converter.py

# Copyright 2018 Xiaomi, Inc.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import filelock
import glob
import hashlib
import os
import re
import sh
import subprocess
import sys
import urllib
import yaml

from enum import Enum

import sh_commands
from sh_commands import BuildType

from common import CaffeEnvType
from common import DeviceType
from common import mace_check
from common import MaceLogger
from common import StringFormatter

################################
# common definitions
################################
BUILD_OUTPUT_DIR = 'build'
PHONE_DATA_DIR = "/data/local/tmp/mace_run"
MODEL_OUTPUT_DIR_NAME = 'model'
MODEL_HEADER_DIR_PATH = 'include/mace/public'
BUILD_TMP_DIR_NAME = '_tmp'
BUILD_TMP_GENERAL_OUTPUT_DIR_NAME = 'general'
OUTPUT_LIBRARY_DIR_NAME = 'lib'
OUTPUT_OPENCL_BINARY_DIR_NAME = 'opencl'
OUTPUT_OPENCL_BINARY_FILE_NAME = 'compiled_opencl_kernel'
CL_COMPILED_BINARY_FILE_NAME = "mace_cl_compiled_program.bin"
CODEGEN_BASE_DIR = 'mace/codegen'
MODEL_CODEGEN_DIR = CODEGEN_BASE_DIR + '/models'
LIBMACE_SO_TARGET = "//mace:libmace.so"
MACE_RUN_STATIC_TARGET = "//mace/tools/validation:mace_run_static"
MACE_RUN_SHARED_TARGET = "//mace/tools/validation:mace_run_shared"
ALL_SOC_TAG = 'all'

ABITypeStrs = [
    'armeabi-v7a',
    'arm64-v8a',
    'host',
]


class ABIType(object):
    armeabi_v7a = 'armeabi-v7a'
    arm64_v8a = 'arm64-v8a'
    host = 'host'


PlatformTypeStrs = [
    "tensorflow",
    "caffe",
]
PlatformType = Enum('PlatformType', [(ele, ele) for ele in PlatformTypeStrs],
                    type=str)

RuntimeTypeStrs = [
    "cpu",
    "gpu",
    "dsp",
    "cpu+gpu"
]


class RuntimeType(object):
    cpu = 'cpu'
    gpu = 'gpu'
    dsp = 'dsp'
    cpu_gpu = 'cpu+gpu'


CPUDataTypeStrs = [
    "fp32",
]

CPUDataType = Enum('CPUDataType', [(ele, ele) for ele in CPUDataTypeStrs],
                   type=str)

GPUDataTypeStrs = [
    "fp16_fp32",
    "fp32_fp32",
]

GPUDataType = Enum('GPUDataType', [(ele, ele) for ele in GPUDataTypeStrs],
                   type=str)

DSPDataTypeStrs = [
    "uint8",
]

DSPDataType = Enum('DSPDataType', [(ele, ele) for ele in DSPDataTypeStrs],
                   type=str)

WinogradParameters = [0, 2, 4]


class DefaultValues(object):
    omp_num_threads = -1,
    cpu_affinity_policy = 1,
    gpu_perf_hint = 3,
    gpu_priority_hint = 3,


class YAMLKeyword(object):
    library_name = 'library_name'
    target_abis = 'target_abis'
    target_socs = 'target_socs'
    build_type = 'build_type'
    embed_model_data = 'embed_model_data'
    linkshared = 'linkshared'
    models = 'models'
    platform = 'platform'
    model_file_path = 'model_file_path'
    model_sha256_checksum = 'model_sha256_checksum'
    weight_file_path = 'weight_file_path'
    weight_sha256_checksum = 'weight_sha256_checksum'
    subgraphs = 'subgraphs'
    input_tensors = 'input_tensors'
    input_shapes = 'input_shapes'
    input_ranges = 'input_ranges'
    output_tensors = 'output_tensors'
    output_shapes = 'output_shapes'
    runtime = 'runtime'
    data_type = 'data_type'
    limit_opencl_kernel_time = 'limit_opencl_kernel_time'
    nnlib_graph_mode = 'nnlib_graph_mode'
    obfuscate = 'obfuscate'
    winograd = 'winograd'
    validation_inputs_data = 'validation_inputs_data'
    graph_optimize_options = 'graph_optimize_options'  # internal use for now


class ModuleName(object):
    YAML_CONFIG = 'YAML CONFIG'
    MODEL_CONVERTER = 'Model Converter'
    RUN = 'RUN'
    BENCHMARK = 'Benchmark'


CPP_KEYWORDS = [
    'alignas', 'alignof', 'and', 'and_eq', 'asm', 'atomic_cancel',
    'atomic_commit', 'atomic_noexcept', 'auto', 'bitand', 'bitor',
    'bool', 'break', 'case', 'catch', 'char', 'char16_t', 'char32_t',
    'class', 'compl', 'concept', 'const', 'constexpr', 'const_cast',
    'continue', 'co_await', 'co_return', 'co_yield', 'decltype', 'default',
    'delete', 'do', 'double', 'dynamic_cast', 'else', 'enum', 'explicit',
    'export', 'extern', 'false', 'float', 'for', 'friend', 'goto', 'if',
    'import', 'inline', 'int', 'long', 'module', 'mutable', 'namespace',
    'new', 'noexcept', 'not', 'not_eq', 'nullptr', 'operator', 'or', 'or_eq',
    'private', 'protected', 'public', 'register', 'reinterpret_cast',
    'requires', 'return', 'short', 'signed', 'sizeof', 'static',
    'static_assert', 'static_cast', 'struct', 'switch', 'synchronized',
    'template', 'this', 'thread_local', 'throw', 'true', 'try', 'typedef',
    'typeid', 'typename', 'union', 'unsigned', 'using', 'virtual', 'void',
    'volatile', 'wchar_t', 'while', 'xor', 'xor_eq', 'override', 'final',
    'transaction_safe', 'transaction_safe_dynamic', 'if', 'elif', 'else',
    'endif', 'defined', 'ifdef', 'ifndef', 'define', 'undef', 'include',
    'line', 'error', 'pragma',
]


