from types import MappingProxyType
import sympy as sp
from pystencils.field import Field
from pystencils.assignment import Assignment
from pystencils.astnodes import LoopOverCoordinate, Conditional, Block, SympyAssignment
from pystencils.cpu.vectorization import vectorize
from pystencils.simp.assignment_collection import AssignmentCollection
from pystencils.gpucuda.indexing import indexing_creator_from_params
from pystencils.transformations import remove_conditionals_in_staggered_kernel
def create_kernel(assignments, target='cpu', data_type="double", iteration_slice=None, ghost_layers=None,
skip_independence_check=False,
cpu_openmp=False, cpu_vectorize_info=None,
gpu_indexing='block', gpu_indexing_params=MappingProxyType({})):
"""
Creates abstract syntax tree (AST) of kernel, using a list of update equations.
Args:
assignments: can be a single assignment, sequence of assignments or an `AssignmentCollection`
target: 'cpu', 'llvm' or 'gpu'
data_type: data type used for all untyped symbols (i.e. non-fields), can also be a dict from symbol name
to type
iteration_slice: rectangular subset to iterate over, if not specified the complete non-ghost layer \
part of the field is iterated over
ghost_layers: if left to default, the number of necessary ghost layers is determined automatically
a single integer specifies the ghost layer count at all borders, can also be a sequence of
pairs ``[(x_lower_gl, x_upper_gl), .... ]``
skip_independence_check: don't check that loop iterations are independent. This is needed e.g. for
periodicity kernel, that access the field outside the iteration bounds. Use with care!
cpu_openmp: True or number of threads for OpenMP parallelization, False for no OpenMP
cpu_vectorize_info: a dictionary with keys, 'vector_instruction_set', 'assume_aligned' and 'nontemporal'
for documentation of these parameters see vectorize function. Example:
'{'instruction_set': 'avx512', 'assume_aligned': True, 'nontemporal':True}'
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) }'
Returns:
abstract syntax tree (AST) object, that can either be printed as source code with `show_code` or
can be compiled with through its 'compile()' member
Example:
>>> import pystencils as ps
>>> import numpy as np
>>> s, d = ps.fields('s, d: [2D]')
>>> assignment = ps.Assignment(d[0,0], s[0, 1] + s[0, -1] + s[1, 0] + s[-1, 0])
>>> ast = ps.create_kernel(assignment, target='cpu', cpu_openmp=True)
>>> kernel = ast.compile()
>>> d_arr = np.zeros([5, 5])
>>> kernel(d=d_arr, s=np.ones([5, 5]))
>>> d_arr
array([[0., 0., 0., 0., 0.],
[0., 4., 4., 4., 0.],
[0., 4., 4., 4., 0.],
[0., 4., 4., 4., 0.],
[0., 0., 0., 0., 0.]])
"""
# ---- Normalizing parameters
split_groups = ()
if isinstance(assignments, AssignmentCollection):
if 'split_groups' in assignments.simplification_hints:
split_groups = assignments.simplification_hints['split_groups']
assignments = assignments.all_assignments
if isinstance(assignments, Assignment):
assignments = [assignments]
# ---- Creating ast
if target == 'cpu':
from pystencils.cpu import create_kernel
from pystencils.cpu import add_openmp
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)
if cpu_openmp:
add_openmp(ast, num_threads=cpu_openmp)
if cpu_vectorize_info:
if cpu_vectorize_info is True:
vectorize(ast)
elif isinstance(cpu_vectorize_info, dict):
vectorize(ast, **cpu_vectorize_info)
else:
raise ValueError("Invalid value for cpu_vectorize_info")
return ast
elif target == 'llvm':
from pystencils.llvm import create_kernel
ast = create_kernel(assignments, type_info=data_type, split_groups=split_groups,
iteration_slice=iteration_slice, ghost_layers=ghost_layers)
return ast
elif target == 'gpu':
from pystencils.gpucuda import create_cuda_kernel
ast = create_cuda_kernel(assignments, type_info=data_type,
indexing_creator=indexing_creator_from_params(gpu_indexing, gpu_indexing_params),
iteration_slice=iteration_slice, ghost_layers=ghost_layers,
skip_independence_check=skip_independence_check)
return ast
else:
raise ValueError("Unknown target %s. Has to be one of 'cpu', 'gpu' or 'llvm' " % (target,))
def create_indexed_kernel(assignments, index_fields, target='cpu', data_type="double", coordinate_names=('x', 'y', 'z'),
cpu_openmp=True, gpu_indexing='block', gpu_indexing_params=MappingProxyType({})):
"""
Similar to :func:`create_kernel`, but here not all cells of a field are updated but only cells with
coordinates which are stored in an index field. This traversal method can e.g. be used for boundary handling.
The coordinates are stored in a separated index_field, which is a one dimensional array with struct data type.
