def visit_StencilModel(self, node):
self.argdict = dict()
for i in range(len(node.input_grids)):
self.var_names.append(node.input_grids[i].name)
self.argdict[node.input_grids[i].name] = self.input_grids[i]
self.argdict[self.output_grid_name] = self.output_grid
assert node.border_kernel.body == [], 'Border kernels not yet implemented'
func_name = "kernel"
arg_names = [x.name
for x in node.input_grids] + [self.output_grid_name]
args = [
cpp_ast.Pointer(cpp_ast.Value("PyObject", x)) for x in arg_names
]
body = cpp_ast.Block()
# generate the code to unpack arrays into C++ pointers and macros for accessing
# the arrays
body.extend([self.gen_array_macro_definition(x) for x in self.argdict])
body.extend(self.gen_array_unpack())
body.append(self.visit_interior_kernel(node.interior_kernel))
return cpp_ast.FunctionBody(
cpp_ast.FunctionDeclaration(cpp_ast.Value("void", func_name),
args), body)
def visit_arguments(self, node):
args = map(lambda arg: self.visit(arg), node.args)
defaults = map(lambda tup: self.visit(tup), node.defaults)
annotations = dict()
for tup in defaults:
annotations[tup[0].text] = tuple([elt.text for elt in tup])
ret = []
if len(self._data_model) != len(args):
raise TypeError('Expected %d arguments, received %d' %
(len(args), len(self._data_model)))
foo = zip(args, self._data_model)
for arg, model in foo:
model.name = arg.name
model.should_subsample = not (model.name in annotations
and 'nosubsample'
in annotations[model.name])
if not model.should_subsample and type(model) is not DataModel:
model = DataModel.clone(model)
model.dimensions = tuple([len(model)] + model.dimensions[1:])
ret.append(cpp_ast.Pointer(cpp_ast.Value(model.ctype(), arg.name)))
self.data_model[arg.name] = model
self.arg_model.append(model)
if self.weighted:
ret.append(
cpp_ast.Pointer(
cpp_ast.Value("const unsigned int",
cpp_ast.CName('_blb_weights'))))
return ret
def gen_array_unpack(self):
ret = [
cpp_ast.Assign(
cpp_ast.Pointer(cpp_ast.Value("npy_double", "_my_" + x)),
cpp_ast.TypeCast(
cpp_ast.Pointer(cpp_ast.Value("npy_double", "")),
cpp_ast.FunctionCall(cpp_ast.CName("PyArray_DATA"),
params=[cpp_ast.CName(x)])))
for x in self.argdict.keys()
]
return ret
def visit_interior_kernel(self, node):
cur_node, ret_node = self.gen_loops(node)
body = cpp_ast.Block()
self.output_index_var = cpp_ast.CName(self.gen_fresh_var())
body.append(cpp_ast.Value("int", self.output_index_var))
body.append(
cpp_ast.Assign(
self.output_index_var,
self.gen_array_macro(self.output_grid_name,
[cpp_ast.CName(x)
for x in self.dim_vars])))
replaced_body = None
for gridname in self.argdict.keys():
replaced_body = [
ast_tools.ASTNodeReplacer(ast.Name(gridname, None),
ast.Name("_my_" + gridname,
None)).visit(x)
for x in node.body
]
body.extend([self.visit(x) for x in replaced_body])
cur_node.body = body
return ret_node
def declare(self, converter):
""" Returns a c++ statement declaring this object. """
if self.is_scalar():
return cpp_ast.Assign(
cpp_ast.Value(self.scalar_t.ctype(), self.ref_name()),
cpp_ast.CNumber(0))
else:
self._declare = True
return cpp_ast.Expression()
def render(self, node):
declarator, args, body = self.visit(node)
self.inject_registers(body)
args.append(
cpp_ast.Pointer(
cpp_ast.Value(self.retModel.scalar_t.ctype(), '_blb_result')))
model = cpp_ast.FunctionBody(
cpp_ast.FunctionDeclaration(declarator, args), body)
self.desired_funcs.extend(functions.flush_requests())
return str(model)
def inject_registers(self, body):
for model in self.data_model.itervalues():
if model.should_declare():
#TODO: This is an awkward way to do this.
body.contents.insert(
0,
cpp_ast.Call('memset', [
model.ref_name(), 0,
model.size() * model.scalar_t.csize()
]))
body.contents.insert(
0,
cpp_ast.Value(
model.scalar_t.ctype(),
cpp_ast.CName("%s [%d]" %
(model.ref_name(), model.size()))))
def visit_FunctionDef(self, node):
declarator = cpp_ast.Value('void', node.name)
args = self.visit(node.args)
body = cpp_ast.Block([self.visit(x) for x in node.body])
return declarator, args, body
def visit_For(self, node):
_iters = self.visit(node.iter)
targets = self.visit(node.target)
#are we iterating over a data object?
if type(_iters) is cpp_ast.CName and _iters.name in self.data_model:
_iters = [_iters]
targets = [targets]
#co-iterating over a set of datasets!