################################
# common functions
################################
def parse_device_type(runtime):
    device_type = ""

    if runtime == RuntimeType.dsp:
        device_type = DeviceType.HEXAGON
    elif runtime == RuntimeType.gpu:
        device_type = DeviceType.GPU
    elif runtime == RuntimeType.cpu:
        device_type = DeviceType.CPU

    return device_type


def get_hexagon_mode(configs):
    runtime_list = []
    for model_name in configs[YAMLKeyword.models]:
        model_runtime =\
            configs[YAMLKeyword.models][model_name].get(
                YAMLKeyword.runtime, "")
        runtime_list.append(model_runtime.lower())

    if RuntimeType.dsp in runtime_list:
        return True
    return False


def md5sum(str):
    md5 = hashlib.md5()
    md5.update(str)
    return md5.hexdigest()


def sha256_checksum(fname):
    hash_func = hashlib.sha256()
    with open(fname, "rb") as f:
        for chunk in iter(lambda: f.read(4096), b""):
            hash_func.update(chunk)
    return hash_func.hexdigest()


def format_model_config(flags):
    with open(flags.config) as f:
        configs = yaml.load(f)

    library_name = configs.get(YAMLKeyword.library_name, "")
    mace_check(len(library_name) > 0,
               ModuleName.YAML_CONFIG, "library name should not be empty")

    if flags.target_abis:
        target_abis = flags.target_abis.split(',')
    else:
        target_abis = configs.get(YAMLKeyword.target_abis, [])
    mace_check((isinstance(target_abis, list) and len(target_abis) > 0),
               ModuleName.YAML_CONFIG, "target_abis list is needed")
    configs[YAMLKeyword.target_abis] = target_abis
    for abi in target_abis:
        mace_check(abi in ABITypeStrs,
                   ModuleName.YAML_CONFIG,
                   "target_abis must be in " + str(ABITypeStrs))

    target_socs = configs.get(YAMLKeyword.target_socs, "")
    if flags.target_socs:
        configs[YAMLKeyword.target_socs] = \
               [soc.lower() for soc in flags.target_socs.split(',')]
    elif not target_socs:
        configs[YAMLKeyword.target_socs] = []
    elif not isinstance(target_socs, list):
        configs[YAMLKeyword.target_socs] = [target_socs]

    configs[YAMLKeyword.target_socs] = \
        [soc.lower() for soc in configs[YAMLKeyword.target_socs]]

    if ABIType.armeabi_v7a in target_abis \
            or ABIType.arm64_v8a in target_abis:
        available_socs = sh_commands.adb_get_all_socs()
        target_socs = configs[YAMLKeyword.target_socs]
        if ALL_SOC_TAG in target_socs:
            mace_check(available_socs,
                       ModuleName.YAML_CONFIG,
                       "Build for all SOCs plugged in computer, "
                       "you at least plug in one phone")
        else:
            for soc in target_socs:
                mace_check(soc in available_socs,
                           ModuleName.YAML_CONFIG,
                           "Build specified SOC library, "
                           "you must plug in a phone using the SOC")

    build_type = BuildType.code
    if flags.build_type:
        build_type_str = flags.build_type
    else:
        build_type_str = configs.get(YAMLKeyword.build_type, "")
    if build_type_str == BuildType.proto:
        build_type = BuildType.proto
    elif build_type_str == BuildType.code:
        build_type = BuildType.code
    else:
        MaceLogger.error(ModuleName.YAML_CONFIG,
                         "Invalid build type " + build_type_str
                         + ". only support [proto|code] format, "
                         + "proto for converting model to ProtoBuf file, "
                         + "code for converting model to c++ code.")
    configs[YAMLKeyword.build_type] = build_type
    embed_model_data = configs.get(YAMLKeyword.embed_model_data, "")
    if embed_model_data == "" or not isinstance(embed_model_data, int) or \
       embed_model_data < 0 or embed_model_data > 1:
        MaceLogger.error(ModuleName.YAML_CONFIG,
                         "embed_model_data must be 0 or 1. "
                         "0 for embed model data to code, 1 not.")
    if build_type == BuildType.proto:
        configs[YAMLKeyword.embed_model_data] = 0

    linkshared = configs.get(YAMLKeyword.linkshared, "")
    if linkshared == "":
        configs[YAMLKeyword.linkshared] = 0
        linkshared = 0
    if not isinstance(linkshared, int) or linkshared < 0 or \
       linkshared > 1:
        MaceLogger.error(ModuleName.YAML_CONFIG,
                         "linkshared must be 0 or 1. "
                         "default is 0, for link mace lib statically, "
                         "1 for dynamic linking.")
    if build_type == BuildType.code and linkshared == 1:
        MaceLogger.error(ModuleName.YAML_CONFIG,
                         "'linkshared == 1' only support when "
                         "'build_type == proto'")

    model_names = configs.get(YAMLKeyword.models, [])
    mace_check(len(model_names) > 0, ModuleName.YAML_CONFIG,
               "no model found in config file")

    model_name_reg = re.compile(r'^[a-zA-Z0-9_]+$')
    for model_name in model_names:
        # check model_name legality
        mace_check(model_name not in CPP_KEYWORDS,
                   ModuleName.YAML_CONFIG,
                   "model name should not be c++ keyword.")
        mace_check((model_name[0] == '_' or model_name[0].isalpha())
                   and bool(model_name_reg.match(model_name)),
                   ModuleName.YAML_CONFIG,
                   "model name should Meet the c++ naming convention"
                   " which start with '_' or alpha"
                   " and only contain alpha, number and '_'")

        model_config = configs[YAMLKeyword.models][model_name]
        platform = model_config.get(YAMLKeyword.platform, "")
        mace_check(platform in PlatformTypeStrs,
                   ModuleName.YAML_CONFIG,
                   "'platform' must be in " + str(PlatformTypeStrs))

        for key in [YAMLKeyword.model_file_path,
                    YAMLKeyword.model_sha256_checksum]:
            value = model_config.get(key, "")
            mace_check(value != "", ModuleName.YAML_CONFIG,
                       "'%s' is necessary" % key)

        weight_file_path = model_config.get(YAMLKeyword.weight_file_path, "")
        if weight_file_path:
            weight_checksum =\
                model_config.get(YAMLKeyword.weight_sha256_checksum, "")
            mace_check(weight_checksum != "", ModuleName.YAML_CONFIG,
                       "'%s' is necessary" %
                       YAMLKeyword.weight_sha256_checksum)
        else:
            model_config[YAMLKeyword.weight_sha256_checksum] = ""

        runtime = model_config.get(YAMLKeyword.runtime, "")
        mace_check(runtime in RuntimeTypeStrs,
                   ModuleName.YAML_CONFIG,
                   "'runtime' must be in " + str(RuntimeTypeStrs))
        if ABIType.host in target_abis:
            mace_check(runtime == RuntimeType.cpu,
                       ModuleName.YAML_CONFIG,
                       "host only support cpu runtime now.")

