1
         for m in range(50, st_whole.shape[0] + 1, 1):
             for n in range(50, st_whole.shape[1] + 1, 1):
-                for t in range(1, 3):
+                for t in range(2, 3):
                     st = st_whole[:m, :n].clone()
                     a_comp = ht.array(st, split=0)
                     a = ht.array(st, split=sp)
@@ -50,7 +50,7 @@ def test_qr(self):
         m, n = 20, 40
         st = torch.randn(m, n, device=self.device.torch_device, dtype=torch.float)
         a_comp = ht.array(st, split=0)
-        for t in range(1, 3):
+        for t in range(2, 3):
             for sp in range(2):
                 a = ht.array(st, split=sp, dtype=torch.float)
                 qr = a.qr(tiles_per_proc=t)
@@ -60,7 +60,7 @@ def test_qr(self):
         m, n = 40, 40
         st1 = torch.randn(m, n, device=self.device.torch_device)
         a_comp1 = ht.array(st1, split=0)
-        for t in range(1, 3):
+        for t in range(2, 3):
             for sp in range(2):
                 a1 = ht.array(st1, split=sp)
                 qr1 = a1.qr(tiles_per_proc=t)
@@ -70,7 +70,7 @@ def test_qr(self):
         m, n = 40, 20
         st2 = torch.randn(m, n, dtype=torch.double, device=self.device.torch_device)
         a_comp2 = ht.array(st2, split=0, dtype=ht.double)
-        for t in range(1, 3):
+        for t in range(2, 3):
             for sp in range(2):
                 a2 = ht.array(st2, split=sp)
                 qr2 = a2.qr(tiles_per_proc=t)

heat/core/version.py

@@ -5,7 +5,7 @@
 """Indicates Heat's main version."""
 minor: int = 4
 """Indicates feature extension."""
-micro: int = 0
+micro: int = 1
 """Indicates revisions for bugfixes."""
 extension: str = "dev"
 """Indicates special builds, e.g. for specific hardware."""