"""
Authors: Pratik Bhatu.
Copyright:
Copyright (c) 2021 Microsoft Research
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import tempfile
import sys
import os
import shutil
import re
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
import CompilerScripts.parse_config as parse_config
import CompileTFGraph
import numpy as np
import subprocess
import threading
class Config:
def __init__(self, mode):
self.config = {
"model_name": "model.pb",
"scale": 23,
"bitlength": 64,
"save_weights": True,
}
if mode == "CPP":
self.config["target"] = "CPP"
elif mode == "3PC":
self.config["target"] = "PORTHOS"
elif mode == "2PC_OT":
self.config["target"] = "PORTHOS2PC"
self.config["bitlength"] = 41
self.config["scale"] = 12
self.config["backend"] = "OT"
elif mode == "2PC_HE":
self.config["target"] = "PORTHOS2PC"
self.config["bitlength"] = 41
self.config["scale"] = 12
self.config["backend"] = "HE"
else:
assert False, "Mode has to be one of CPP/3PC/2PC_OT/2PC_HE"
def add_input(self, tensor_op):
input_name = tensor_op.op.name
shape = tensor_op.shape.as_list()
shape_string = ",".join(map(str, shape))
inputs = self.config.get("input_tensors")
if inputs == None:
self.config["input_tensors"] = {input_name: shape_string}
else:
self.config["input_tensors"][input_name] = shape_string
return self
def add_output(self, tensor_op):
output_name = tensor_op.op.name
outputs = self.config.get("output_tensors")
if outputs == None:
self.config["output_tensors"] = [output_name]
else:
self.config["output_tensors"].append(output_name)
return self
def get_params(config):
return parse_config.parse_config(config)
def make_dir(path):
if os.path.exists(path):
shutil.rmtree(path, ignore_errors=True)
else:
os.mkdir(path)
return
def save_graph(graph_def, config, test_dir):
fname = config["model_name"]
fpath = os.path.join(test_dir, fname)
with open(fpath, "wb") as f:
f.write(graph_def.SerializeToString())
print("\n\nfile name: ", f.name, "\n\n\n")
config["model_name"] = fpath
return
def convert_raw_output_to_np(filename, bitlength, scale):
matcher = re.compile(r"[-]?[0-9]+")
scaled_array = []
with open(filename, "r") as f:
for line in f:
match = matcher.fullmatch(line.rstrip())
if match:
unsigned_number = int(match.group(0))
number = (
unsigned_number
if (unsigned_number < 2 ** (bitlength - 1))
else unsigned_number - 2 ** bitlength
)
scaled_array.append(float(number) / (2 ** scale))
return np.array(scaled_array)
class Program:
def __init__(self, program_path, model_weight_path, params, test_dir):
self.program_path = program_path
self.model_weight_path = model_weight_path
self.scale = params["scale"]
self.bitlength = params["bitlength"]
self.target = params["target"]
self.test_dir = test_dir
def run(self, inputs, timeoutSeconds):
# scale input and dump to file
inputs_scaled = os.path.join(
self.test_dir, "input_fixedpt_scale_" + str(self.scale) + ".inp"
)
with open(inputs_scaled, "w") as ff:
for i in inputs:
for xx in np.nditer(i, order="C"):
ff.write(str(int(xx * (1 << self.scale))) + " ")
ff.write("\n")
raw_output = os.path.join(self.test_dir, "raw_output")
if self.target == "CPP":
os.system(
"cat {inputs} {weights} | {program} > {output}".format(
program=self.program_path,
inputs=inputs_scaled,
weights=self.model_weight_path,
output=raw_output,
)
)
elif self.target == "PORTHOS":
util_dir = os.path.dirname(os.path.abspath(__file__))
porthos_dir = os.path.join(util_dir, "..", "..", "Porthos")
ip_addr = os.path.join(porthos_dir, "files", "addresses")
keys_dir = os.path.join(porthos_dir, "files", "keys")
client_cmd = (
"{program} 0 {ip_addr_file} {keys_dir} < {input} > {output}".format(
program=self.program_path,
ip_addr_file=ip_addr,
input=inputs_scaled,
output=raw_output,
keys_dir=keys_dir,
)
)
server_cmd = "{program} 1 {ip_addr_file} {keys_dir} < {input}".format(
program=self.program_path,
ip_addr_file=ip_addr,
input=self.model_weight_path,
keys_dir=keys_dir,
)
party2_cmd = "{program} 2 {ip_addr_file} {keys_dir}".format(
program=self.program_path, ip_addr_file=ip_addr, keys_dir=keys_dir
)
commands = [client_cmd, server_cmd, party2_cmd]
procs = [subprocess.Popen(i, shell=True) for i in commands]
for p in procs:
try:
p.wait(timeoutSeconds)
except subprocess.TimeoutExpired:
p.kill()
elif self.target == "PORTHOS2PC":
util_dir = os.path.dirname(os.path.abspath(__file__))
sci_dir = os.path.join(util_dir, "..", "..", "SCI")
port = 1234
client_cmd = "{program} r=2 p={port} < {input} > {output}".format(
program=self.program_path,
port=port,
input=inputs_scaled,
output=raw_output,
)
server_cmd = "{program} r=1 p={port} < {input} > /dev/null".format(
program=self.program_path,
port=port,
input=self.model_weight_path,
output=raw_output,
)
commands = [client_cmd, server_cmd]
procs = [subprocess.Popen(i, shell=True) for i in commands]
for p in procs:
try:
p.wait(timeoutSeconds)
except subprocess.TimeoutExpired:
p.kill()
return convert_raw_output_to_np(raw_output, self.bitlength, self.scale)
class Compiler:
def __init__(self, graph, config, test_dir):
self.graph_def = graph.as_graph_def()
self.config = config.config
self.test_dir = test_dir
def compile_and_run(self, inputs, timeoutSeconds=40):
save_graph(self.graph_def, self.config, self.test_dir)
params = get_params(self.config)
print(params)
(output_program, model_weight_file) = CompileTFGraph.generate_code(
params, debug=False
)
prog = Program(output_program, model_weight_file, params, self.test_dir)
output = prog.run(inputs, timeoutSeconds)
return output
def assert_almost_equal(tf_output, mpc_tensor, precision):
if tf_output.shape == (0,):
return
np.testing.assert_almost_equal(tf_output.flatten(), mpc_tensor, decimal=precision)
return