Reimplement create_staggered_kernel

create_staggered_kernel now takes a list of assignments to staggered fields and otherwise behaves like create_kernel. I have included a diffusion equation test case in both D2Q5 and D2Q9. Putting vectorial/tensorial quantities on the staggered points also works.

This merge requests break API compatibility with the previous create_staggered_kernel.

Fixes #16 (closed). My next step is to implement automatic FV discretization.

Build of dependent projects (pygrandchem, pystencils_walberla) passes as per https://i10git.cs.fau.de/pycodegen/pycodegen/merge_requests/6.

pystencils/kernelcreation.py

-import itertools
 from types import MappingProxyType
 
 import sympy as sp
@@ -6,8 +5,10 @@ import sympy as sp
 from pystencils.assignment import Assignment
 from pystencils.astnodes import Block, Conditional, LoopOverCoordinate, SympyAssignment
 from pystencils.cpu.vectorization import vectorize
+from pystencils.field import Field, FieldType
 from pystencils.gpucuda.indexing import indexing_creator_from_params
 from pystencils.simp.assignment_collection import AssignmentCollection
+from pystencils.stencil import direction_string_to_offset, inverse_direction_string
 from pystencils.transformations import (
     loop_blocking, move_constants_before_loop, remove_conditionals_in_staggered_kernel)
 
@@ -23,7 +24,8 @@ def create_kernel(assignments,
                   cpu_blocking=None,
                   gpu_indexing='block',
                   gpu_indexing_params=MappingProxyType({}),
-                  use_textures_for_interpolation=True):
+                  use_textures_for_interpolation=True,
+                  cpu_prepend_optimizations=[]):
     """
     Creates abstract syntax tree (AST) of kernel, using a list of update equations.
 
@@ -47,6 +49,7 @@ def create_kernel(assignments,
         gpu_indexing: either 'block' or 'line' , or custom indexing class, see `AbstractIndexing`
         gpu_indexing_params: dict with indexing parameters (constructor parameters of indexing class)
                              e.g. for 'block' one can specify '{'block_size': (20, 20, 10) }'
+        cpu_prepend_optimizations: list of extra optimizations to perform first on the AST
 
     Returns:
         abstract syntax tree (AST) object, that can either be printed as source code with `show_code` or
@@ -84,6 +87,8 @@ def create_kernel(assignments,
         ast = create_kernel(assignments, type_info=data_type, split_groups=split_groups,
                             iteration_slice=iteration_slice, ghost_layers=ghost_layers,
                             skip_independence_check=skip_independence_check)
+        for optimization in cpu_prepend_optimizations:
+            optimization(ast)
         omp_collapse = None
         if cpu_blocking:
             omp_collapse = loop_blocking(ast, cpu_blocking)
@@ -186,104 +191,99 @@ def create_indexed_kernel(assignments,
         raise ValueError("Unknown target %s. Has to be either 'cpu' or 'gpu'" % (target,))
 
 
-def create_staggered_kernel(staggered_field, expressions, subexpressions=(), target='cpu',
-                            gpu_exclusive_conditions=False, **kwargs):
+def create_staggered_kernel(assignments, gpu_exclusive_conditions=False, **kwargs):
     """Kernel that updates a staggered field.
 
     .. image:: /img/staggered_grid.svg
 
+    For a staggered field, the first index coordinate defines the location of the staggered value.
+    Further index coordinates can be used to store vectors/tensors at each point.
+
     Args:
-        staggered_field: field where the first index coordinate defines the location of the staggered value
-                can have 1 or 2 index coordinates, in case of two index coordinates at every staggered location
-                a vector is stored, expressions parameter has to be a sequence of sequences then
-                where e.g. ``f[0,0](0)`` is interpreted as value at the left cell boundary, ``f[1,0](0)`` the right cell
-                boundary and ``f[0,0](1)`` the southern cell boundary etc.
-        expressions: sequence of expressions of length dim, defining how the west, southern, (bottom) cell boundary
-                     should be updated.
-        subexpressions: optional sequence of Assignments, that define subexpressions used in the main expressions
-        target: 'cpu' or 'gpu'
-        gpu_exclusive_conditions: if/else construct to have only one code block for each of 2**dim code paths
-        kwargs: passed directly to create_kernel, iteration slice and ghost_layers parameters are not allowed
+        assignments: a sequence of assignments or an AssignmentCollection.
+                     Assignments to staggered field are processed specially, while subexpressions and assignments to
+                     regular fields are passed through to `create_kernel`. Multiple different staggered fields can be
+                     used, but they all need to use the same stencil (i.e. the same number of staggered points) and
+                     shape.
+        gpu_exclusive_conditions: whether to use nested conditionals instead of multiple conditionals
+        kwargs: passed directly to create_kernel, iteration_slice and ghost_layers parameters are not allowed
 
