path: root/lib/data_parameters.py

"""
Utilities for parameter extraction from data layout.

Parameters include the amount of keys, length of strings (both keys and values),
length of lists, ane more.
"""

from protocol_benchmarks import codegen_for_lib
import cycles_to_energy, size_to_radio_energy, utils
import numpy as np
import ubjson

def _string_value_length(json):
    if type(json) == str:
        return len(json)

    if type(json) == dict:
        return sum(map(_string_value_length, json.values()))

    if type(json) == list:
        return sum(map(_string_value_length, json))

    return 0

# TODO distinguish between int and uint, which is not visible from the
# data value alone
def _int_value_length(json):
    if type(json) == int:
        if json < 256:
            return 1
        if json < 65536:
            return 2
        return 4

    if type(json) == dict:
        return sum(map(_int_value_length, json.values()))

    if type(json) == list:
        return sum(map(_int_value_length, json))

    return 0

def _string_key_length(json):
    if type(json) == dict:
        return sum(map(len, json.keys())) + sum(map(_string_key_length, json.values()))

    return 0

def _num_keys(json):
    if type(json) == dict:
        return len(json.keys()) + sum(map(_num_keys, json.values()))

    return 0

def _num_of_type(json, wanted_type):
    ret = 0
    if type(json) == wanted_type:
        ret = 1

    if type(json) == dict:
        ret += sum(map(lambda x: _num_of_type(x, wanted_type), json.values()))

    if type(json) == list:
        ret += sum(map(lambda x: _num_of_type(x, wanted_type), json))

    return ret

def json_to_param(json):
    """Return numeric parameters describing the structure of JSON data."""
    ret = dict()

    ret['strlen_keys'] = _string_key_length(json)
    ret['strlen_values'] = _string_value_length(json)
    ret['bytelen_int'] = _int_value_length(json)
    ret['num_int'] = _num_of_type(json, int)
    ret['num_float'] = _num_of_type(json, float)
    ret['num_str'] = _num_of_type(json, str)

    return ret


class Protolog:
    """
    Loader and postprocessor for raw protobench (protocol-modeling/benchmark.py) data.

    Converts data sorted by (arch,lib)/benchmark/index/attribute
    to data sorted by (benchmark,index)/arch/lib/attribute.

    Once constructed, a class object provides three members:

    libraries -- array of library:config elements found in the benchmark results
    architectures -- array of multipass architecture names
    aggregate -- enriched log data, ordered by benchmark: {
        ('benchmark name', 'sub-benchmark index') : {
            'architecture' : {
                'library:options' : {
                    'attribute' : value (usually int or array)
                }
            }
        }
    }

    aggregate attributes:
    bss_{nop,ser,serdes} : whole-program Block Storage Segment (BSS) size
    callcycles_raw : { 'C++ statement' : [CPU cycles for execution] ... }.
        Not adjusted for 'nop' cycles -> values are a few cycles higher than true duration
    cycles_{ser,des,enc,dec,encser,desdec} : cycles for complete (de)serialization step,
        measured using just one counter start/stop (not a sum of callcycles_raw entries).
        Adjusted for 'nop' cycles -> should give accurate function call duration
    data_{secnop,ser,serdes} : whole-program Data Segment size
    heap_{ser,des} : Maximum heap usage during step
    serialized_size : Size (Bytes) of serialized data
    stack_alloc_{ser,des} : Maximum stack usage (Bytes) during step.
        Based on online analysis (comparison of memory dumps)
    stack_set_{ser,des} : Number of stack bytes modified during step.
        Based on online analysis (comparison of memory dumps), should be
        smaller than the corresponding stack_alloc_ value
    text_{nop,ser,serdes} : whole-program Text Segment (code/Flash) size
    """

    def _median_cycles(data, key):
        # There should always be more than just one measurement -- otherwise
        # something went wrong
        if len(data[key]) <= 1:
            return np.nan
        for val in data[key]:
            # bogus data
            if val > 10_000_000:
                return np.nan
        for val in data['nop']:
            # bogus data
            if val > 10_000_000:
                return np.nan
        # All measurements in data[key] cover the same instructions, so they
        # should be identical -> it's safe to take the median.
        # However, we leave out the first measurement as it is often bogus.
        if key == 'nop':
            return np.median(data['nop'][1:])
        return max(0, int(np.median(data[key][1:]) - np.median(data['nop'][1:])))

    def _median_callcycles(data):
        ret = dict()
        for line in data.keys():
            ret[line] = np.median(data[line])
        return ret

