David Galiffi
d07bf508a9
The Omnitrace program is being renamed. Full name: "ROCm Systems Profiler" Package name: "rocprofiler-systems" Binary / Library names: "rocprof-sys-*" --------- Co-authored-by: Xuan Chen <xuchen@amd.com> Signed-off-by: David Galiffi <David.Galiffi@amd.com>
874 linhas
30 KiB
Python
874 linhas
30 KiB
Python
#!/usr/bin/env python3
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# MIT License
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#
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# Copyright (c) 2023-2024 Advanced Micro Devices, Inc. All Rights Reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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from __future__ import absolute_import
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__author__ = "AMD Research"
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__copyright__ = "Copyright 2023-2024, Advanced Micro Devices, Inc."
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__license__ = "MIT"
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__maintainer__ = "AMD Research"
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__status__ = "Development"
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import sys
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import json
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import jsondiff
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import re
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import pandas as pd
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import numpy as np
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import math
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from collections import OrderedDict
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import os
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import glob
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num_stddev = 1.0
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def set_num_stddev(_value):
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global num_stddev
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num_stddev = _value
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def mean(_data):
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return sum(_data) / float(len(_data)) if len(_data) > 0 else 0.0
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def stddev(_data):
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if len(_data) == 0:
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return 0.0
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_mean = mean(_data)
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_variance = sum([((x - _mean) ** 2) for x in _data]) / float(len(_data))
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return float(num_stddev) * math.sqrt(_variance)
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class validation(object):
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def __init__(self, _exp_re, _pp_re, _virt, _expected, _tolerance):
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self.experiment_filter = re.compile(_exp_re)
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self.progress_pt_filter = re.compile(_pp_re)
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self.virtual_speedup = int(_virt)
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self.program_speedup = float(_expected)
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self.tolerance = float(_tolerance)
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def validate(
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self,
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_exp_name,
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_pp_name,
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_virt_speedup,
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_prog_speedup,
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_prog_speedup_stddev,
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_base_speedup_stddev,
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):
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if (
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not re.search(self.experiment_filter, _exp_name)
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or not re.search(self.progress_pt_filter, _pp_name)
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or _virt_speedup != self.virtual_speedup
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):
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return None
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_tolerance = self.tolerance
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if _base_speedup_stddev > 2.0 * self.tolerance:
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sys.stderr.write(
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f" [{_exp_name}][{_pp_name}][{_virt_speedup}] base speedup has stddev > 2 * tolerance (+/- {_base_speedup_stddev:.3f}). Relaxing validation...\n"
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)
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_tolerance += math.sqrt(_base_speedup_stddev)
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elif _prog_speedup_stddev > 2.0 * self.tolerance:
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sys.stderr.write(
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f" [{_exp_name}][{_pp_name}][{_virt_speedup}] program speedup has stddev > 2 * tolerance (+/- {_prog_speedup_stddev:.3f}). Relaxing validation...\n"
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)
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_tolerance += math.sqrt(_prog_speedup_stddev)
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return _prog_speedup >= (self.program_speedup - _tolerance) and _prog_speedup <= (
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self.program_speedup + _tolerance
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)
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class throughput_point(object):
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def __init__(self, _speedup):
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self.speedup = _speedup
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self.delta = []
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self.duration = []
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def __iadd__(self, _data):
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self.delta += [float(_data[0])]
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self.duration += [float(_data[1])]
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def __len__(self):
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return len(self.duration)
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def __eq__(self, rhs):
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return self.speedup == rhs.speedup
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def __neq__(self, rhs):
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return not self == rhs
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def __lt__(self, rhs):
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return self.speedup < rhs.speedup
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def get_data(self):
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return [x / y for x, y in zip(self.duration, self.delta)]