        data_type = model_config.get(YAMLKeyword.data_type, "")
        if runtime == RuntimeType.cpu_gpu and data_type not in GPUDataTypeStrs:
            model_config[YAMLKeyword.data_type] = \
                GPUDataType.fp16_fp32.value
        elif runtime == RuntimeType.cpu:
            if len(data_type) > 0:
                mace_check(data_type in CPUDataTypeStrs,
                           ModuleName.YAML_CONFIG,
                           "'data_type' must be in " + str(CPUDataTypeStrs)
                           + " for cpu runtime")
            else:
                model_config[YAMLKeyword.data_type] = \
                    CPUDataType.fp32.value
        elif runtime == RuntimeType.gpu:
            if len(data_type) > 0:
                mace_check(data_type in GPUDataTypeStrs,
                           ModuleName.YAML_CONFIG,
                           "'data_type' must be in " + str(GPUDataTypeStrs)
                           + " for gpu runtime")
            else:
                model_config[YAMLKeyword.data_type] =\
                    GPUDataType.fp16_fp32.value
        elif runtime == RuntimeType.dsp:
            if len(data_type) > 0:
                mace_check(data_type in DSPDataTypeStrs,
                           ModuleName.YAML_CONFIG,
                           "'data_type' must be in " + str(DSPDataTypeStrs)
                           + " for dsp runtime")
            else:
                model_config[YAMLKeyword.data_type] = \
                    DSPDataType.uint8.value

        subgraphs = model_config.get(YAMLKeyword.subgraphs, "")
        mace_check(len(subgraphs) > 0, ModuleName.YAML_CONFIG,
                   "at least one subgraph is needed")

        for subgraph in subgraphs:
            for key in [YAMLKeyword.input_tensors,
                        YAMLKeyword.input_shapes,
                        YAMLKeyword.output_tensors,
                        YAMLKeyword.output_shapes]:
                value = subgraph.get(key, "")
                mace_check(value != "", ModuleName.YAML_CONFIG,
                           "'%s' is necessary in subgraph" % key)
                if not isinstance(value, list):
                    subgraph[key] = [value]
            validation_inputs_data = subgraph.get(
                YAMLKeyword.validation_inputs_data, [])
            if not isinstance(validation_inputs_data, list):
                subgraph[YAMLKeyword.validation_inputs_data] = [
                    validation_inputs_data]
            else:
                subgraph[YAMLKeyword.validation_inputs_data] = \
                    validation_inputs_data
            input_ranges = subgraph.get(
                YAMLKeyword.input_ranges, [])
            if not isinstance(input_ranges, list):
                subgraph[YAMLKeyword.input_ranges] = [input_ranges]
            else:
                subgraph[YAMLKeyword.input_ranges] = input_ranges

        for key in [YAMLKeyword.limit_opencl_kernel_time,
                    YAMLKeyword.nnlib_graph_mode,
                    YAMLKeyword.obfuscate,
                    YAMLKeyword.winograd]:
            value = model_config.get(key, "")
            if value == "":
                model_config[key] = 0

        mace_check(model_config[YAMLKeyword.winograd] in WinogradParameters,
                   ModuleName.YAML_CONFIG,
                   "'winograd' parameters must be in "
                   + str(WinogradParameters) +
                   ". 0 for disable winograd convolution")

        weight_file_path = model_config.get(YAMLKeyword.weight_file_path, "")
        model_config[YAMLKeyword.weight_file_path] = weight_file_path

    return configs


def get_build_binary_dir(library_name, target_abi, target_soc,
                         serial_num):
    if not target_soc or not serial_num:
        binary_path_digest = md5sum(target_abi)
        binary_path_digest = "%s_%s" % (target_abi, binary_path_digest)
    else:
        device_name = sh_commands.adb_get_device_name_by_serialno(serial_num)
        binary_path_digest = md5sum(target_abi + target_soc + serial_num)
        binary_path_digest = "%s_%s_%s" % \
                             (device_name, target_soc, binary_path_digest)
    return "%s/%s/%s/%s" % (
        BUILD_OUTPUT_DIR, library_name, BUILD_TMP_DIR_NAME, binary_path_digest)


def get_build_model_dirs(library_name, model_name, target_abi, target_soc,
                         serial_num, model_file_path):
    model_path_digest = md5sum(model_file_path)
    model_output_base_dir = "%s/%s/%s/%s/%s" % (
        BUILD_OUTPUT_DIR, library_name, BUILD_TMP_DIR_NAME,
        model_name, model_path_digest)

    if target_abi == ABIType.host:
        model_output_dir = "%s/%s" % (model_output_base_dir, target_abi)
    elif not target_soc or not serial_num:
        model_output_dir = "%s/%s/%s" % (
            model_output_base_dir, BUILD_TMP_GENERAL_OUTPUT_DIR_NAME,
            target_abi)
    else:
        device_name = \
            sh_commands.adb_get_device_name_by_serialno(serial_num)
        model_output_dir = "%s/%s_%s/%s" % (
            model_output_base_dir, device_name,
            target_soc, target_abi)

    mace_model_dir = \
        '%s/%s/%s' % (BUILD_OUTPUT_DIR, library_name, MODEL_OUTPUT_DIR_NAME)

    return model_output_base_dir, model_output_dir, mace_model_dir


def get_opencl_binary_output_path(library_name, target_abi,
                                  target_soc, serial_num):
    device_name = \
        sh_commands.adb_get_device_name_by_serialno(serial_num)
    return '%s/%s/%s/%s/%s_%s.%s.%s.bin' % \
           (BUILD_OUTPUT_DIR,
            library_name,
            OUTPUT_OPENCL_BINARY_DIR_NAME,
            target_abi,
            library_name,
            OUTPUT_OPENCL_BINARY_FILE_NAME,
            device_name,
            target_soc)


def get_shared_library_dir(library_name, abi):
    return '%s/%s/%s/%s' % (BUILD_OUTPUT_DIR,
                            library_name,
                            OUTPUT_LIBRARY_DIR_NAME,
                            abi)