    idem = lambda x: x
    datamap = [
        ['bss_nop', 'bss_size_nop', idem],
        ['bss_ser', 'bss_size_ser', idem],
        ['bss_serdes', 'bss_size_serdes', idem],
        ['callcycles_raw', 'callcycles', idem],
        ['callcycles_median', 'callcycles', _median_callcycles],
        # Used to remove nop cycles from callcycles_median
        ['cycles_nop', 'cycles', lambda x: Protolog._median_cycles(x, 'nop')],
        ['cycles_ser', 'cycles', lambda x: Protolog._median_cycles(x, 'ser')],
        ['cycles_des', 'cycles', lambda x: Protolog._median_cycles(x, 'des')],
        ['cycles_enc', 'cycles', lambda x: Protolog._median_cycles(x, 'enc')],
        ['cycles_dec', 'cycles', lambda x: Protolog._median_cycles(x, 'dec')],
        #['cycles_ser_arr', 'cycles', lambda x: np.array(x['ser'][1:]) - np.mean(x['nop'][1:])],
        #['cycles_des_arr', 'cycles', lambda x: np.array(x['des'][1:]) - np.mean(x['nop'][1:])],
        #['cycles_enc_arr', 'cycles', lambda x: np.array(x['enc'][1:]) - np.mean(x['nop'][1:])],
        #['cycles_dec_arr', 'cycles', lambda x: np.array(x['dec'][1:]) - np.mean(x['nop'][1:])],
        ['data_nop', 'data_size_nop', idem],
        ['data_ser', 'data_size_ser', idem],
        ['data_serdes', 'data_size_serdes', idem],
        ['heap_ser', 'heap_usage_ser', idem],
        ['heap_des', 'heap_usage_des', idem],
        ['serialized_size', 'serialized_size', idem],
        ['stack_alloc_ser', 'stack_online_ser', lambda x: x['allocated']],
        ['stack_set_ser', 'stack_online_ser', lambda x: x['used']],
        ['stack_alloc_des', 'stack_online_des', lambda x: x['allocated']],
        ['stack_set_des', 'stack_online_des', lambda x: x['used']],
        ['text_nop', 'text_size_nop', idem],
        ['text_ser', 'text_size_ser', idem],
        ['text_serdes', 'text_size_serdes', idem],
    ]

    def __init__(self, logfile, cpu_conf = None, cpu_conf_str = None, radio_conf = None, radio_conf_str = None):
        """
        Load and enrich raw protobench log data.

        The enriched data can be accessed via the .aggregate class member,
        see the class documentation for details.
        """
        with open(logfile, 'rb') as f:
            self.data = ubjson.load(f)
        self.libraries = set()
        self.architectures = set()
        self.aggregate = dict()

        for arch_lib in self.data.keys():
            arch, lib, libopts = arch_lib.split(':')
            library = lib + ':' + libopts
            for benchmark in self.data[arch_lib].keys():
                for benchmark_item in self.data[arch_lib][benchmark].keys():
                    subv = self.data[arch_lib][benchmark][benchmark_item]
                    for aggregate_label, data_label, getter in Protolog.datamap:
                        try:
                            self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, aggregate_label, data_label, getter)
                        except KeyError:
                            pass
                        except TypeError as e:
                            print('TypeError in {} {} {} {}: {} -> {}'.format(
                                arch_lib, benchmark, benchmark_item, aggregate_label,
                                subv[data_label]['v'], str(e)))
                            pass
                    try:
                        codegen = codegen_for_lib(lib, libopts.split(','), subv['data'])
                        if codegen.max_serialized_bytes != None:
                            self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, 'buffer_size', data_label, lambda x: codegen.max_serialized_bytes)
                        else:
                            self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, 'buffer_size', data_label, lambda x: 0)
                    except:
                        # avro's codegen will raise RuntimeError("Unsupported Schema") on unsupported data. Other libraries may just silently ignore it.
                        self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, 'buffer_size', data_label, lambda x: 0)
                    #self.aggregate[(benchmark, benchmark_item)][arch][lib][aggregate_label] = getter(value[data_label]['v'])