     Returns:
         AST, see `create_kernel`
     """
     assert 'iteration_slice' not in kwargs and 'ghost_layers' not in kwargs
-    assert staggered_field.index_dimensions in (1, 2), 'Staggered field must have one or two index dimensions'
+
+    subexpressions = ()
+    if isinstance(assignments, AssignmentCollection):
+        subexpressions = assignments.subexpressions + [a for a in assignments.main_assignments
+                                                       if type(a.lhs) is not Field.Access
+                                                       and not FieldType.is_staggered(a.lhs.field)]
+        assignments = [a for a in assignments.main_assignments if type(a.lhs) is Field.Access
+                       and FieldType.is_staggered(a.lhs.field)]
+    else:
+        subexpressions = [a for a in assignments if type(a.lhs) is not Field.Access
+                          and not FieldType.is_staggered(a.lhs.field)]
+        assignments = [a for a in assignments if type(a.lhs) is Field.Access 
+                       and FieldType.is_staggered(a.lhs.field)]
+    if len(set([tuple(a.lhs.field.staggered_stencil) for a in assignments])) != 1:
+        raise ValueError("All assignments need to be made to staggered fields with the same stencil")
+    if len(set([a.lhs.field.shape for a in assignments])) != 1:
+        raise ValueError("All assignments need to be made to staggered fields with the same shape")
+
+    staggered_field = assignments[0].lhs.field
+    stencil = staggered_field.staggered_stencil
     dim = staggered_field.spatial_dimensions
+    points = staggered_field.index_shape[0]
+    shape = staggered_field.shape
 
     counters = [LoopOverCoordinate.get_loop_counter_symbol(i) for i in range(dim)]
-    conditions = [counters[i] < staggered_field.shape[i] - 1 for i in range(dim)]
-    assert len(expressions) == dim
-    if staggered_field.index_dimensions == 2:
-        assert all(len(sublist) == len(expressions[0]) for sublist in expressions), \
-            "If staggered field has two index dimensions expressions has to be a sequence of sequences of all the " \
-            "same length."
 
     final_assignments = []
-    last_conditional = None
 
-    def add(condition, dimensions, as_else_block=False):
-        nonlocal last_conditional
-        if staggered_field.index_dimensions == 1:
-            assignments = [Assignment(staggered_field(d), expressions[d]) for d in dimensions]
-            a_coll = AssignmentCollection(assignments, list(subexpressions))
-            a_coll = a_coll.new_filtered([staggered_field(d) for d in dimensions])
-        elif staggered_field.index_dimensions == 2:
-            assert staggered_field.has_fixed_index_shape
-            assignments = [Assignment(staggered_field(d, i), expr)
-                           for d in dimensions
-                           for i, expr in enumerate(expressions[d])]
-            a_coll = AssignmentCollection(assignments, list(subexpressions))
-            a_coll = a_coll.new_filtered([staggered_field(d, i) for i in range(staggered_field.index_shape[1])
-                                          for d in dimensions])
-        sp_assignments = [SympyAssignment(a.lhs, a.rhs) for a in a_coll.all_assignments]
-        if as_else_block and last_conditional:
-            new_cond = Conditional(condition, Block(sp_assignments))
-            last_conditional.false_block = Block([new_cond])
-            last_conditional = new_cond
-        else:
-            last_conditional = Conditional(condition, Block(sp_assignments))
-            final_assignments.append(last_conditional)
+    # find out whether any of the ghost layers is not needed
+    common_exclusions = set(["E", "W", "N", "S", "T", "B"][:2 * dim])
+    for direction in stencil:
+        exclusions = set(["E", "W", "N", "S", "T", "B"][:2 * dim])
+        for elementary_direction in direction:
+            exclusions.remove(inverse_direction_string(elementary_direction))
+        common_exclusions.intersection_update(exclusions)
+    ghost_layers = [[1, 1] for d in range(dim)]
+    for direction in common_exclusions:
+        direction = direction_string_to_offset(direction)
+        for d, s in enumerate(direction):
+            if s == 1:
+                ghost_layers[d][0] = 0
+            elif s == -1:
+                ghost_layers[d][1] = 0
 