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def mean(self):
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return sum(self.duration) / sum(self.delta)
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class latency_point(object):
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def __init__(self, _speedup):
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self.speedup = _speedup
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self.arrivals = []
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self.departures = []
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self.duration = []
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def __iadd__(self, _data):
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self.arrivals += [float(_data[0])]
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self.departures += [float(_data[1])]
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self.duration += [float(_data[2])]
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def __len__(self):
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return len(self.duration)
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def __eq__(self, rhs):
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return self.speedup == rhs.speedup
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def __neq__(self, rhs):
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return not self == rhs
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def __lt__(self, rhs):
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return self.speedup < rhs.speedup
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def get_data(self):
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return [y / x for x, y in zip(self.arrivals, self.duration)]
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def get_difference(self):
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_duration = sum(self.duration)
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return [x / _duration for x in self.duration]
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def mean(self):
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rate = sum(self.arrivals) / sum(self.duration)
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return sum(self.get_difference()) / rate
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class line_speedup(object):
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def __init__(self, _name="", _prog="", _exp_data=None, _exp_base=None):
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self.name = _name
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self.prog = _prog
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self.data = _exp_data
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self.base = _exp_base
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def virtual_speedup(self):
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if self.data is None or self.base is None:
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return 0.0
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return self.data.speedup
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def compute_speedup(self):
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if self.data is None or self.base is None:
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return 0.0
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return ((self.base.mean() - self.data.mean()) / self.base.mean()) * 100
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def compute_speedup_stddev(self):
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if self.data is None or self.base is None:
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return 0.0
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_data = []
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_base = self.base.mean()
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for ditr in self.data.get_data():
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_data += [((_base - ditr) / _base) * 100]
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return stddev(_data)
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def get_name(self):
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return ":".join(
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[
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os.path.basename(x) if os.path.isfile(x) else x
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for x in self.name.split(":")
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]
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)
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def __str__(self):
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if self.data is None or self.base is None:
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return f"{self.name}"
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_line_speedup = self.compute_speedup()
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_line_stddev = self.compute_speedup_stddev() # 3 stddev == 99.87%
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_name = self.get_name()
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return f"[{_name}][{self.prog}][{self.data.speedup:3}] speedup: {_line_speedup:6.1f} +/- {_line_stddev:6.2f} %"
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def __eq__(self, rhs):
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return (
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self.name == rhs.name
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and self.prog == rhs.prog
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and self.data == rhs.data
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and self.base == rhs.base
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)
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def __neq__(self, rhs):
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return not self == rhs
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def __lt__(self, rhs):
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if self.name != rhs.name:
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return self.name < rhs.name
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elif self.prog != rhs.prog:
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return self.prog < rhs.prog
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elif self.data != rhs.data:
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return self.data < rhs.data
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elif self.base != rhs.base:
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return self.base < rhs.base
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return False
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class experiment_progress(object):
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def __init__(self, _data):
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self.data = _data
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def get_impact(self):
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speedup_c = [x.compute_speedup() for x in self.data]
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speedup_v = [x.virtual_speedup() for x in self.data]
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impact = []