################################
# build
################################
def pull_opencl_binary_and_tuning_param(target_abi,
                                        serialno,
                                        model_output_dirs):
    sh_commands.pull_binaries(target_abi, serialno, model_output_dirs,
                              CL_COMPILED_BINARY_FILE_NAME)


def print_configuration(flags, configs):
    title = "Common Configuration"
    header = ["key", "value"]
    data = list()
    data.append([YAMLKeyword.library_name,
                 configs[YAMLKeyword.library_name]])
    data.append([YAMLKeyword.target_abis,
                 configs[YAMLKeyword.target_abis]])
    data.append([YAMLKeyword.target_socs,
                 configs[YAMLKeyword.target_socs]])
    data.append([YAMLKeyword.build_type,
                 configs[YAMLKeyword.build_type]])
    data.append([YAMLKeyword.embed_model_data,
                 configs[YAMLKeyword.embed_model_data]])
    data.append([YAMLKeyword.linkshared,
                 configs[YAMLKeyword.linkshared]])
    data.append(["Tuning", flags.disable_tuning])
    MaceLogger.summary(StringFormatter.table(header, data, title))


def download_model_files(model_file_path,
                         model_output_dir,
                         weight_file_path=""):
    MaceLogger.info("Downloading model, please wait ...")
    if model_file_path.startswith("http://") or \
            model_file_path.startswith("https://"):
        model_file = model_output_dir + "/model.pb"
        urllib.urlretrieve(model_file_path, model_file)

    if weight_file_path.startswith("http://") or \
            weight_file_path.startswith("https://"):
        weight_file = model_output_dir + "/model.caffemodel"
        urllib.urlretrieve(weight_file_path, weight_file)
    MaceLogger.info("Model downloaded successfully.")


def get_model_files_path(model_file_path,
                         model_output_dir,
                         weight_file_path=""):
    if model_file_path.startswith("http://") or \
            model_file_path.startswith("https://"):
        model_file = model_output_dir + "/model.pb"
    else:
        model_file = model_file_path

    if weight_file_path.startswith("http://") or \
            weight_file_path.startswith("https://"):
        weight_file = model_output_dir + "/model.caffemodel"
    else:
        weight_file = weight_file_path

    return model_file, weight_file


def convert_model(configs):
    # Remove previous output dirs
    library_name = configs[YAMLKeyword.library_name]
    if not os.path.exists(BUILD_OUTPUT_DIR):
        os.makedirs(BUILD_OUTPUT_DIR)
    elif os.path.exists(os.path.join(BUILD_OUTPUT_DIR, library_name)):
        sh.rm("-rf", os.path.join(BUILD_OUTPUT_DIR, library_name))
    os.makedirs(os.path.join(BUILD_OUTPUT_DIR, library_name))

    model_output_dir = \
        '%s/%s/%s' % (BUILD_OUTPUT_DIR, library_name, MODEL_OUTPUT_DIR_NAME)
    model_header_dir = \
        '%s/%s/%s' % (BUILD_OUTPUT_DIR, library_name, MODEL_HEADER_DIR_PATH)
    if os.path.exists(model_output_dir):
        sh.rm("-rf", model_output_dir)
    os.makedirs(model_output_dir)
    if os.path.exists(model_header_dir):
        sh.rm("-rf", model_header_dir)
    os.makedirs(model_header_dir)
    # copy header files
    sh.cp("-f", glob.glob("mace/public/*.h"), model_header_dir)

    sh_commands.gen_mace_engine_factory_source(
        configs[YAMLKeyword.models].keys(),
        configs[YAMLKeyword.build_type])
    if configs[YAMLKeyword.build_type] == BuildType.code:
        sh.cp("-f", glob.glob("mace/codegen/engine/*.h"),
              model_header_dir)

    embed_model_data = configs[YAMLKeyword.embed_model_data]

    sh_commands.clear_model_codegen()
    for model_name in configs[YAMLKeyword.models]:
        MaceLogger.header(
            StringFormatter.block("Convert %s model" % model_name))
        model_config = configs[YAMLKeyword.models][model_name]
        runtime = model_config[YAMLKeyword.runtime]

        # Create model build directory
        model_path_digest = md5sum(
            model_config[YAMLKeyword.model_file_path])

        model_output_base_dir = "%s/%s/%s/%s/%s" % (
            BUILD_OUTPUT_DIR, library_name, BUILD_TMP_DIR_NAME,
            model_name, model_path_digest)

        if os.path.exists(model_output_base_dir):
            sh.rm("-rf", model_output_base_dir)
        os.makedirs(model_output_base_dir)

        download_model_files(
            model_config[YAMLKeyword.model_file_path],
            model_output_base_dir,
            model_config[YAMLKeyword.weight_file_path])

        model_file_path, weight_file_path = get_model_files_path(
            model_config[YAMLKeyword.model_file_path],
            model_output_base_dir,
            model_config[YAMLKeyword.weight_file_path])

        if sha256_checksum(model_file_path) != \
                model_config[YAMLKeyword.model_sha256_checksum]:
            MaceLogger.error(ModuleName.MODEL_CONVERTER,
                             "model file sha256checksum not match")

        if weight_file_path:
            if sha256_checksum(weight_file_path) != \
                    model_config[YAMLKeyword.weight_sha256_checksum]:
                MaceLogger.error(ModuleName.MODEL_CONVERTER,
                                 "weight file sha256checksum not match")

        data_type = model_config[YAMLKeyword.data_type]
        # TODO(liuqi): support multiple subgraphs
        subgraphs = model_config[YAMLKeyword.subgraphs]

        model_codegen_dir = "%s/%s" % (MODEL_CODEGEN_DIR, model_name)
        sh_commands.gen_model_code(
            model_codegen_dir,
            model_config[YAMLKeyword.platform],
            model_file_path,
            weight_file_path,
            model_config[YAMLKeyword.model_sha256_checksum],
            model_config[YAMLKeyword.weight_sha256_checksum],
            ",".join(subgraphs[0][YAMLKeyword.input_tensors]),
            ",".join(subgraphs[0][YAMLKeyword.output_tensors]),
            runtime,
            model_name,
            ":".join(subgraphs[0][YAMLKeyword.input_shapes]),
            model_config[YAMLKeyword.nnlib_graph_mode],
            embed_model_data,
            model_config[YAMLKeyword.winograd],
            model_config[YAMLKeyword.obfuscate],
            configs[YAMLKeyword.build_type],
            data_type,
            ",".join(model_config.get(YAMLKeyword.graph_optimize_options, [])))

        if configs[YAMLKeyword.build_type] == BuildType.proto:
            sh.mv("-f",
                  '%s/%s.pb' % (model_codegen_dir, model_name),
                  model_output_dir)
            sh.mv("-f",
                  '%s/%s.data' % (model_codegen_dir, model_name),
                  model_output_dir)
        else:
            if not embed_model_data:
                sh.mv("-f",
                      '%s/%s.data' % (model_codegen_dir, model_name),
                      model_output_dir)
            sh.cp("-f", glob.glob("mace/codegen/models/*/*.h"),
                  model_header_dir)