This struct has to contain fields named 'x', 'y' and for 3D fields ('z'). These names are configurable with the
'coordinate_names' parameter. The struct can have also other fields that can be read and written in the kernel, for
example boundary parameters.
index_fields: list of index fields, i.e. 1D fields with struct data type
coordinate_names: name of the coordinate fields in the struct data type
Example:
>>> import pystencils as ps
>>> import numpy as np
>>>
>>> # Index field stores the indices of the cell to visit together with optional values
>>> index_arr_dtype = np.dtype([('x', np.int32), ('y', np.int32), ('val', np.double)])
>>> index_arr = np.array([(1, 1, 0.1), (2, 2, 0.2), (3, 3, 0.3)], dtype=index_arr_dtype)
>>> idx_field = ps.fields(idx=index_arr)
>>>
>>> # Additional values stored in index field can be accessed in the kernel as well
>>> s, d = ps.fields('s, d: [2D]')
>>> assignment = ps.Assignment(d[0,0], 2 * s[0, 1] + 2 * s[1, 0] + idx_field('val'))
>>> ast = create_indexed_kernel(assignment, [idx_field], coordinate_names=('x', 'y'))
>>> kernel = ast.compile()
>>> d_arr = np.zeros([5, 5])
>>> kernel(s=np.ones([5, 5]), d=d_arr, idx=index_arr)
>>> d_arr
array([[0. , 0. , 0. , 0. , 0. ],
[0. , 4.1, 0. , 0. , 0. ],
[0. , 0. , 4.2, 0. , 0. ],
[0. , 0. , 0. , 4.3, 0. ],
[0. , 0. , 0. , 0. , 0. ]])
"""
if isinstance(assignments, Assignment):
assignments = [assignments]
elif isinstance(assignments, AssignmentCollection):
assignments = assignments.all_assignments
if target == 'cpu':
from pystencils.cpu import create_indexed_kernel
from pystencils.cpu import add_openmp
ast = create_indexed_kernel(assignments, index_fields=index_fields, type_info=data_type,
coordinate_names=coordinate_names)
if cpu_openmp:
add_openmp(ast, num_threads=cpu_openmp)
return ast
elif target == 'llvm':
raise NotImplementedError("Indexed kernels are not yet supported in LLVM backend")
elif target == 'gpu':
from pystencils.gpucuda import created_indexed_cuda_kernel
idx_creator = indexing_creator_from_params(gpu_indexing, gpu_indexing_params)
ast = created_indexed_cuda_kernel(assignments, index_fields, type_info=data_type,
coordinate_names=coordinate_names, indexing_creator=idx_creator)
return ast
else:
raise ValueError("Unknown target %s. Has to be either 'cpu' or 'gpu'" % (target,))
def create_staggered_kernel_from_assignments(assignments, **kwargs):
assert 'iteration_slice' not in kwargs and 'ghost_layers' not in kwargs
lhs_fields = {a.lhs.atoms(Field.Access) for a in assignments}
assert len(lhs_fields) == 1
staggered_field = lhs_fields.pop()
dim = staggered_field.spatial_dimensions
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)]
guarded_assignments = []
for d in range(dim):
cond = sp.And(*[conditions[i] for i in range(dim) if d != i])
guarded_assignments.append(Conditional(cond, Block(assignments)))
def create_staggered_kernel(staggered_field, expressions, subexpressions=(), target='cpu', **kwargs):
"""Kernel that updates a staggered field.
.. image:: /img/staggered_grid.svg
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'
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'
dim = staggered_field.spatial_dimensions
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 = []
for d in range(dim):
cond = sp.And(*[conditions[i] for i in range(dim) if d != i])
if staggered_field.index_dimensions == 1:
assignments = [Assignment(staggered_field(d), expressions[d])]
a_coll = AssignmentCollection(assignments, list(subexpressions)).new_filtered([staggered_field(d)])
elif staggered_field.index_dimensions == 2:
assert staggered_field.has_fixed_index_shape
assignments = [Assignment(staggered_field(d, i), expr) 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])])
sp_assignments = [SympyAssignment(a.lhs, a.rhs) for a in a_coll.all_assignments]
final_assignments.append(Conditional(cond, Block(sp_assignments)))
ghost_layers = [(1, 0)] * dim
cpu_vectorize_info = kwargs.get('cpu_vectorize_info', None)
if cpu_vectorize_info:
del kwargs['cpu_vectorize_info']
ast = create_kernel(final_assignments, ghost_layers=ghost_layers, target=target, **kwargs)
if target == 'cpu':
remove_conditionals_in_staggered_kernel(ast)
if cpu_vectorize_info is True:
vectorize(ast)
elif isinstance(cpu_vectorize_info, dict):
vectorize(ast, **cpu_vectorize_info)
return ast