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#!/usr/bin/env python3
import argparse
import json
import logging
import sys
import dfatool.cli
import dfatool.utils
from dfatool.model import AnalyticModel
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
)
parser.add_argument("--aggregate", choices=["sum"], default="sum")
parser.add_argument("--aggregate-unit", choices=["s", "B/s"], default="s")
parser.add_argument(
"--aggregate-init",
default=0,
type=float,
)
parser.add_argument(
"--log-level",
metavar="LEVEL",
choices=["debug", "info", "warning", "error"],
default="warning",
help="Set log level",
)
parser.add_argument("--normalize-output", type=str)
parser.add_argument(
"--info",
action="store_true",
help="Show benchmark information (number of measurements, parameter values, ...)",
)
parser.add_argument(
"--models",
nargs="+",
type=str,
help="Path to model file (.json or .json.xz)",
)
parser.add_argument(
"--use-lut",
action="store_true",
help="Use LUT rather than performance model for prediction",
)
parser.add_argument("event", nargs="+", type=str)
args = parser.parse_args()
if args.log_level:
numeric_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(numeric_level, int):
print(f"Invalid log level: {args.log_level}", file=sys.stderr)
sys.exit(1)
logging.basicConfig(
level=numeric_level,
format="{asctime} {levelname}:{name}:{message}",
style="{",
)
models = list()
for model_file in args.models:
with open(model_file, "r") as f:
models.append(AnalyticModel.from_json(json.load(f)))
if args.info:
for i in range(len(models)):
print(f"""{args.models[i]}: {" ".join(models[i].parameters)}""")
for name in models[i].names:
for attr in models[i].attributes(name):
print(f" {name}.{attr}")
aggregate = args.aggregate_init
for event in args.event:
event_normalizer = lambda p: p
if "/" in event:
v1, v2 = event.split("/")
if dfatool.utils.is_numeric(v1):
event = v2.strip()
event_normalizer = lambda p: dfatool.utils.soft_cast_float(v1) / p
elif dfatool.utils.is_numeric(v2):
event = v1.strip()
event_normalizer = lambda p: p / dfatool.utils.soft_cast_float(v2)
else:
raise RuntimeError(f"Cannot parse '{event}'")
nn, param = event.split("(")
name, action = nn.split(".")
param_model = None
ref_model = None
for model in models:
if name in model.names and action in model.attributes(name):
ref_model = model
if args.use_lut:
param_model = model.get_param_lut(allow_none=True)
else:
param_model, param_info = model.get_fitted()
break
if param_model is None:
raise RuntimeError(f"Did not find a model for {name}.{action}")
param = param.removesuffix(")")
if param == "":
param = dict()
else:
param = dfatool.utils.parse_conf_str(param)
param_list = dfatool.utils.param_dict_to_list(param, ref_model.parameters)
if not args.use_lut and not param_info(name, action).is_predictable(param_list):
logging.warning(
f"Cannot predict {name}.{action}({param}), falling back to static model"
)
event_output = event_normalizer(
param_model(
name,
action,
param=param_list,
)
)
if args.aggregate == "sum":
aggregate += event_output
if args.normalize_output:
sf = dfatool.cli.parse_shift_function(
"--normalize-output", args.normalize_output
)
print(dfatool.utils.human_readable(sf(aggregate), args.aggregate_unit))
else:
print(dfatool.utils.human_readable(aggregate, args.aggregate_unit))
if __name__ == "__main__":
main()
|