        MaceLogger.summary(
            StringFormatter.block("Model %s converted" % model_name))


def build_specific_lib(target_abi, target_soc, serial_num,
                       configs, tuning, enable_openmp,
                       address_sanitizer):
    library_name = configs[YAMLKeyword.library_name]
    build_type = configs[YAMLKeyword.build_type]
    embed_model_data = configs[YAMLKeyword.embed_model_data]
    linkshared = configs[YAMLKeyword.linkshared]
    hexagon_mode = get_hexagon_mode(configs)
    model_output_dirs = []

    build_tmp_binary_dir = get_build_binary_dir(library_name, target_abi,
                                                target_soc, serial_num)
    if os.path.exists(build_tmp_binary_dir):
        sh.rm("-rf", build_tmp_binary_dir)
    os.makedirs(build_tmp_binary_dir)

    sh_commands.gen_tuning_param_code(model_output_dirs)
    if linkshared == 0:
        mace_run_target = MACE_RUN_STATIC_TARGET
    else:
        mace_run_target = MACE_RUN_SHARED_TARGET
        sh_commands.bazel_build(
            LIBMACE_SO_TARGET,
            abi=target_abi,
            hexagon_mode=hexagon_mode,
            enable_openmp=enable_openmp,
            address_sanitizer=address_sanitizer
        )
        sh_commands.update_libmace_shared_library(serial_num,
                                                  target_abi,
                                                  library_name,
                                                  BUILD_OUTPUT_DIR,
                                                  OUTPUT_LIBRARY_DIR_NAME)

    sh_commands.bazel_build(
        mace_run_target,
        abi=target_abi,
        hexagon_mode=hexagon_mode,
        enable_openmp=enable_openmp,
        address_sanitizer=address_sanitizer
    )
    sh_commands.update_mace_run_lib(build_tmp_binary_dir, linkshared)
    binary_changed = False

    for model_name in configs[YAMLKeyword.models]:
        model_config = configs[YAMLKeyword.models][model_name]
        model_runtime = model_config[YAMLKeyword.runtime]
        # Create model build directory
        model_output_base_dir, model_output_dir, mace_model_dir = \
            get_build_model_dirs(library_name, model_name, target_abi,
                                 target_soc, serial_num,
                                 model_config[YAMLKeyword.model_file_path])

        model_output_dirs.append(model_output_dir)

        if os.path.exists(model_output_dir):
            sh.rm("-rf", model_output_dir)
        os.makedirs(model_output_dir)

        # build for specified soc
        if not address_sanitizer and target_abi != ABIType.host \
                and target_soc is not None and \
                model_runtime in [RuntimeType.gpu, RuntimeType.cpu_gpu]:
            sh_commands.clear_phone_data_dir(serial_num, PHONE_DATA_DIR)

            subgraphs = model_config[YAMLKeyword.subgraphs]
            # generate input data
            sh_commands.gen_random_input(
                model_output_dir,
                subgraphs[0][YAMLKeyword.input_tensors],
                subgraphs[0][YAMLKeyword.input_shapes],
                subgraphs[0][YAMLKeyword.validation_inputs_data],
                input_ranges=subgraphs[0][YAMLKeyword.input_ranges])

            device_type = parse_device_type(RuntimeType.gpu)
            sh_commands.tuning_run(
                abi=target_abi,
                serialno=serial_num,
                mace_run_dir=build_tmp_binary_dir,
                vlog_level=0,
                embed_model_data=embed_model_data,
                model_output_dir=model_output_dir,
                input_nodes=subgraphs[0][YAMLKeyword.input_tensors],
                output_nodes=subgraphs[0][YAMLKeyword.output_tensors],
                input_shapes=subgraphs[0][YAMLKeyword.input_shapes],
                output_shapes=subgraphs[0][YAMLKeyword.output_shapes],
                mace_model_dir=mace_model_dir,
                model_tag=model_name,
                device_type=device_type,
                running_round=0,
                restart_round=1,
                limit_opencl_kernel_time=model_config[YAMLKeyword.limit_opencl_kernel_time],  # noqa
                tuning=tuning,
                out_of_range_check=False,
                phone_data_dir=PHONE_DATA_DIR,
                build_type=build_type,
                opencl_binary_file="",
                shared_library_dir=get_shared_library_dir(library_name, target_abi),  # noqa
                linkshared=linkshared,
            )

            pull_opencl_binary_and_tuning_param(target_abi, serial_num,
                                                [model_output_dir])
            sh_commands.touch_tuned_file_flag(build_tmp_binary_dir)
            binary_changed = True

    if binary_changed:
        opencl_output_bin_path = get_opencl_binary_output_path(
            library_name, target_abi, target_soc, serial_num
        )
        sh_commands.merge_opencl_binaries(
            model_output_dirs, CL_COMPILED_BINARY_FILE_NAME,
            opencl_output_bin_path)
        sh_commands.gen_tuning_param_code(model_output_dirs)
        sh_commands.bazel_build(
            mace_run_target,
            abi=target_abi,
            hexagon_mode=hexagon_mode,
            enable_openmp=enable_openmp,
            address_sanitizer=address_sanitizer
        )
        sh_commands.update_mace_run_lib(build_tmp_binary_dir, linkshared)

    if target_abi == ABIType.host:
        sh_commands.build_host_libraries(build_type, target_abi)

    # build benchmark_model binary
    sh_commands.build_benchmark_model(target_abi,
                                      build_tmp_binary_dir,
                                      hexagon_mode,
                                      enable_openmp,
                                      linkshared)

    # generate library
    if linkshared == 0:
        sh_commands.merge_libs(target_soc,
                               serial_num,
                               target_abi,
                               library_name,
                               BUILD_OUTPUT_DIR,
                               OUTPUT_LIBRARY_DIR_NAME,
                               build_type,
                               hexagon_mode)


def generate_library(configs, tuning, enable_openmp, address_sanitizer):
    MaceLogger.header(StringFormatter.block("Building library"))
    # generate source
    MaceLogger.info('* generate common source files...')
    sh_commands.gen_mace_version()
    sh_commands.gen_encrypted_opencl_source()
    MaceLogger.info('generate common source files done')

