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#!/usr/bin/python
import argparse
from textwrap import dedent
from subprocess import call
def parse_lines(lines):
# States
EMPTY = 0
OP = 1
MACRO = 2
parse_state = EMPTY
# Preprocess the macros
curr_macro = ""
macros = {}
index = 0
while index < len(lines):
line = lines[index]
if line.lower().startswith("macro"):
assert parse_state == EMPTY
macro_line = line.split(" ")
# Support macros that look like attributes
# e.g. macro - CONV_LIKE
curr_macro = " ".join(macro_line[1:])
assert curr_macro not in macros, 'Macro "{}" defined twice.'.format(
curr_macro
)
macros[curr_macro] = []
parse_state = MACRO
lines = lines[:index] + lines[index + 1 :]
continue
elif line.lower().startswith("endmacro"):
assert parse_state == MACRO
parse_state = EMPTY
lines = lines[:index] + lines[index + 1 :]
continue
elif parse_state == MACRO:
macros[curr_macro].append(line)
lines = lines[:index] + lines[index + 1 :]
continue
index += 1
index = 0
while index < len(lines):
line = lines[index]
if line in macros:
lines = lines[:index] + macros[line] + lines[index + 1 :]
index += len(macros[line]) - 1
index += 1
# Now parse the file
curr_op = ""
# dict of the form
# opName : { attributes: [], ... }
ops = {}
# To preserve parsing order for dependencies (for things like init_from)
op_list = []
for line in lines:
if not len(line):
continue
if line[0] == "-":
assert parse_state is OP
attr = [_.strip() for _ in line[1:].split(":")]
assert attr[0][0].isupper()
if len(attr) == 2: # attribute : type
ops[curr_op]["attributes"].append((attr[0], attr[1]))
elif len(attr) == 3: # attribute : type
ops[curr_op]["attributes"].append((attr[0], attr[1], attr[2]))
else:
op = [l.strip() for l in line.split(":")]
assert len(op[0].split(" ")) == 1
parse_state = OP
curr_op = op[0]
assert curr_op not in ops
ops[curr_op] = {}
op_list.append(curr_op)
if len(op) > 1:
ops[curr_op]["init_from"] = [op[1]]
ops[curr_op]["attributes"] = []
return ops, op_list
def gen_class(op, op_def):
attributes = op_def["attributes"]
attribute_args = []
default_init = "NeuralNetOperator(NNKind::{op})".format(op=op)
attribute_init = [default_init]
attribute_declarations = []
attribute_getters = []
attribute_setters = []
for attr in attributes:
lower_name = attr[0][0].lower() + attr[0][1:]
private_name = lower_name + "_"
default_arg = "" if len(attr) < 3 else " = {}".format(attr[2])
name = attr[0]
t = attr[1]
attr_arg = "{type} {lower_name}".format(
type=t, lower_name=lower_name + default_arg
)
attr_init = "{private_name}({lower_name})".format(
private_name=private_name, lower_name=lower_name)
attr_declare = "{type} {private_name};".format(
type=t, private_name=private_name)
attr_get = dedent(
"""
{type} get{name}() const {{
return {private_name};
}}
""".format(
type=t, name=name, private_name=private_name
)
)
attr_set = dedent(
"""
void set{name}({type} {lower_name}) {{
{private_name} = {lower_name};
}}
""".format(
type=t, name=name, private_name=private_name, lower_name=lower_name
)
)
attribute_args.append(attr_arg)
attribute_init.append(attr_init)
attribute_declarations.append(attr_declare)
attribute_getters.append(attr_get)
attribute_setters.append(attr_set)
extra_init = ""
if "init_from" in op_def:
for other_op in op_def["init_from"]:
lower_other_op = other_op[0].lower() + other_op[1:]
other_init = [default_init]
for attr in attributes:
lower_name = attr[0][0].lower() + attr[0][1:]
private_name = lower_name + "_"
other_init.append(
"{private_name}({other_op}.get{name}())".format(
name=attr[0], private_name=private_name, other_op=lower_other_op
)
)
init = dedent(
"""
{op}(const {other_op}& {lower_other_op}) :
{other_init} {{}}
""".format(
op=op,
other_op=other_op,
lower_other_op=lower_other_op,
other_init=",\n ".join(other_init),
)
)
extra_init += init
return dedent(
"""
class {op} : public NeuralNetOperator {{
public:
{op}({attribute_args}) :
{attribute_init} {{}}
{extra_init}
~{op}() {{}}
NOMNIGRAPH_DEFINE_NN_RTTI({op});
{getters}{setters}
private:
{attribute_declarations}
}};
""".format(
op=op,
extra_init=extra_init,
getters="".join(attribute_getters),
setters="".join(attribute_setters),
attribute_args=",\n".join(attribute_args),
attribute_init=",\n".join(attribute_init),
attribute_declarations="\n".join(attribute_declarations),
)
)
def gen_classes(ops, op_list):
f = ""
for op in op_list:
f += gen_class(op, ops[op])
return f
def gen_enum(op_list):
return ",\n".join([op for op in op_list]) + "\n"
def gen_names(op_list):
f = ""
for op in op_list:
f += dedent(
"""
case NNKind::{name}:
return \"{name}\";
""".format(
name=op
)
)
return f
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate op files.")
parser.add_argument("--install_dir", help="installation directory")
parser.add_argument("--source_def", help="ops.def", action="append")
args = parser.parse_args()
install_dir = args.install_dir
sources = args.source_def
lines = []
for source in sources:
with open(source, "rb") as f:
lines_tmp = f.readlines()
lines += [l.strip().decode("utf-8") for l in lines_tmp]
ops, op_list = parse_lines(lines)
with open(install_dir + "/OpClasses.h", "wb") as f:
f.write(gen_classes(ops, op_list).encode("utf-8"))
with open(install_dir + "/OpNames.h", "wb") as f:
f.write(gen_names(op_list).encode("utf-8"))
with open(install_dir + "/OpEnum.h", "wb") as f:
f.write(gen_enum(op_list).encode("utf-8"))
try:
cmd = ["clang-format", "-i", install_dir + "/OpClasses.h"]
call(cmd)
cmd = ["clang-format", "-i", install_dir + "/OpNames.h"]
call(cmd)
cmd = ["clang-format", "-i", install_dir + "/OpEnum.h"]
call(cmd)
except Exception:
pass
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