def finalize(self):
inferencer = StitchingInferencer(engine=self.engine,
value_map=self.value_map,
quote=self._quote)
hasher = TypedtreeHasher()
# Iterate inference to fixed point.
old_typedtree_hash = None
while True:
inferencer.visit(self.typedtree)
typedtree_hash = hasher.visit(self.typedtree)
if old_typedtree_hash == typedtree_hash:
break
old_typedtree_hash = typedtree_hash
# When we have an excess of type information, sometimes we can infer every type
# in the AST without discovering every referenced attribute of host objects, so
# do one last pass unconditionally.
inferencer.visit(self.typedtree)
# After we have found all functions, synthesize a module to hold them.
source_buffer = source.Buffer("", "<synthesized>")
self.typedtree = asttyped.ModuleT(
typing_env=self.globals, globals_in_scope=set(),
body=self.typedtree, loc=source.Range(source_buffer, 0, 0))
def _add(self, fragment):
range_from = len(self.source)
self.source += fragment
range_to = len(self.source)
return source.Range(self.source_buffer,
range_from,
range_to,
expanded_from=self.expanded_from)
def finalize(self):
inferencer = StitchingInferencer(engine=self.engine,
value_map=self.value_map,
quote=self._quote)
typedtree_hasher = TypedtreeHasher()
# Iterate inference to fixed point.
old_typedtree_hash = None
old_attr_count = None
while True:
inferencer.visit(self.typedtree)
typedtree_hash = typedtree_hasher.visit(self.typedtree)
attr_count = self.embedding_map.attribute_count()
if old_typedtree_hash == typedtree_hash and old_attr_count == attr_count:
break
old_typedtree_hash = typedtree_hash
old_attr_count = attr_count
# After we've discovered every referenced attribute, check if any kernel_invariant
# specifications refers to ones we didn't encounter.
for host_type in self.embedding_map.type_map:
instance_type, constructor_type = self.embedding_map.type_map[host_type]
if not hasattr(instance_type, "constant_attributes"):
# Exceptions lack user-definable attributes.
continue
for attribute in instance_type.constant_attributes:
if attribute in instance_type.attributes:
# Fast path; if the ARTIQ Python type has the attribute, then every observed
# value is guaranteed to have it too.
continue
for value, loc in self.value_map[instance_type]:
if hasattr(value, attribute):
continue
diag = diagnostic.Diagnostic("warning",
"object {value} of type {typ} declares attribute '{attr}' as "
"kernel invariant, but the instance referenced here does not "
"have this attribute",
{"value": repr(value),
"typ": types.TypePrinter().name(instance_type, max_depth=0),
"attr": attribute},
loc)
self.engine.process(diag)
# After we have found all functions, synthesize a module to hold them.
source_buffer = source.Buffer("", "<synthesized>")
self.typedtree = asttyped.ModuleT(
typing_env=self.globals, globals_in_scope=set(),
body=self.typedtree, loc=source.Range(source_buffer, 0, 0))
def _function_loc(self, function):
if isinstance(function, SpecializedFunction):
function = function.host_function
if hasattr(function, 'artiq_embedded') and function.artiq_embedded.function:
function = function.artiq_embedded.function
if isinstance(function, str):
return source.Range(source.Buffer(function, "<string>"), 0, 0)
filename = function.__code__.co_filename
line = function.__code__.co_firstlineno
name = function.__code__.co_name
source_line = linecache.getline(filename, line).lstrip()
while source_line.startswith("@") or source_line == "":
line += 1
source_line = linecache.getline(filename, line).lstrip()
if "<lambda>" in function.__qualname__:
column = 0 # can't get column of lambda
else:
column = re.search("def", source_line).start(0)
source_buffer = source.Buffer(source_line, filename, line)
return source.Range(source_buffer, column, column)
def _function_loc(self, function):
filename = function.__code__.co_filename
line = function.__code__.co_firstlineno
name = function.__code__.co_name
source_line = linecache.getline(filename, line).lstrip()
while source_line.startswith("@") or source_line == "":
line += 1
source_line = linecache.getline(filename, line).lstrip()
if "<lambda>" in function.__qualname__:
column = 0 # can't get column of lambda
else:
column = re.search("def", source_line).start(0)
source_buffer = source.Buffer(source_line, filename, line)
return source.Range(source_buffer, column, column)
def finalize(self):
inferencer = StitchingInferencer(engine=self.engine,
value_map=self.value_map,
quote=self._quote)
hasher = TypedtreeHasher()
# Iterate inference to fixed point.
old_typedtree_hash = None
while True:
inferencer.visit(self.typedtree)
typedtree_hash = hasher.visit(self.typedtree)
if old_typedtree_hash == typedtree_hash:
break
old_typedtree_hash = typedtree_hash
# After we have found all functions, synthesize a module to hold them.
source_buffer = source.Buffer("", "<synthesized>")
self.typedtree = asttyped.ModuleT(typing_env=self.globals,
globals_in_scope=set(),
body=self.typedtree,
loc=source.Range(
source_buffer, 0, 0))
def _quote_embedded_function(self, function, flags):
if isinstance(function, SpecializedFunction):
host_function = function.host_function
else:
host_function = function
if not hasattr(host_function, "artiq_embedded"):
raise ValueError("{} is not an embedded function".format(repr(host_function)))
# Extract function source.
embedded_function = host_function.artiq_embedded.function
source_code = inspect.getsource(embedded_function)
filename = embedded_function.__code__.co_filename
module_name = embedded_function.__globals__['__name__']