    # create build dirs
    library_name = configs[YAMLKeyword.library_name]
    if not os.path.exists(BUILD_OUTPUT_DIR):
        os.makedirs(BUILD_OUTPUT_DIR)
    tmp_build_dir = os.path.join(BUILD_OUTPUT_DIR, library_name,
                                 BUILD_TMP_DIR_NAME)
    if not os.path.exists(tmp_build_dir):
        os.makedirs(tmp_build_dir)
    library_out_dir = os.path.join(BUILD_OUTPUT_DIR, library_name,
                                   OUTPUT_LIBRARY_DIR_NAME)
    if os.path.exists(library_out_dir):
        sh.rm('-rf', library_out_dir)

    target_socs = configs[YAMLKeyword.target_socs]
    for target_abi in configs[YAMLKeyword.target_abis]:
        if not target_socs or target_abi == ABIType.host:
            build_specific_lib(target_abi, None, None, configs,
                               tuning, enable_openmp, address_sanitizer)
        else:
            if ALL_SOC_TAG in target_socs:
                target_socs = sh_commands.adb_get_all_socs()
            for target_soc in target_socs:
                serial_nums = \
                    sh_commands.get_target_socs_serialnos([target_soc])
                for serial_num in serial_nums:
                    with sh_commands.device_lock(serial_num):
                        build_specific_lib(target_abi, target_soc, serial_num,
                                           configs, tuning, enable_openmp,
                                           address_sanitizer)

    # package library
    sh_commands.packaging_lib(BUILD_OUTPUT_DIR,
                              configs[YAMLKeyword.library_name])


def print_library_summary(configs):
    library_name = configs[YAMLKeyword.library_name]
    title = "Library"
    header = ["key", "value"]
    data = list()
    data.append(["library package",
                 "%s/%s/libmace_%s.tar.gz"
                 % (BUILD_OUTPUT_DIR, library_name, library_name)])
    MaceLogger.summary(StringFormatter.table(header, data, title))


def build_library(flags):
    configs = format_model_config(flags)

    print_configuration(flags, configs)

    convert_model(configs)

    generate_library(configs, flags.disable_tuning,
                     flags.disable_openmp, flags.address_sanitizer)

    print_library_summary(configs)


################################
# run
################################
def report_run_statistics(stdout,
                          abi,
                          serialno,
                          model_name,
                          device_type,
                          output_dir,
                          tuned):
    metrics = [0] * 3
    for line in stdout.split('\n'):
        line = line.strip()
        parts = line.split()
        if len(parts) == 4 and parts[0].startswith("time"):
            metrics[0] = str(float(parts[1]))
            metrics[1] = str(float(parts[2]))
            metrics[2] = str(float(parts[3]))
            break

    device_name = ""
    target_soc = ""
    if abi != "host":
        props = sh_commands.adb_getprop_by_serialno(serialno)
        device_name = props.get("ro.product.model", "")
        target_soc = props.get("ro.board.platform", "")

    report_filename = output_dir + "/report.csv"
    if not os.path.exists(report_filename):
        with open(report_filename, 'w') as f:
            f.write("model_name,device_name,soc,abi,runtime,"
                    "init,warmup,run_avg,tuned\n")

    data_str = "{model_name},{device_name},{soc},{abi},{device_type}," \
               "{init},{warmup},{run_avg},{tuned}\n" \
        .format(model_name=model_name,
                device_name=device_name,
                soc=target_soc,
                abi=abi,
                device_type=device_type,
                init=metrics[0],
                warmup=metrics[1],
                run_avg=metrics[2],
                tuned=tuned,
                )
    with open(report_filename, 'a') as f:
        f.write(data_str)


def run_specific_target(flags, configs, target_abi,
                        target_soc, serial_num):
    library_name = configs[YAMLKeyword.library_name]
    build_type = configs[YAMLKeyword.build_type]
    embed_model_data = configs[YAMLKeyword.embed_model_data]
    opencl_output_bin_path = ""
    linkshared = configs[YAMLKeyword.linkshared]
    if not configs[YAMLKeyword.target_socs]:
        build_tmp_binary_dir = get_build_binary_dir(library_name, target_abi,
                                                    None, None)
    else:
        build_tmp_binary_dir = get_build_binary_dir(library_name, target_abi,
                                                    target_soc, serial_num)
        opencl_output_bin_path = get_opencl_binary_output_path(
            library_name, target_abi, target_soc, serial_num
        )
    mace_check(os.path.exists(build_tmp_binary_dir),
               ModuleName.RUN,
               'You should build before run.')

    for model_name in configs[YAMLKeyword.models]:
        if target_abi == ABIType.host:
            device_name = ABIType.host
        else:
            device_name =\
                sh_commands.adb_get_device_name_by_serialno(serial_num)
        MaceLogger.header(
            StringFormatter.block(
                "Run model %s on %s" % (model_name, device_name)))
        model_config = configs[YAMLKeyword.models][model_name]
        model_runtime = model_config[YAMLKeyword.runtime]
        subgraphs = model_config[YAMLKeyword.subgraphs]

        if not configs[YAMLKeyword.target_socs]:
            model_output_base_dir, model_output_dir, mace_model_dir = \
                get_build_model_dirs(library_name, model_name, target_abi,
                                     None, None,
                                     model_config[YAMLKeyword.model_file_path])
        else:
            model_output_base_dir, model_output_dir, mace_model_dir = \
                get_build_model_dirs(library_name, model_name, target_abi,
                                     target_soc, serial_num,
                                     model_config[YAMLKeyword.model_file_path])
        mace_check(os.path.exists(model_output_dir)
                   and os.path.exists(mace_model_dir),
                   ModuleName.RUN,
                   'You should build before run.')
        if target_abi != ABIType.host:
            sh_commands.clear_phone_data_dir(serial_num, PHONE_DATA_DIR)