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for i in range(len(self.data) - 1):
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x = speedup_v[i + 1] - speedup_v[i]
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y = [speedup_c[i], speedup_c[i + 1]]
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y_min = min(y)
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y_max = max(y)
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a_low = x * y_min
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a_upp = 0.5 * x * (y_max - y_min)
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impact += [a_low + a_upp]
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# impact = [x.compute_speedup() for x in self.data]
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return [sum(impact), mean(impact), stddev(impact)]
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def __len__(self):
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return len(self.data)
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def __str__(self):
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_impact_v = self.get_impact()
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_name = self.data[0].get_name()
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_prog = self.data[0].prog
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_impact = [
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f"[{_name}][{_prog}][sum] impact: {_impact_v[0]:6.1f}",
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f"[{_name}][{_prog}][avg] impact: {_impact_v[1]:6.1f} +/- {_impact_v[2]:6.2f}",
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]
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return "\n".join([f"{x}" for x in self.data] + _impact)
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def __lt__(self, rhs):
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self.data.sort()
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return self.get_impact()[0] < rhs.get_impact()[0]
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def process_samples(data, _data):
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if not _data:
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return data
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for record in _data["rocprofsys"]["causal"]["records"]:
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for samp in record["samples"]:
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_info = samp["info"]
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_count = samp["count"]
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_func = _info["dfunc"]
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if _func not in data:
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data[_func] = 0
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data[_func] += _count
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for dwarf_entry in _info["dwarf_info"]:
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_name = "{}:{}".format(dwarf_entry["file"], dwarf_entry["line"])
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if _name not in data:
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data[_name] = 0
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data[_name] += _count
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return data
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def process_data(data, _data, experiments, progress_points):
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def find_or_insert(_data, _value, _type):
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if _value not in _data:
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if _type == "throughput":
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_data[_value] = throughput_point(_value)
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elif _type == "latency":
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_data[_value] = latency_point(_value)
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else:
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raise RuntimeError(f"unknown data type: {_type}")
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return _data[_value]
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if not _data:
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return data
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_selection_filter = re.compile(experiments)
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_progresspt_filter = re.compile(progress_points)
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for record in _data["rocprofsys"]["causal"]["records"]:
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for exp in record["experiments"]:
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_speedup = exp["virtual_speedup"]
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_duration = exp["duration"]
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_file = exp["selection"]["info"]["file"]
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_line = exp["selection"]["info"]["line"]
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_func = exp["selection"]["info"]["dfunc"]
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_sym_addr = exp["selection"]["symbol_address"]
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_selected = ":".join([_file, f"{_line}"]) if _sym_addr == 0 else _func
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if not re.search(_selection_filter, _selected):
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continue
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if _selected not in data:
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data[_selected] = {}
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for pts in exp["progress_points"]:
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_name = pts["name"]
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if not re.search(_progresspt_filter, _name):
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continue
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if _name not in data[_selected]:
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data[_selected][_name] = {}
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if "delta" in pts:
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_delt = pts["delta"]
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if _delt > 0:
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itr = find_or_insert(
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data[_selected][_name], _speedup, "throughput"
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)
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itr += [_delt, _duration]
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elif "arrival" in pts and pts["arrival"] > 0:
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itr = find_or_insert(data[_selected][_name], _speedup, "latency")
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itr += [pts["arrival"], pts["departure"], _duration]
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else:
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_delt = pts["laps"]
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if _delt > 0:
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itr = find_or_insert(data[_selected][_name], _speedup)
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itr += [_delt, _duration]
