from collections import namedtuple
from datetime import datetime
from typing import Dict, List, Tuple
from mythril.laser.plugin.builder import PluginBuilder
from mythril.laser.plugin.interface import LaserPlugin
from mythril.laser.ethereum.svm import LaserEVM
from mythril.laser.ethereum.state.global_state import GlobalState
from datetime import datetime
import logging
# Type annotations:
# start_time: datetime
# end_time: datetime
_InstrExecRecord = namedtuple("_InstrExecRecord", ["start_time", "end_time"])
# Type annotations:
# total_time: float
# total_nr: float
# min_time: float
# max_time: float
_InstrExecStatistic = namedtuple(
"_InstrExecStatistic", ["total_time", "total_nr", "min_time", "max_time"]
)
# Map the instruction opcode to its records if all execution times
_InstrExecRecords = Dict[str, List[_InstrExecRecord]]
# Map the instruction opcode to the statistic of its execution times
_InstrExecStatistics = Dict[str, _InstrExecStatistic]
log = logging.getLogger(__name__)
[docs]class InstructionProfilerBuilder(PluginBuilder):
name = "instruction-profiler"
def __call__(self, *args, **kwargs):
return InstructionProfiler()
[docs]class InstructionProfiler(LaserPlugin):
"""Performance profile for the execution of each instruction."""
def __init__(self):
self._reset()
def _reset(self):
self.records = dict()
self.start_time = None
[docs] def initialize(self, symbolic_vm: LaserEVM) -> None:
@symbolic_vm.instr_hook("pre", None)
def get_start_time(op_code: str):
def start_time_wrapper(global_state: GlobalState):
self.start_time = datetime.now()
return start_time_wrapper
@symbolic_vm.instr_hook("post", None)
def record(op_code: str):
def record_opcode(global_state: GlobalState):
end_time = datetime.now()
try:
self.records[op_code].append(
_InstrExecRecord(self.start_time, end_time)
)
except KeyError:
self.records[op_code] = [
_InstrExecRecord(self.start_time, end_time)
]
return record_opcode
@symbolic_vm.laser_hook("stop_sym_exec")
def print_stats():
total, stats = self._make_stats()
s = "Total: {} s\n".format(total)
for op in sorted(stats):
stat = stats[op]
s += "[{:12s}] {:>8.4f} %, nr {:>6}, total {:>8.4f} s, avg {:>8.4f} s, min {:>8.4f} s, max {:>8.4f} s\n".format(
op,
stat.total_time * 100 / total,
stat.total_nr,
stat.total_time,
stat.total_time / stat.total_nr,
stat.min_time,
stat.max_time,
)
log.info(s)
def _make_stats(self) -> Tuple[float, _InstrExecStatistics]:
periods = {
op: list(
map(lambda r: r.end_time.timestamp() - r.start_time.timestamp(), rs)
)
for op, rs in self.records.items()
}
stats = dict()
total_time = 0
for _, (op, times) in enumerate(periods.items()):
stat = _InstrExecStatistic(
total_time=sum(times),
total_nr=len(times),
min_time=min(times),
max_time=max(times),
)
total_time += stat.total_time
stats[op] = stat
return total_time, stats