if type(_iters) is list:
#set up the loop variable and weight
self.loopvar.append(self.loopvar[-1] + 'i')
loopvar = cpp_ast.CName(self.loopvar[-1])
body = []
weight_loop = self.weighted and reduce(lambda a, b: a or b, [
(_iter.name in self.data_model
and self.data_model[_iter.name].should_subsample)
for _iter in _iters
])
if weight_loop:
self.aggregate_on(loopvar)
# add the temporary children to the data model
for target, _iter in zip(targets, _iters):
if not (type(_iter) is cpp_ast.CName
and _iter.name in self.data_model):
raise ValueError(
"Cannot iterate over unknown data object: '%s'" %
_iter.name)
parent_model = self.data_model[_iter.name]
if type(target) is not cpp_ast.CName:
raise ValueError("Not a valid iteration target: '%s'" %
str(target))
if target.name in self.data_model:
raise ValueError("Cannot reuse iteration target: '%s'" %
target.name)
target_model = self.data_model[
target.name] = parent_model.branch(name=target.name)
ctype = target_model.scalar_t.ctype()
decl = cpp_ast.Value(
ctype, target.name) if target_model.is_scalar(
) else cpp_ast.Pointer(cpp_ast.Value(ctype, target.name))
init = parent_model.declare_child(target_model, loopvar)
#init = cpp_ast.Subscript( _iter, loopvar) if target_model.is_scalar() \
# else cpp_ast.BinOp( _iter, '+', cpp_ast.BinOp( loopvar , '*', parent_model.element_size() ) )
body.append(cpp_ast.Assign(decl, init))
# visit the body of the for
body += [self.visit(x) for x in node.body]
# generate the C for intializer
ret = cpp_ast.RawFor( cpp_ast.Assign( cpp_ast.Value( 'int', loopvar ), cpp_ast.CNumber(0) ), \
cpp_ast.Compare( loopvar, '<', cpp_ast.CNumber( self.data_model[ _iter.name ].dimensions[0] ) ), \
cpp_ast.UnaryOp( '++', loopvar ), cpp_ast.Block( body ) )
# remove temporary children from data model and otherwise clean up
self.loopvar = self.loopvar[:-1]
for target in targets:
del self.data_model[target.name]
if weight_loop:
self.aggregate_off(loopvar)
# return
return ret
#or perhaps, a known range.
elif isinstance(_iters, cpp_ast.CNumber):
return cpp_ast.RawFor( cpp_ast.Assign( cpp_ast.Value( 'int', targets ), cpp_ast.CNumber(0) ), \
cpp_ast.Compare( targets, '<', _iter ), cpp_ast.UnaryOp( '++', target ), \
cpp_ast.Block([ self.visit( x ) for x in node.body ] ) )
else:
raise ValueError(
'Loop iterand "%s" is not a numeric expression or known data object'
% str(_iters))
def visit_Assign(self, node):
lhs = self.visit(node.targets[0])
rhs = self.visit(node.value)
#find or create target model
#pass pointer into opencall, or do what is necessary
target = None
try:
target = self.get_or_create_model(lhs)
except ValueError:
pass
value = None
try:
value = self.get_or_create_model(rhs)
except TypeError:
pass
if value is not None: #RHS is a data object
if target is not None:
if type(target) != DataModelView:
raise ValueError(
"'%s' refers to a real buffer and cannot be reassigned to '%s'"
% (str(lhs), str(rhs)))
#TODO we allways assume here that the name must be assigned, which is not always the case.
name = target.name
self.data_model[name] = DataModelView(value, name)
return cpp_ast.Assign(cpp_ast.CName(target.ref_name()),
cpp_ast.CName(value.ref_name()))
elif type(lhs) == cpp_ast.CName:
self.data_model[lhs.name] = DataModelView(value, lhs.name)
data_type = value.scalar_t.ctype() if value.is_scalar(
) else value.scalar_t.ctype() + "*"
return cpp_ast.Assign(cpp_ast.Value(data_type, lhs.name),
cpp_ast.CName(value.ref_name()))
else:
raise ValueError(
"could not assign to '%s': not a data object or name" %
str(lhs))
elif type(rhs) == OpenCall:
if target is None and type(lhs) == cpp_ast.CName:
params = rhs.get_output_params()
self.data_model[lhs.name] = target = DataModel(
params['type'], params['shape'], None, lhs.name)
pre = target.declare(self)
post = rhs.write_to(target, self)
return cpp_ast.UnbracedBlock([pre, post])
elif target:
return rhs.write_to(target, self)
else:
raise ValueError(
"could not assign to '%s': not a data object or name" %
str(lhs))
elif type(rhs) == cpp_ast.CNumber:
if target is None and type(lhs) == cpp_ast.CName:
self.data_model[lhs.name] = target = DataModel(
type(rhs.num), [1], None, lhs.name)
return cpp_ast.Initializer(
cpp_ast.Value(target.scalar_t.ctype(), lhs.name), rhs.num)
elif target:
assert target.is_scalar(
), "'%s' is not a scalar data object" % target.name
assert target.scalar_t.matches( type( rhs.num ) ), \
"Type mismatch: '%s' is type '%s', but '%s' is type '%s'" % ( target.name, target.scalar_t, rhs.num, type(rhs.num ) )
return cpp_ast.Initializer(
cpp_ast.Value(taget.scalar_t.ctype(), target.name),
rhs.num)
else:
raise ValueError(
"could not assign to '%s': not a data object or name" %
str(lhs))
else:
raise ValueError("could not assign from '%s'" % str(rhs))