        # generate input data
        sh_commands.gen_random_input(
            model_output_dir,
            subgraphs[0][YAMLKeyword.input_tensors],
            subgraphs[0][YAMLKeyword.input_shapes],
            subgraphs[0][YAMLKeyword.validation_inputs_data],
            input_ranges=subgraphs[0][YAMLKeyword.input_ranges])
        runtime_list = []
        if target_abi == ABIType.host:
            runtime_list.extend([RuntimeType.cpu])
        elif model_runtime == RuntimeType.cpu_gpu:
            runtime_list.extend([RuntimeType.cpu, RuntimeType.gpu])
        else:
            runtime_list.extend([model_runtime])
        for runtime in runtime_list:
            device_type = parse_device_type(runtime)

            run_output = sh_commands.tuning_run(
                abi=target_abi,
                serialno=serial_num,
                mace_run_dir=build_tmp_binary_dir,
                vlog_level=flags.vlog_level,
                embed_model_data=embed_model_data,
                model_output_dir=model_output_dir,
                input_nodes=subgraphs[0][YAMLKeyword.input_tensors],
                output_nodes=subgraphs[0][YAMLKeyword.output_tensors],
                input_shapes=subgraphs[0][YAMLKeyword.input_shapes],
                output_shapes=subgraphs[0][YAMLKeyword.output_shapes],
                mace_model_dir=mace_model_dir,
                model_tag=model_name,
                device_type=device_type,
                running_round=flags.round,
                restart_round=flags.restart_round,
                limit_opencl_kernel_time=model_config[YAMLKeyword.limit_opencl_kernel_time],  # noqa
                tuning=False,
                out_of_range_check=flags.gpu_out_of_range_check,
                phone_data_dir=PHONE_DATA_DIR,
                build_type=build_type,
                omp_num_threads=flags.omp_num_threads,
                cpu_affinity_policy=flags.cpu_affinity_policy,
                gpu_perf_hint=flags.gpu_perf_hint,
                gpu_priority_hint=flags.gpu_priority_hint,
                runtime_failure_ratio=flags.runtime_failure_ratio,
                address_sanitizer=flags.address_sanitizer,
                opencl_binary_file=opencl_output_bin_path,
                shared_library_dir=get_shared_library_dir(library_name, target_abi),  # noqa
                linkshared=linkshared,
            )
            if flags.validate:
                model_file_path, weight_file_path = get_model_files_path(
                    model_config[YAMLKeyword.model_file_path],
                    model_output_base_dir,
                    model_config[YAMLKeyword.weight_file_path])

                sh_commands.validate_model(
                    abi=target_abi,
                    serialno=serial_num,
                    model_file_path=model_file_path,
                    weight_file_path=weight_file_path,
                    platform=model_config[YAMLKeyword.platform],
                    device_type=device_type,
                    input_nodes=subgraphs[0][YAMLKeyword.input_tensors],
                    output_nodes=subgraphs[0][YAMLKeyword.output_tensors],
                    input_shapes=subgraphs[0][YAMLKeyword.input_shapes],
                    output_shapes=subgraphs[0][YAMLKeyword.output_shapes],
                    model_output_dir=model_output_dir,
                    phone_data_dir=PHONE_DATA_DIR,
                    caffe_env=flags.caffe_env)
            if flags.report and flags.round > 0:
                report_run_statistics(
                    run_output, target_abi, serial_num,
                    model_name, device_type, flags.report_dir,
                    sh_commands.is_binary_tuned(build_tmp_binary_dir))


def run_mace(flags):
    configs = format_model_config(flags)

    target_socs = configs[YAMLKeyword.target_socs]
    if not target_socs or ALL_SOC_TAG in target_socs:
        target_socs = sh_commands.adb_get_all_socs()

    for target_abi in configs[YAMLKeyword.target_abis]:
        if target_abi == ABIType.host:
            run_specific_target(flags, configs, target_abi, None, None)
        else:
            for target_soc in target_socs:
                serial_nums = \
                    sh_commands.get_target_socs_serialnos([target_soc])
                mace_check(serial_nums,
                           ModuleName.RUN,
                           'There is no device with soc: ' + target_soc)
                for serial_num in serial_nums:
                    with sh_commands.device_lock(serial_num):
                        run_specific_target(flags, configs, target_abi,
                                            target_soc, serial_num)


################################
#  benchmark model
################################
def bm_specific_target(flags, configs, target_abi, target_soc, serial_num):
    library_name = configs[YAMLKeyword.library_name]
    build_type = configs[YAMLKeyword.build_type]
    embed_model_data = configs[YAMLKeyword.embed_model_data]
    opencl_output_bin_path = ""
    linkshared = configs[YAMLKeyword.linkshared]
    if not configs[YAMLKeyword.target_socs]:
        build_tmp_binary_dir = get_build_binary_dir(library_name, target_abi,
                                                    None, None)
    else:
        build_tmp_binary_dir = get_build_binary_dir(library_name, target_abi,
                                                    target_soc, serial_num)
        opencl_output_bin_path = get_opencl_binary_output_path(
            library_name, target_abi, target_soc, serial_num
        )
    mace_check(os.path.exists(build_tmp_binary_dir),
               ModuleName.BENCHMARK,
               'You should build before benchmark.')

    for model_name in configs[YAMLKeyword.models]:
        if target_abi == ABIType.host:
            device_name = ABIType.host
        else:
            device_name = \
                sh_commands.adb_get_device_name_by_serialno(serial_num)
        MaceLogger.header(
            StringFormatter.block(
                "Benchmark model %s on %s" % (model_name, device_name)))
        model_config = configs[YAMLKeyword.models][model_name]
        model_runtime = model_config[YAMLKeyword.runtime]
        subgraphs = model_config[YAMLKeyword.subgraphs]

        if not configs[YAMLKeyword.target_socs]:
            model_output_base_dir, model_output_dir, mace_model_dir = \
                get_build_model_dirs(library_name, model_name, target_abi,
                                     None, None,
                                     model_config[YAMLKeyword.model_file_path])
        else:
            model_output_base_dir, model_output_dir, mace_model_dir = \
                get_build_model_dirs(library_name, model_name, target_abi,
                                     target_soc, serial_num,
                                     model_config[YAMLKeyword.model_file_path])
        mace_check(os.path.exists(model_output_dir)
                   and os.path.exists(mace_model_dir),
                   ModuleName.BENCHMARK,
                   'You should build before benchmark.')
        if target_abi != ABIType.host:
            sh_commands.clear_phone_data_dir(serial_num, PHONE_DATA_DIR)

