def get_class_properties(cls, self_name):
"""
Get a list of Property objects representing the properties of a class.
Args:
cls: The class to get properties of.
self_name: The name of the class that the properties should belong to.
Returns:
A list of Property objects corresponding to the properties of cls. Property
here refers to the subclass of TreeView.
"""
props = inspect.getmembers(cls,
predicate=lambda m: isinstance(m, property))
# Any property that should not compiled must be in this list on the Module.
unused_properties = getattr(cls, "__jit_unused_properties__", [])
# Create Property TreeView objects from inspected property objects.
properties = []
for prop in props:
if prop[0] not in unused_properties and not should_drop(prop[1].fget):
getter = get_jit_def(prop[1].fget,
f"__{prop[0]}_getter",
self_name=self_name)
setter = get_jit_def(prop[1].fset,
f"__{prop[0]}_setter",
self_name=self_name) if prop[1].fset else None
properties.append(
Property(getter.range(), Ident(getter.range(), prop[0]),
getter, setter))
return properties
def get_jit_def(fn, def_name, self_name=None, is_classmethod=False):
"""
Build a JIT AST (TreeView) from the given function.
Args:
fn: A function object to compile or a pre-parsed ParsedDef object
def_name: The name to give to the resulting AST object. This is not
always the same as `fn.__name__`, for example:
def _forward(self):
...
forward = _forward
In this case, the `__name__` attribute of the function object is "_forward",
but we want the result AST to have the name "forward".
self_name: If this function is a method, what the type name of `self` is.
"""
parsed_def = parse_def(fn) if not isinstance(fn, ParsedDef) else fn
type_line = torch.jit.annotations.get_type_line(parsed_def.source)
fn_def = parsed_def.ast.body[0]
if is_classmethod:
arg_name = fn_def.args.args[0].arg
# Insert a statement that assigns the first argument to the class
assign_stmt = ast.parse(f"{arg_name} = {self_name}").body[0]
fn_def.body.insert(0, assign_stmt)
# Swap out the function signature and body if it is unused
if should_drop(fn):
unused_fn_def = ast.parse(
"def unused_fn(self: Any):\n\traise RuntimeError(\"Cannot call @unused methods\")"
)
if len(unused_fn_def.body) != 1 or not isinstance(
unused_fn_def.body[0], ast.FunctionDef):
raise RuntimeError(
f"Expected a single top-level function: {parsed_def.filename}:{parsed_def.file_lineno}"
)
unused_def = unused_fn_def.body[0]
fn_def.body = unused_def.body
# kwarg/vararg not supported by `build_def`
fn_def.args.kwarg = fn_def.args.vararg = None
for arg in fn_def.args.args + fn_def.args.kwonlyargs:
# Replace potentially unsupported type annotations by "Any"
arg.annotation = unused_def.args.args[0].annotation
# If MonkeyType is installed, get all the consolidated type traces
# for the arguments from type_trace_db
type_trace_db = torch.jit._script._get_type_trace_db()
pdt_arg_types = None
if monkeytype_trace and not isinstance(fn, ParsedDef):
qualname = get_qualified_name(fn)
pdt_arg_types = type_trace_db.get_args_types(qualname)
return build_def(parsed_def.ctx,
fn_def,
type_line,
def_name,
self_name=self_name,
pdt_arg_types=pdt_arg_types)
def get_jit_def(fn, def_name, self_name=None, is_classmethod=False):
"""
Build a JIT AST (TreeView) from the given function.
Args:
fn: A function object to compile
def_name: The name to give to the resulting AST object. This is not
always the same as `fn.__name__`, for example:
def _forward(self):
...
forward = _forward
In this case, the `__name__` attribute of the function object is "_forward",
but we want the result AST to have the name "forward".
self_name: If this function is a method, what the type name of `self` is.
"""
sourcelines, file_lineno, filename = get_source_lines_and_file(
fn, torch._C.ErrorReport.call_stack())
sourcelines = normalize_source_lines(sourcelines)
source = ''.join(sourcelines)
dedent_src = dedent(source)
py_ast = ast.parse(dedent_src)
if len(py_ast.body) != 1 or not isinstance(py_ast.body[0],
ast.FunctionDef):
raise RuntimeError(
f"Expected a single top-level function: {filename}:{file_lineno}")
leading_whitespace_len = len(source.split('\n', 1)[0]) - len(
dedent_src.split('\n', 1)[0])
type_line = torch.jit.annotations.get_type_line(source)
ctx = SourceContext(source, filename, file_lineno, leading_whitespace_len,
True)
fn_def = py_ast.body[0]
if is_classmethod:
arg_name = fn_def.args.args[0].arg
# Insert a statement that assigns the first argument to the class
assign_stmt = ast.parse(f"{arg_name} = {self_name}").body[0]
fn_def.body.insert(0, assign_stmt)
# Swap out the function signature and body if it is unused
if should_drop(fn):
unused_fn_def = ast.parse(
"def unused_fn(self: Any):\n\traise RuntimeError(\"Cannot call @unused methods\")"
)
if len(unused_fn_def.body) != 1 or not isinstance(
unused_fn_def.body[0], ast.FunctionDef):
raise RuntimeError(
f"Expected a single top-level function: {filename}:{file_lineno}"
)
unused_def = unused_fn_def.body[0]
fn_def.body = unused_def.body
# kwarg/vararg not supported by `build_def`
fn_def.args.kwarg = fn_def.args.vararg = None
for arg in fn_def.args.args + fn_def.args.kwonlyargs:
# Replace potentially unsupported type annotations by "Any"
arg.annotation = unused_def.args.args[0].annotation
return build_def(ctx, fn_def, type_line, def_name, self_name=self_name)
def get_jit_def(fn, def_name, self_name=None):
"""
Build a JIT AST (TreeView) from the given function.
Arguments:
fn: A function object to compile
def_name: The name to give to the resulting AST object. This is not
always the same as `fn.__name__`, for example:
def _forward(self):
...
forward = _forward
In this case, the `__name__` attribute of the function object is "_forward",
but we want the result AST to have the name "forward".
self_name: If this function is a method, what the type name of `self` is.
"""
sourcelines, file_lineno, filename = get_source_lines_and_file(
fn, torch._C.ErrorReport.call_stack())
source = ''.join(sourcelines)
dedent_src = dedent(source)
py_ast = ast.parse(dedent_src)
if len(py_ast.body) != 1 or not isinstance(py_ast.body[0],
ast.FunctionDef):
raise RuntimeError("Expected a single top-level function")
leading_whitespace_len = len(source.split('\n', 1)[0]) - len(
dedent_src.split('\n', 1)[0])
type_line = torch.jit.annotations.get_type_line(source)
ctx = SourceContext(source, filename, file_lineno, leading_whitespace_len,
True)
fn_def = py_ast.body[0]
# Swap out the function body if it is unused
if should_drop(fn):
unused_fn_def = ast.parse(
"def unused_fn(self):\n\traise RuntimeError(\"Cannot call @unused methods\")"
).body[0]
fn_def.body = unused_fn_def.body
return build_def(ctx, fn_def, type_line, def_name, self_name=self_name)