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return data
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def compute_speedups(runs, speedups=[], num_points=0, validate=[], debug=False):
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out = pd.DataFrame()
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data = {}
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for workload in runs:
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_data = runs[workload]
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for selected, pitr in _data.items():
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if selected not in data:
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data[selected] = {}
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for progpt, ditr in pitr.items():
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data[selected][progpt] = OrderedDict(sorted(ditr.items()))
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ret = []
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for selected, pitr in _data.items():
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for progpt, ditr in pitr.items():
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if 0 not in ditr.keys():
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print(f"missing baseline data for {progpt} in {selected}...")
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continue
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for speedup, itr in ditr.items():
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if len(speedups) > 0 and speedup not in speedups:
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continue
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if speedup != itr.speedup:
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raise ValueError(f"in {selected}: {speedup} != {itr.speedup}")
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_val = line_speedup(selected, progpt, itr, ditr[0])
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ret.append(_val)
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ret.sort()
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_last_name = None
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_last_prog = None
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result = []
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for itr in ret:
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if itr.name != _last_name or itr.prog != _last_prog:
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result.append([])
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result[-1].append(itr)
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_last_name = itr.name
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_last_prog = itr.prog
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_data = []
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for itr in result:
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experiment_prog = experiment_progress(itr)
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_data.append(experiment_prog)
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if len(itr) != 0:
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impact = experiment_prog.get_impact()
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for itrx in itr:
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speedup = itrx.compute_speedup()
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line_stddev = itrx.compute_speedup_stddev()
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if speedup <= 200 and speedup >= -100:
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out = pd.concat(
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[
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out,
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pd.DataFrame(
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{
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"idx": [(itrx.prog, itrx.name)],
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"progress points": [itrx.prog],
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"point": [itrx.name],
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"line speedup": [itrx.virtual_speedup()],
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"program speedup": [speedup],
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"speedup err": line_stddev,
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"impact sum": impact[0],
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"impact avg": impact[1],
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"impact err": float(impact[2]),
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"workload": workload,
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}
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),
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],
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ignore_index=True,
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)
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_data.sort()
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if debug:
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for itr in _data:
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if len(itr) < num_points:
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continue
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print("")
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print(f"{itr}")
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sys.stdout.flush()
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validations = get_validations(validate)
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expected_validations = len(validations)
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correct_validations = 0
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if expected_validations > 0:
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print(f"\nPerforming {expected_validations} validations...\n")
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for eitr in _data:
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_experiment = eitr.data[0].get_name()
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_progresspt = eitr.data[0].prog
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_base_speedup_stddev = eitr.data[0].compute_speedup_stddev()
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for ditr in eitr.data:
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_virt_speedup = ditr.virtual_speedup()
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_prog_speedup = ditr.compute_speedup()
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_prog_speedup_stddev = ditr.compute_speedup_stddev()
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for vitr in validations:
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_v = vitr.validate(
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_experiment,
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_progresspt,
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_virt_speedup,
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_prog_speedup,
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_prog_speedup_stddev,
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_base_speedup_stddev,
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)