        sh_commands.gen_random_input(
            model_output_dir,
            subgraphs[0][YAMLKeyword.input_tensors],
            subgraphs[0][YAMLKeyword.input_shapes],
            subgraphs[0][YAMLKeyword.validation_inputs_data],
            input_ranges=subgraphs[0][YAMLKeyword.input_ranges])
        runtime_list = []
        if target_abi == ABIType.host:
            runtime_list.extend([RuntimeType.cpu])
        elif model_runtime == RuntimeType.cpu_gpu:
            runtime_list.extend([RuntimeType.cpu, RuntimeType.gpu])
        else:
            runtime_list.extend([model_runtime])
        for runtime in runtime_list:
            device_type = parse_device_type(runtime)
            sh_commands.benchmark_model(
                abi=target_abi,
                serialno=serial_num,
                benchmark_binary_dir=build_tmp_binary_dir,
                vlog_level=0,
                embed_model_data=embed_model_data,
                model_output_dir=model_output_dir,
                input_nodes=subgraphs[0][YAMLKeyword.input_tensors],
                output_nodes=subgraphs[0][YAMLKeyword.output_tensors],
                input_shapes=subgraphs[0][YAMLKeyword.input_shapes],
                output_shapes=subgraphs[0][YAMLKeyword.output_shapes],
                mace_model_dir=mace_model_dir,
                model_tag=model_name,
                device_type=device_type,
                phone_data_dir=PHONE_DATA_DIR,
                build_type=build_type,
                omp_num_threads=flags.omp_num_threads,
                cpu_affinity_policy=flags.cpu_affinity_policy,
                gpu_perf_hint=flags.gpu_perf_hint,
                gpu_priority_hint=flags.gpu_priority_hint,
                opencl_binary_file=opencl_output_bin_path,
                shared_library_dir=get_shared_library_dir(library_name, target_abi),  # noqa
                linkshared=linkshared)


def benchmark_model(flags):
    configs = format_model_config(flags)

    target_socs = configs[YAMLKeyword.target_socs]
    if not target_socs or ALL_SOC_TAG in target_socs:
        target_socs = sh_commands.adb_get_all_socs()

    for target_abi in configs[YAMLKeyword.target_abis]:
        if target_abi == ABIType.host:
            bm_specific_target(flags, configs, target_abi, None, None)
        else:
            for target_soc in target_socs:
                serial_nums = \
                    sh_commands.get_target_socs_serialnos([target_soc])
                mace_check(serial_nums,
                           ModuleName.BENCHMARK,
                           'There is no device with soc: ' + target_soc)
                for serial_num in serial_nums:
                    with sh_commands.device_lock(serial_num):
                        bm_specific_target(flags, configs, target_abi,
                                           target_soc, serial_num)


################################
# Parsing arguments
################################
def str2bool(v):
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')


def str_to_caffe_env_type(v):
    if v.lower() == 'docker':
        return CaffeEnvType.DOCKER
    elif v.lower() == 'local':
        return CaffeEnvType.LOCAL
    else:
        raise argparse.ArgumentTypeError('[docker | local] expected.')


def parse_args():
    """Parses command line arguments."""
    all_type_parent_parser = argparse.ArgumentParser(add_help=False)
    all_type_parent_parser.add_argument(
        '--config',
        type=str,
        default="",
        required=True,
        help="the path of model yaml configuration file.")
    all_type_parent_parser.add_argument(
        "--build_type",
        type=str,
        default="",
        help="Model build type, can be ['proto', 'code'].")
    all_type_parent_parser.add_argument(
        "--target_abis",
        type=str,
        default="",
        help="Target ABIs, comma seperated list.")
    all_type_parent_parser.add_argument(
        "--target_socs",
        type=str,
        default="",
        help="Target SOCs, comma seperated list.")
    build_run_parent_parser = argparse.ArgumentParser(add_help=False)
    build_run_parent_parser.add_argument(
        '--address_sanitizer',
        action="store_true",
        help="Whether to use address sanitizer to check memory error")
    run_bm_parent_parser = argparse.ArgumentParser(add_help=False)
    run_bm_parent_parser.add_argument(
        "--omp_num_threads",
        type=int,
        default=DefaultValues.omp_num_threads,
        help="num of openmp threads")
    run_bm_parent_parser.add_argument(
        "--cpu_affinity_policy",
        type=int,
        default=DefaultValues.cpu_affinity_policy,
        help="0:AFFINITY_NONE/1:AFFINITY_BIG_ONLY/2:AFFINITY_LITTLE_ONLY")
    run_bm_parent_parser.add_argument(
        "--gpu_perf_hint",
        type=int,
        default=DefaultValues.gpu_perf_hint,
        help="0:DEFAULT/1:LOW/2:NORMAL/3:HIGH")
    run_bm_parent_parser.add_argument(
        "--gpu_priority_hint",
        type=int,
        default=DefaultValues.gpu_priority_hint,
        help="0:DEFAULT/1:LOW/2:NORMAL/3:HIGH")

    parser = argparse.ArgumentParser()
    subparsers = parser.add_subparsers()
    build = subparsers.add_parser(
        'build',
        parents=[all_type_parent_parser, build_run_parent_parser],
        help='build model library and test tools')
    build.set_defaults(func=build_library)
    build.add_argument(
        '--disable_tuning',
        action="store_false",
        help="Disable tuning the parameters for the GPU of specified SoC.")
    build.add_argument(
        "--disable_openmp",
        action="store_false",
        help="Disable openmp for multiple thread.")
    run = subparsers.add_parser(
        'run',
        parents=[all_type_parent_parser, run_bm_parent_parser,
                 build_run_parent_parser],
        help='run model in command line')
    run.set_defaults(func=run_mace)
    run.add_argument(
        "--round",
        type=int,
        default=1,
        help="The model running round.")
    run.add_argument(
        "--validate",
        action="store_true",
        help="whether to verify the results are consistent with "
             "the frameworks.")
    run.add_argument(
        "--caffe_env",
        type=str_to_caffe_env_type,
        default='docker',
        help="[docker | local] you can specific caffe environment for"
             " validation. local environment or caffe docker image.")
    run.add_argument(
        "--vlog_level",
        type=int,
        default=0,
        help="[1~5]. Verbose log level for debug.")
    run.add_argument(
        "--gpu_out_of_range_check",
        action="store_true",
        help="Enable out of memory check for gpu.")
    run.add_argument(
        "--restart_round",
        type=int,
        default=1,
        help="restart round between run.")
    run.add_argument(
        "--report",
        action="store_true",
        help="print run statistics report.")
    run.add_argument(
        "--report_dir",
        type=str,
        default="",
        help="print run statistics report.")
    run.add_argument(
        "--runtime_failure_ratio",
        type=float,
        default=0.0,
        help="[mock runtime failure ratio].")
    benchmark = subparsers.add_parser(
        'benchmark',
        parents=[all_type_parent_parser, run_bm_parent_parser,
                 build_run_parent_parser],
        help='benchmark model for detail information')
    benchmark.set_defaults(func=benchmark_model)
    return parser.parse_known_args()


if __name__ == "__main__":
    flags, unparsed = parse_args()
    flags.func(flags)