-    if target == 'cpu' or not gpu_exclusive_conditions:
-        for d in range(dim):
-            cond = sp.And(*[conditions[i] for i in range(dim) if d != i])
-            add(cond, [d])
-    elif target == 'gpu':
-        full_conditions = [sp.And(*[conditions[i] for i in range(dim) if d != i]) for d in range(dim)]
-        for include in itertools.product(*[[1, 0]] * dim):
-            case_conditions = sp.And(*[c if value else sp.Not(c) for c, value in zip(full_conditions, include)])
-            dimensions_to_include = [i for i in range(dim) if include[i]]
-            if dimensions_to_include:
-                add(case_conditions, dimensions_to_include, True)
-
-    ghost_layers = [(1, 0)] * dim
+    def condition(direction):
+        """exclude those staggered points that correspond to fluxes between ghost cells"""
+        exclusions = set(["E", "W", "N", "S", "T", "B"][:2 * dim])
 
-    blocking = kwargs.get('cpu_blocking', None)
-    if blocking:
-        del kwargs['cpu_blocking']
+        for elementary_direction in direction:
+            exclusions.remove(inverse_direction_string(elementary_direction))
+        conditions = []
+        for e in exclusions:
+            if e in common_exclusions:
+                continue
+            offset = direction_string_to_offset(e)
+            for i, o in enumerate(offset):
+                if o == 1:
+                    conditions.append(counters[i] < shape[i] - 1)
+                elif o == -1:
+                    conditions.append(counters[i] > 0)
+        return sp.And(*conditions)
 
-    cpu_vectorize_info = kwargs.get('cpu_vectorize_info', None)
-    if cpu_vectorize_info:
-        del kwargs['cpu_vectorize_info']
-    openmp = kwargs.get('cpu_openmp', None)
-    if openmp:
-        del kwargs['cpu_openmp']
+    if gpu_exclusive_conditions:
+        raise NotImplementedError('gpu_exclusive_conditions is not implemented yet')
 
-    ast = create_kernel(final_assignments, ghost_layers=ghost_layers, target=target, **kwargs)
+    for d, direction in zip(range(points), stencil):
+        sp_assignments = [SympyAssignment(assignments[d].lhs, assignments[d].rhs)] + \
+                         [SympyAssignment(s.lhs, s.rhs) for s in subexpressions]
+        last_conditional = Conditional(condition(direction), Block(sp_assignments))
+        final_assignments.append(last_conditional)
 
-    if target == 'cpu':
-        remove_conditionals_in_staggered_kernel(ast)
-        move_constants_before_loop(ast)
-        omp_collapse = None
-        if blocking:
-            omp_collapse = loop_blocking(ast, blocking)
-        if openmp:
-            from pystencils.cpu import add_openmp
-            add_openmp(ast, num_threads=openmp, collapse=omp_collapse, assume_single_outer_loop=False)
-        if cpu_vectorize_info is True:
-            vectorize(ast)
-        elif isinstance(cpu_vectorize_info, dict):
-            vectorize(ast, **cpu_vectorize_info)
+    prepend_optimizations = [remove_conditionals_in_staggered_kernel, move_constants_before_loop]
+    ast = create_kernel(final_assignments, ghost_layers=ghost_layers, cpu_prepend_optimizations=prepend_optimizations,
+                        **kwargs)
     return ast