Merge pull request #4861 from firedrakeproject/pbrubeck/merge-release

Merge release

Author: pbrubeck

.github/workflows/core.yml

@@ -177,10 +177,8 @@ jobs:
           python3 -m venv venv
           . venv/bin/activate
 
-          : # Force a rebuild of petsc4py as the cached one will not link to the fresh
-          : # install of PETSc.
-          pip cache remove petsc4py
-          pip cache remove slepc4py
+          : # Empty the pip cache to ensure that everything is compiled from scratch
+          pip cache purge
 
           if [ ${{ inputs.target_branch }} = 'release' ]; then
             EXTRA_BUILD_ARGS=''
@@ -433,14 +431,8 @@ jobs:
           python3 -m venv venv
           . venv/bin/activate
 
-          : # Force a rebuild of petsc4py as the cached one will not link to the fresh
-          : # install of PETSc. A similar trick may be needed for compiled dependencies
-          : # like h5py or mpi4py if changing HDF5/MPI libraries.
-          pip cache remove petsc4py
-
-          : # Hotfix for petsc4py build, see https://gitlab.com/petsc/petsc/-/issues/1759
-          echo 'Cython<3.1' > constraints.txt
-          export PIP_CONSTRAINT=constraints.txt
+          : # Empty the pip cache to ensure that everything is compiled from scratch
+          pip cache purge
 
           if [ ${{ inputs.target_branch }} = 'release' ]; then
             EXTRA_PIP_FLAGS=''

demos/patch/hcurl_riesz_star.py.rst

@@ -10,7 +10,7 @@ Here, we demonstrate how to do this in the latter case.
 We start by importing firedrake and setting up a :func:`.MeshHierarchy` and the
 exact solution and forcing data. Crucially, the meshes must have an overlapping
 parallel domain decomposition that supports the vertex star patches. This is set
-via the `distribution_parameters` kwarg of the :func:`.Mesh` constructor. ::
+via the ``distribution_parameters`` kwarg of the :func:`.Mesh` constructor. ::
 
   from firedrake import *
 

demos/patch/hdiv_riesz_star.py.rst

@@ -10,7 +10,7 @@ Here, we demonstrate how to do this in the former case.
 We start by importing firedrake and setting up a :func:`.MeshHierarchy` and the
 exact solution and forcing data. Crucially, the meshes must have an overlapping
 parallel domain decomposition that supports the vertex star patches. This is set
-via the `distribution_parameters` kwarg of the :func:`.Mesh` constructor. ::
+via the ``distribution_parameters`` kwarg of the :func:`.Mesh` constructor. ::
 
   from firedrake import *
 

demos/patch/poisson_mg_patches.py.rst

@@ -23,7 +23,7 @@ get a convergent method.  We refer the reader to other demos.
 We start by importing firedrake and setting up a :func:`.MeshHierarchy` and the
 exact solution and forcing data. Crucially, the meshes must have an overlapping
 parallel domain decomposition that supports the vertex star patches. This is set
-via the `distribution_parameters` kwarg of the :func:`.Mesh` constructor. ::
+via the ``distribution_parameters`` kwarg of the :func:`.Mesh` constructor. ::
 
   from firedrake import *
 

demos/patch/stokes_vanka_patches.py.rst

@@ -21,7 +21,7 @@ operators are extracted from the globally assembled stiffness matrix.
 We start by importing firedrake and setting up a :func:`.MeshHierarchy` and the
 exact solution and forcing data. Crucially, the meshes must have an overlapping
 parallel domain decomposition that supports the Vanka patches. This is set
-via the `distribution_parameters` kwarg of the :func:`.Mesh` constructor. ::
+via the ``distribution_parameters`` kwarg of the :func:`.Mesh` constructor. ::
 
   from firedrake import *
 

pyop2/types/mat.py

@@ -1159,16 +1159,16 @@ def mult(self, mat, x, y):
                 # Row matrix
                 out = v.dot(x)
                 if y.comm.rank == 0:
-                    y.array[0] = out
+                    y.array[...] = out
                 else:
                     y.array[...]
             else:
                 # Column matrix
                 if x.sizes[1] == 1:
                     v.copy(y)
-                    a = np.zeros(1, dtype=dtypes.ScalarType)
+                    a = np.zeros((), dtype=dtypes.ScalarType)
                     if x.comm.rank == 0:
-                        a[0] = x.array_r
+                        a[...] = x.array_r
                     else:
                         x.array_r
                     with mpi.temp_internal_comm(x.comm) as comm:
@@ -1183,9 +1183,9 @@ def multTranspose(self, mat, x, y):
                 # Row matrix
                 if x.sizes[1] == 1:
                     v.copy(y)
-                    a = np.zeros(1, dtype=dtypes.ScalarType)
+                    a = np.zeros((), dtype=dtypes.ScalarType)
                     if x.comm.rank == 0:
-                        a[0] = x.array_r
+                        a[...] = x.array_r
                     else:
                         x.array_r
                     with mpi.temp_internal_comm(x.comm) as comm:
@@ -1197,7 +1197,7 @@ def multTranspose(self, mat, x, y):
                 # Column matrix
                 out = v.dot(x)
                 if y.comm.rank == 0:
-                    y.array[0] = out
+                    y.array[...] = out
                 else:
                     y.array[...]
 
@@ -1208,9 +1208,9 @@ def multTransposeAdd(self, mat, x, y, z):
                 # Row matrix
                 if x.sizes[1] == 1:
                     v.copy(z)
-                    a = np.zeros(1, dtype=dtypes.ScalarType)
+                    a = np.zeros((), dtype=dtypes.ScalarType)
                     if x.comm.rank == 0:
-                        a[0] = x.array_r
+                        a[...] = x.array_r
                     else:
                         x.array_r
                     with mpi.temp_internal_comm(x.comm) as comm:
@@ -1235,7 +1235,7 @@ def multTransposeAdd(self, mat, x, y, z):
                 out = v.dot(x)
                 y = y.array_r
                 if z.comm.rank == 0:
-                    z.array[0] = out + y[0]
+                    z.array[...] = out + y
                 else:
                     z.array[...]