first_line = embedded_function.__code__.co_firstlineno
# Extract function annotation.
signature = inspect.signature(embedded_function)
loc = self._function_loc(embedded_function)
arg_types = OrderedDict()
optarg_types = OrderedDict()
for param in signature.parameters.values():
if param.kind == inspect.Parameter.VAR_POSITIONAL or \
param.kind == inspect.Parameter.VAR_KEYWORD:
diag = diagnostic.Diagnostic("error",
"variadic arguments are not supported; '{argument}' is variadic",
{"argument": param.name},
self._function_loc(function),
notes=self._call_site_note(loc, fn_kind='kernel'))
self.engine.process(diag)
arg_type = self._type_of_param(function, loc, param, fn_kind='kernel')
if param.default is inspect.Parameter.empty:
arg_types[param.name] = arg_type
else:
optarg_types[param.name] = arg_type
if signature.return_annotation is not inspect.Signature.empty:
ret_type = self._extract_annot(function, signature.return_annotation,
"return type", loc, fn_kind='kernel')
else:
ret_type = types.TVar()
# Extract function environment.
host_environment = dict()
host_environment.update(embedded_function.__globals__)
cells = embedded_function.__closure__
cell_names = embedded_function.__code__.co_freevars
host_environment.update({var: cells[index] for index, var in enumerate(cell_names)})
# Find out how indented we are.
initial_whitespace = re.search(r"^\s*", source_code).group(0)
initial_indent = len(initial_whitespace.expandtabs())
# Parse.
source_buffer = source.Buffer(source_code, filename, first_line)
lexer = source_lexer.Lexer(source_buffer, version=sys.version_info[0:2],
diagnostic_engine=self.engine)
lexer.indent = [(initial_indent,
source.Range(source_buffer, 0, len(initial_whitespace)),
initial_whitespace)]
parser = source_parser.Parser(lexer, version=sys.version_info[0:2],
diagnostic_engine=self.engine)
function_node = parser.file_input().body[0]
# Mangle the name, since we put everything into a single module.
full_function_name = "{}.{}".format(module_name, host_function.__qualname__)
if isinstance(function, SpecializedFunction):
instance_type = function.instance_type
function_node.name = "_Z{}{}I{}{}Ezz".format(len(full_function_name), full_function_name,
len(instance_type.name), instance_type.name)
else:
function_node.name = "_Z{}{}zz".format(len(full_function_name), full_function_name)
# Record the function in the function map so that LLVM IR generator
# can handle quoting it.
self.embedding_map.store_function(function, function_node.name)
# Fill in the function type before typing it to handle recursive
# invocations.
self.functions[function] = types.TFunction(arg_types, optarg_types, ret_type)
# Rewrite into typed form.
asttyped_rewriter = StitchingASTTypedRewriter(
engine=self.engine, prelude=self.prelude,
globals=self.globals, host_environment=host_environment,
quote=self._quote)
function_node = asttyped_rewriter.visit_quoted_function(function_node, embedded_function)
function_node.flags = flags
# Add it into our typedtree so that it gets inferenced and codegen'd.
self._inject(function_node)
# Tie the typing knot.
self.functions[function].unify(function_node.signature_type)
return function_node
def _quote_embedded_function(self, function, flags):
if isinstance(function, SpecializedFunction):
host_function = function.host_function
else:
host_function = function
if not hasattr(host_function, "artiq_embedded"):
raise ValueError("{} is not an embedded function".format(repr(host_function)))
# Extract function source.
embedded_function = host_function.artiq_embedded.function
source_code = inspect.getsource(embedded_function)
filename = embedded_function.__code__.co_filename
module_name = embedded_function.__globals__['__name__']
first_line = embedded_function.__code__.co_firstlineno
# Extract function environment.
host_environment = dict()
host_environment.update(embedded_function.__globals__)
cells = embedded_function.__closure__
cell_names = embedded_function.__code__.co_freevars
host_environment.update({var: cells[index] for index, var in enumerate(cell_names)})
# Find out how indented we are.
initial_whitespace = re.search(r"^\s*", source_code).group(0)
initial_indent = len(initial_whitespace.expandtabs())
# Parse.
source_buffer = source.Buffer(source_code, filename, first_line)
lexer = source_lexer.Lexer(source_buffer, version=sys.version_info[0:2],
diagnostic_engine=self.engine)
lexer.indent = [(initial_indent,
source.Range(source_buffer, 0, len(initial_whitespace)),
initial_whitespace)]
parser = source_parser.Parser(lexer, version=sys.version_info[0:2],
diagnostic_engine=self.engine)
function_node = parser.file_input().body[0]
# Mangle the name, since we put everything into a single module.
full_function_name = "{}.{}".format(module_name, host_function.__qualname__)
if isinstance(function, SpecializedFunction):
instance_type = function.instance_type
function_node.name = "_Z{}{}I{}{}Ezz".format(len(full_function_name), full_function_name,
len(instance_type.name), instance_type.name)
else:
function_node.name = "_Z{}{}zz".format(len(full_function_name), full_function_name)
# Record the function in the function map so that LLVM IR generator
# can handle quoting it.
self.embedding_map.store_function(function, function_node.name)
# Memoize the function type before typing it to handle recursive
# invocations.
self.functions[function] = types.TVar()
# Rewrite into typed form.
asttyped_rewriter = StitchingASTTypedRewriter(
engine=self.engine, prelude=self.prelude,
globals=self.globals, host_environment=host_environment,
quote=self._quote)
function_node = asttyped_rewriter.visit_quoted_function(function_node, embedded_function)
function_node.flags = flags
# Add it into our typedtree so that it gets inferenced and codegen'd.
self._inject(function_node)
# Tie the typing knot.
self.functions[function].unify(function_node.signature_type)
return function_node