Merge branch '1031-support-distribution-of-xarray' of github.com:helm…

…holtz-analytics/heat into 1031-support-distribution-of-xarray

.github/ISSUE_TEMPLATE/feature_request.md

@@ -2,7 +2,7 @@
 name: Feature request
 about: Suggest missing features or functionalities
 title: ''
-labels: ''
+labels: 'enhancement'
 assignees: ''
 
 ---

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,3 +1,15 @@
+## Due Diligence
+<!--- Please address the following points before setting your PR "ready for review".
+--->
+- General:
+    - [ ]  **base branch** must be `main` for new features, latest release branch (e.g. `release/1.3.x`) for bug fixes
+    - [ ]  **title** of the PR is suitable to appear in the [Release Notes](https://github.com/helmholtz-analytics/heat/releases/latest)
+- Implementation:
+    - [ ] unit tests: all split configurations tested
+    - [ ] unit tests: multiple dtypes tested
+    - [ ] documentation updated where needed
+
+
 ## Description
 
 <!--- Include a summary of the change/s.
@@ -42,11 +54,5 @@ my be illegible. It may be easiest to save the output of each to a file.
 --->
 
 
-## Due Diligence
-- [ ] All split configurations tested
-- [ ] Multiple dtypes tested in relevant functions
-- [ ] Documentation updated (if needed)
-- [ ] Title of PR is suitable for corresponding CHANGELOG entry
-
 #### Does this change modify the behaviour of other functions? If so, which?
 yes / no

.github/issue-branch.yml

@@ -0,0 +1,13 @@
+mode: auto
+silent: false
+branchName: full
+defaultBranch: 'main'
+
+branches:
+  - label: bug
+    name: release/1.3.x
+    prefix: bugs/
+  - label: enhancement
+    prefix: features/
+  - label: documentation
+    prefix: docs/

.github/workflows/create-branch-on-assignment.yml

@@ -0,0 +1,16 @@
+name: Create branch on assignment
+
+on:
+  # The issues event below is only needed for the default (auto) mode,
+  # you can remove it otherwise
+  issues:
+    types: [ assigned ]
+
+jobs:
+  create_issue_branch_job:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Create Issue Branch
+        uses: robvanderleek/create-issue-branch@main
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.pre-commit-config.yaml

@@ -10,7 +10,7 @@ repos:
     -   id: check-added-large-files
     -   id: flake8
 -   repo: https://github.com/psf/black
-    rev: 23.3.0
+    rev: 23.7.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/pydocstyle

heat/core/printing.py

@@ -238,9 +238,15 @@ def _torch_data(dndarray, summarize) -> DNDarray:
 
             # non-split dimension, can slice locally
             if i != dndarray.split:
-                start_tensor = torch.index_select(data, i, torch.arange(edgeitems + 1))
+                start_tensor = torch.index_select(
+                    data, i, torch.arange(edgeitems + 1, device=data.device)
+                )
                 end_tensor = torch.index_select(
-                    data, i, torch.arange(dndarray.lshape[i] - edgeitems, dndarray.lshape[i])
+                    data,
+                    i,
+                    torch.arange(
+                        dndarray.lshape[i] - edgeitems, dndarray.lshape[i], device=data.device
+                    ),
                 )
                 data = torch.cat([start_tensor, end_tensor], dim=i)
             # split-dimension , need to respect the global offset
@@ -249,18 +255,27 @@ def _torch_data(dndarray, summarize) -> DNDarray:
 
                 if offset < edgeitems + 1:
                     end = min(dndarray.lshape[i], edgeitems + 1 - offset)
-                    data = torch.index_select(data, i, torch.arange(end))
+                    data = torch.index_select(data, i, torch.arange(end, device=data.device))
                 elif dndarray.gshape[i] - edgeitems < offset - dndarray.lshape[i]:
                     global_start = dndarray.gshape[i] - edgeitems
                     data = torch.index_select(
-                        data, i, torch.arange(max(0, global_start - offset), dndarray.lshape[i])
+                        data,
+                        i,
+                        torch.arange(
+                            max(0, global_start - offset),
+                            dndarray.lshape[i],
+                            device=data.device,
+                        ),
                     )
         # exchange data
         received = dndarray.comm.gather(data)
         if dndarray.comm.rank == 0:
             # concatenate data along the split axis
+            # problem: CUDA-aware MPI `gather`s all `data` in a list of tensors on MPI-process no. 0, but not necessarily on the same cuda device.
+            # Indeed, `received` may be a list of tensors on cuda device 0, cuda device 1, ... therefore, we need to move all entries of the list to cuda device 0 before applying `cat`.
+            device0 = received[0].device
+            received = [tens.to(device0) for tens in received]
             data = torch.cat(received, dim=dndarray.split)
-
     return data
 
 

heat/core/tests/test_printing.py

@@ -429,3 +429,11 @@ def test_split_2_above_threshold(self):
 
         if dndarray.comm.rank == 0:
             self.assertEqual(comparison, __str)
+
+
+class TestPrintingGPU(TestCase):
+    def test_print_GPU(self):
+        # this test case also includes GPU now, checking the output is not done; only test whether the routine itself works...
+        a0 = ht.arange(2**20, dtype=ht.float32).reshape((2**10, 2**10)).resplit_(0)
+        a1 = ht.arange(2**20, dtype=ht.float32).reshape((2**10, 2**10)).resplit_(1)
+        print(a0, a1)