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if _v is None:
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continue
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if _v is True:
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correct_validations += 1
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else:
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sys.stderr.write(
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f" [{_experiment}][{_progresspt}][{_virt_speedup}] failed validation: {_prog_speedup:8.3f} != {vitr.program_speedup} +/- {vitr.tolerance}\n"
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)
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if expected_validations != correct_validations:
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sys.stderr.flush()
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sys.stderr.write(
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f"\nCausal profiling predictions not validated. Expected {expected_validations}, found {correct_validations}\n"
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)
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sys.stderr.flush()
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sys.exit(-1)
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elif expected_validations > 0:
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print(f"Causal profiling predictions validated: {expected_validations}")
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return out
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def get_validations(validate):
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data = []
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_len = len(validate)
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if _len == 0:
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return data
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elif _len % 5 != 0:
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raise ValueError(
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"validation requires format: {0}experiment regex{1} {0}progress-point regex{1} {0}virtual-speedup{1} {0}expected-speedup{1} {0}tolerance{1} (i.e. 5 args per validation. There are {2} extra/missing arguments".format(
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"{", "}", _len % 5
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)
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)
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v = validate
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for i in range(int(_len / 5)):
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off = 5 * i
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data.append(
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validation(v[off + 0], v[off + 1], v[off + 2], v[off + 3], v[off + 4])
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)
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return data
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def compute_sorts(_data):
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Max_speedup_order = _data.sort_values(
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by="program speedup", ascending=False
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).point.unique()
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Min_speedup_order = _data.sort_values(
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by="program speedup", ascending=True
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).point.unique()
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point_counts = _data.point.value_counts()
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# speedups = pd.DataFrame(_data["Line Speedup"].unique(), columns=["Line Speedup"])
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_data["max speedup"] = np.nan
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_data["min speedup"] = np.nan
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_data["point count"] = np.nan
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for index in _data.index:
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_data.at[index, "max speedup"] = np.where(
|
|
Max_speedup_order == _data.at[index, "point"]
|
|
)[0][0]
|
|
_data.at[index, "min speedup"] = np.where(
|
|
Min_speedup_order == _data.at[index, "point"]
|
|
)[0][0]
|
|
_data.at[index, "point count"] = point_counts[_data.at[index, "point"]]
|
|
return _data
|
|
|
|
|
|
def is_valid_data_point(data):
|
|
return not math.isnan(data) and not math.isinf(data)
|
|
|
|
|
|
def get_value(splice):
|
|
if "type" in splice:
|
|
# Something might happen here later
|
|
print("type found in coz file, finish adding code")
|
|
sys.exit(1)
|
|
if "difference" in splice or "speedup" in splice:
|
|
return float(splice[1])
|
|
else:
|
|
return splice[1]
|
|
|
|
|
|
def add_throughput(df, experiment, value):
|
|
ix = (experiment["selected"], value["name"], experiment["speedup"])
|
|
df_points = list(df.index)
|
|
if ix not in df_points:
|
|
new_row = pd.DataFrame(
|
|
data={
|
|
# "selected" : [experiment["selected"]],
|
|
# "speedup" : [float(experiment["speedup"])],
|
|
"delta": [float(value["delta"])],
|
|
"duration": [float(experiment["duration"])],
|
|
"type": ["throughput"],
|
|
},
|
|
index=[ix],
|
|
)
|
|
df = pd.concat([new_row, df])
|
|
else:
|
|
df["delta"][ix] = df["delta"][ix] + float(value["delta"])
|
|
df["duration"][ix] = df["duration"][ix] + float(experiment["duration"])
|
|
return df
|
|
|
|
|
|
def add_latency(df, experiment, value):
|
|
ix = (experiment["selected"], experiment["speedup"])
|
|
df_points = list(df.index)
|
|
if ix not in df_points:
|
|
df = pd.concat(
|
|
[
|
|
df,
|
|
pd.Dataframe(
|
|
data={
|
|
"selected": [experiment["selected"]],
|
|
"point": [value["name"]],
|
|
"speedup": [float(experiment["speedup"])],
|
|
"arrivals": [float(value["arrivals"])],
|
|
"departures": [float(value["departures"])],
|
|
"duration": [0],
|
|
"type": ["latency"],
|
|
}
|
|
),
|
|
]
|
|
)
|
|
|
|
if value.duration == 0:
|
|
df["difference"] = value.difference
|
|
else:
|
|
duration = df["duration"][ix] + float(experiment["duration"])
|
|
df["difference"][ix] = df["difference"][ix] * df["duration"][ix] / duration
|
|
df["difference"][ix] = (
|
|
df["difference"]
|
|
+ (float(value["difference"]) * float(experiment["duration"])) / duration
|
|
)
|
|
|
|
df["duration"] = df["duration"] + float(experiment["duration"])
|
|
|
|
return df
|
|
|
|
|
|
def parse_files(
|
|
files,
|
|
experiments=".*",
|
|
progress_points=".*",
|
|
speedups=[],
|
|
num_points=0,
|
|
validate=[],
|
|
verbose=0,
|
|
cli=False,
|
|
):
|
|
result_df = pd.DataFrame()
|
|
sample_df = pd.DataFrame()
|
|
|
|
def name_wo_ext(x):
|
|
return x.replace(".json", "").replace(".coz", "")
|
|
|
|
json_files = [x for x in filter(lambda y: y.endswith(".json"), files)]
|
|
coz_files = [x for x in filter(lambda y: y.endswith(".coz"), files)]
|
|
read_files = []
|
|
file_names = []
|
|
|
|
# prefer JSON files first
|
|
files = json_files + coz_files
|
|
for file in files:
|
|
if verbose >= 3:
|
|
print(f"Potentially reading causal profile: '{file}'...")
|
|
|
|
_base_name = name_wo_ext(file)
|
|
# do not read in a COZ file if the JSON already read
|
|
if _base_name in read_files:
|
|
continue
|
|
|
|
if verbose >= 3 or (verbose >= 1 and not cli):
|
|
print(f"Reading causal profile: '{file}'...")