        for key in self.aggregate.keys():
            for arch in self.aggregate[key].keys():
                for lib, val in self.aggregate[key][arch].items():
                    try:
                        val['cycles_encser'] = val['cycles_enc'] + val['cycles_ser']
                    except KeyError:
                        pass
                    try:
                        val['cycles_desdec'] = val['cycles_des'] + val['cycles_dec']
                    except KeyError:
                        pass
                    try:
                        for line in val['callcycles_median'].keys():
                            val['callcycles_median'][line] -= val['cycles_nop']
                    except KeyError:
                        pass
                    try:
                        val['total_dmem_ser'] = val['stack_alloc_ser']
                        val['total_dmem_ser'] += val['heap_ser']
                    except KeyError:
                        pass
                    try:
                        val['total_dmem_des'] = val['stack_alloc_des']
                        val['total_dmem_des'] += val['heap_des']
                    except KeyError:
                        pass
                    try:
                        val['total_dmem_serdes'] = max(val['total_dmem_ser'], val['total_dmem_des'])
                    except KeyError:
                        pass
                    try:
                        val['text_ser_delta'] = val['text_ser'] - val['text_nop']
                        val['text_serdes_delta'] = val['text_serdes'] - val['text_nop']
                    except KeyError:
                        pass
                    try:
                        val['bss_ser_delta'] = val['bss_ser'] - val['bss_nop']
                        val['bss_serdes_delta'] = val['bss_serdes'] - val['bss_nop']
                    except KeyError:
                        pass
                    try:
                        val['data_ser_delta'] = val['data_ser'] - val['data_nop']
                        val['data_serdes_delta'] = val['data_serdes'] - val['data_nop']
                    except KeyError:
                        pass
                    try:
                        val['allmem_ser'] = val['text_ser'] + val['data_ser'] + val['bss_ser'] + val['total_dmem_ser'] - val['buffer_size']
                        val['allmem_serdes'] = val['text_serdes'] + val['data_serdes'] + val['bss_serdes'] + val['total_dmem_serdes'] - val['buffer_size']
                    except KeyError:
                        pass

        if cpu_conf_str:
            cpu_conf = utils.parse_conf_str(cpu_conf_str)

        if cpu_conf:
            cpu = cycles_to_energy.get_class(cpu_conf['model'])
            for key, value in cpu.default_params.items():
                if not key in cpu_conf:
                    cpu_conf[key] = value
            for key in self.aggregate.keys():
                for arch in self.aggregate[key].keys():
                    for lib, val in self.aggregate[key][arch].items():
                        try:
                            val['energy_enc'] = int(val['cycles_enc'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
                        except KeyError:
                            pass
                        try:
                            val['energy_ser'] = int(val['cycles_ser'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
                        except KeyError:
                            pass
                        try:
                            val['energy_encser'] = int(val['cycles_encser'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
                        except KeyError:
                            pass
                        try:
                            val['energy_des'] = int(val['cycles_des'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
                        except KeyError:
                            pass
                        try:
                            val['energy_dec'] = int(val['cycles_dec'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
                        except KeyError:
                            pass
                        try:
                            val['energy_desdec'] = int(val['cycles_desdec'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
                        except KeyError:
                            pass

        if radio_conf_str:
            radio_conf = utils.parse_conf_str(radio_conf_str)

        if radio_conf:
            radio = size_to_radio_energy.get_class(radio_conf['model'])
            for key, value in radio.default_params.items():
                if not key in radio_conf:
                    radio_conf[key] = value
            for key in self.aggregate.keys():
                for arch in self.aggregate[key].keys():
                    for lib, val in self.aggregate[key][arch].items():
                        try:
                            radio_conf['txbytes'] = val['serialized_size']
                            if radio_conf['txbytes'] > 0:
                                val['energy_tx'] = int(radio.get_energy(radio_conf) * 1e9)
                            else:
                                val['energy_tx'] = 0
                            val['energy_encsertx'] = val['energy_encser'] + val['energy_tx']
                        except KeyError:
                            pass


    def add_datapoint(self, arch, lib, key, value, aggregate_label, data_label, getter):
        """
        Set self.aggregate[key][arch][lib][aggregate_Label] = getter(value[data_label]['v']).

        Additionally, add lib to self.libraries and arch to self.architectures
        key usually is ('benchmark name', 'sub-benchmark index').
        """
        if data_label in value and 'v' in value[data_label]:
            self.architectures.add(arch)
            self.libraries.add(lib)
            if not key in self.aggregate:
                self.aggregate[key] = dict()
            if not arch in self.aggregate[key]:
                self.aggregate[key][arch] = dict()
            if not lib in self.aggregate[key][arch]:
                self.aggregate[key][arch][lib] = dict()
            self.aggregate[key][arch][lib][aggregate_label] = getter(value[data_label]['v'])