|
|
|
|
if file.endswith(".json"):
|
|
with open(file, "r") as j:
|
|
_data = json.load(j)
|
|
dict_data = {}
|
|
# make sure the JSON is an rocprof-sys causal JSON
|
|
if "rocprofsys" not in _data or "causal" not in _data["rocprofsys"]:
|
|
continue
|
|
dict_data[file] = process_data({}, _data, experiments, progress_points)
|
|
samps = process_samples({}, _data)
|
|
sample_df = pd.concat(
|
|
[
|
|
sample_df,
|
|
pd.DataFrame(
|
|
[
|
|
{"location": loc, "count": count}
|
|
for loc, count in sorted(samps.items())
|
|
]
|
|
),
|
|
]
|
|
)
|
|
result_df = pd.concat(
|
|
[
|
|
result_df,
|
|
compute_sorts(
|
|
compute_speedups(
|
|
dict_data,
|
|
speedups,
|
|
num_points,
|
|
validate,
|
|
verbose >= 3 or cli,
|
|
)
|
|
),
|
|
]
|
|
)
|
|
read_files.append(_base_name)
|
|
file_names.append(file)
|
|
|
|
elif file.endswith(".coz"):
|
|
try:
|
|
f = open(file, "r")
|
|
except IOError as e:
|
|
sys.stderr.write(f"{e}\n")
|
|
continue
|
|
|
|
lines = f.readlines()
|
|
# Parse lines
|
|
experiment = None
|
|
samps = {}
|
|
data = pd.DataFrame()
|
|
|
|
for line in lines:
|
|
if line != "\n":
|
|
data_type = ""
|
|
value = {}
|
|
sections = line.split("\t")
|
|
if len(sections) == 0:
|
|
continue
|
|
value["type"] = sections[0]
|
|
for section in sections:
|
|
splice = section.split("\n")[0].split("=")
|
|
if len(splice) > 1:
|
|
val = get_value(splice)
|
|
value[splice[0]] = val
|
|
else:
|
|
data_type = splice[0]
|
|
if data_type == "experiment":
|
|
experiment = value
|
|
elif data_type == "throughput-point" or data_type == "progress-point":
|
|
experiment["speedup"] = 100 * experiment["speedup"]
|
|
data = add_throughput(data, experiment, value)
|
|
elif data_type == "latency-point":
|
|
data = add_latency(data, experiment, value)
|
|
elif data_type == "samples":
|
|
if value["location"] not in samps:
|
|
samps[value["location"]] = 0
|
|
samps[value["location"]] += int(value["count"])
|
|
elif data_type not in ["startup", "shutdown", "runtime"]:
|
|
print("Invalid profile")
|
|
|
|
result_df = pd.concat(
|
|
[
|
|
result_df,
|
|
get_speedup_data(data.sort_index()),
|
|
]
|
|
)
|
|
sample_df = pd.concat(
|
|
[
|
|
sample_df,
|
|
pd.DataFrame(
|
|
[
|
|
{"location": loc, "count": count}
|
|
for loc, count in sorted(samps.items())
|
|
]
|
|
),
|
|
]
|
|
)
|
|
read_files.append(_base_name)
|
|
file_names.append(file)
|
|
|
|
return result_df, sample_df, file_names
|
|
|
|
|
|
def parse_uploaded_file(file_name, file, experiments=".*", progress_points=".*"):
|
|
data = pd.DataFrame()
|
|
if "{" in file:
|
|
dict_data = {}
|
|
_data = json.loads(file)
|
|
|
|
dict_data = {
|
|
file_name: process_data(dict_data, _data, experiments, progress_points)
|
|
}
|
|
samps = process_samples({}, _data)
|
|
sample_data = pd.DataFrame(
|
|
[{"location": loc, "count": count} for loc, count in sorted(samps.items())]
|
|
)
|
|
data = compute_sorts(compute_speedups(dict_data))
|
|
return data, sample_data
|
|
|
|
else:
|
|
# coz
|
|
for line in file.split("\n"):
|
|
if len(line) > 0:
|
|
data_type = ""
|
|
value = {}
|
|
sections = line.split("\t")
|
|
value["type"] = sections[0]
|
|
for section in sections:
|
|
splice = section.split("\n")[0].split("=")
|
|
if len(splice) > 1:
|
|
value[splice[0]] = get_value(splice)
|
|
else:
|
|
data_type = splice[0]
|
|
if data_type == "experiment":
|
|
experiment = value
|
|
elif data_type == "throughput-point" or data_type == "progress-point":
|
|
experiment["speedup"] = 100 * experiment["speedup"]
|
|
data = add_throughput(data, experiment, value)
|
|
elif data_type == "latency-point":
|
|
data = add_latency(data, experiment, value)
|
|
elif data_type not in ["startup", "shutdown", "samples", "runtime"]:
|
|
print("Invalid profile")
|
|
# sys.exit(1)
|
|
data = get_speedup_data(data.sort_index())
|
|
return data
|
|
|
|
|
|
def get_data_point(data):
|
|
val = ""
|
|
if math.isclose(data.delta, 0, rel_tol=1e-09, abs_tol=0.0):
|
|
return np.nan
|
|
if data["type"] == "throughput":
|
|
val = data.duration / data.delta
|
|
|
|
return val
|
|
elif data["type"] == "latency":
|
|
arrivalRate = data.arrivals / data.duration
|
|
val = data.difference / arrivalRate
|
|
else:
|
|
print("invalid datapoint")
|
|
val = np.inf
|
|
if math.isinf(val):
|
|
return np.nan
|
|
else:
|
|
return val
|
|
|
|
|
|
def get_speedup_data(data):
|
|
baseline_data_point = get_data_point(data.iloc[0])
|
|
speedup_df = pd.DataFrame()
|
|
curr_selected = ""
|
|
for index, row in data.iterrows():
|
|
if curr_selected != index[0]:
|
|
curr_selected = index[0]
|
|
baseline_data_point = get_data_point(row)
|
|
|
|
maximize = True
|
|
|
|
if row["type"] == "latency":
|
|
maximize = False
|
|
|
|
if not math.isnan(baseline_data_point):
|
|
# for speedup in row:
|
|
data_point = get_data_point(row)
|
|
if not math.isnan(data_point):
|
|
progress_speedup = (
|
|
baseline_data_point - data_point
|
|
) / baseline_data_point
|
|
# speedup = row["speedup"]
|
|
speedup = row.name[2]
|
|
|
|
name = row.name[0]
|
|
|
|
if not maximize:
|
|
# We are trying to *minimize* this progress point, so negate the speedup.
|
|
progress_speedup = -progress_speedup
|
|
|
|
if progress_speedup >= -1 and progress_speedup <= 2:
|
|
speedup_df = pd.concat(
|
|
[
|
|
pd.DataFrame.from_dict(
|
|
data={
|
|
"point": [name],
|
|
"speedup": [speedup],
|
|
"progress_speedup": [100 * progress_speedup],
|
|
}
|
|
),
|
|
speedup_df,
|
|
]
|
|
)
|
|
speedup_df = speedup_df.sort_values(by=["speedup"])
|
|
return speedup_df
|
|
|
|
|
|
def find_causal_files(workload_path, verbose, recursive):
|
|
input_files = []
|
|
|
|
def find_causal_files_helper(inp, _files):
|
|
_input_files_tmp = []
|
|
for itr in _files:
|
|
if os.path.isfile(itr) and itr.endswith(".json"):
|
|
with open(itr, "r") as f:
|
|
inp_data = json.load(f)
|
|
if (
|
|
"rocprofsys" not in inp_data.keys()
|
|
or "causal" not in inp_data["rocprofsys"].keys()
|
|
):
|
|
if verbose >= 2:
|
|
print(f"{itr} is not a causal profile")
|
|
continue
|
|
_input_files_tmp += [itr]
|
|
elif os.path.isfile(itr) and itr.endswith(".coz"):
|
|
_input_files_tmp += [itr]
|
|
return _input_files_tmp
|
|
|
|
for inp in workload_path:
|
|
if verbose == 3:
|
|
print("find_causal_files inp:", inp)
|
|
if os.path.exists(inp):
|
|
if os.path.isdir(inp):
|
|
_files = glob.glob(os.path.join(inp, "**"), recursive=recursive)
|
|
_input_files_tmp = find_causal_files_helper(inp, _files)
|
|
if len(_input_files_tmp) == 0:
|
|
raise ValueError(f"No causal profiles found in {inp}")
|
|
else:
|
|
input_files += _input_files_tmp
|
|
elif os.path.isfile(inp):
|
|
input_files += [inp]
|
|
else:
|
|
_files = glob.glob(inp, recursive=recursive)
|
|
_input_files_tmp = find_causal_files_helper(inp, _files)
|
|
if len(_input_files_tmp) == 0:
|
|
raise ValueError(f"No causal profiles found in {inp}")
|
|
else:
|
|
input_files += _input_files_tmp
|
|
return input_files
|
|
|
|
|
|
def metadata_diff(json1, json2):
|
|
res = jsondiff.diff(json1, json2)
|
|
if res:
|
|
print("Diff found")
|
|
else:
|
|
print("Same")
|