Adding upstream version 6.6.15.upstream/6.6.15

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 574 insertions, 0 deletions

diff --git a/tools/perf/tests/shell/lib/perf_metric_validation.py b/tools/perf/tests/shell/lib/perf_metric_validation.py
new file mode 100644
index 000000000..50a34a9cc
--- /dev/null
+++ b/tools/perf/tests/shell/lib/perf_metric_validation.py
@@ -0,0 +1,574 @@
+#SPDX-License-Identifier: GPL-2.0
+import re
+import csv
+import json
+import argparse
+from pathlib import Path
+import subprocess
+
+class Validator:
+    def __init__(self, rulefname, reportfname='', t=5, debug=False, datafname='', fullrulefname='', workload='true', metrics=''):
+        self.rulefname = rulefname
+        self.reportfname = reportfname
+        self.rules = None
+        self.collectlist:str = metrics
+        self.metrics = self.__set_metrics(metrics)
+        self.skiplist = set()
+        self.tolerance = t
+
+        self.workloads = [x for x in workload.split(",") if x]
+        self.wlidx = 0 # idx of current workloads
+        self.allresults = dict() # metric results of all workload
+        self.allignoremetrics = dict() # metrics with no results or negative results
+        self.allfailtests = dict()
+        self.alltotalcnt = dict()
+        self.allpassedcnt = dict()
+        self.allerrlist = dict()
+
+        self.results = dict() # metric results of current workload
+        # vars for test pass/failure statistics
+        self.ignoremetrics= set() # metrics with no results or negative results, neg result counts as a failed test
+        self.failtests = dict()
+        self.totalcnt = 0
+        self.passedcnt = 0
+        # vars for errors
+        self.errlist = list()
+
+        # vars for Rule Generator
+        self.pctgmetrics = set() # Percentage rule
+
+        # vars for debug
+        self.datafname = datafname
+        self.debug = debug
+        self.fullrulefname = fullrulefname
+
+    def __set_metrics(self, metrics=''):
+        if metrics != '':
+            return set(metrics.split(","))
+        else:
+            return set()
+
+    def read_json(self, filename: str) -> dict:
+        try:
+            with open(Path(filename).resolve(), "r") as f:
+                data = json.loads(f.read())
+        except OSError as e:
+            print(f"Error when reading file {e}")
+            sys.exit()
+
+        return data
+
+    def json_dump(self, data, output_file):
+        parent = Path(output_file).parent
+        if not parent.exists():
+            parent.mkdir(parents=True)
+
+        with open(output_file, "w+") as output_file:
+            json.dump(data,
+                      output_file,
+                      ensure_ascii=True,
+                      indent=4)
+
+    def get_results(self, idx:int = 0):
+        return self.results[idx]
+
+    def get_bounds(self, lb, ub, error, alias={}, ridx:int = 0) -> list:
+        """
+        Get bounds and tolerance from lb, ub, and error.
+        If missing lb, use 0.0; missing ub, use float('inf); missing error, use self.tolerance.
+
+        @param lb: str/float, lower bound
+        @param ub: str/float, upper bound
+        @param error: float/str, error tolerance
+        @returns: lower bound, return inf if the lower bound is a metric value and is not collected
+                  upper bound, return -1 if the upper bound is a metric value and is not collected
+                  tolerance, denormalized base on upper bound value
+        """
+        # init ubv and lbv to invalid values
+        def get_bound_value (bound, initval, ridx):
+            val = initval
+            if isinstance(bound, int) or isinstance(bound, float):
+                val = bound
+            elif isinstance(bound, str):
+                if bound == '':
+                    val = float("inf")
+                elif bound in alias:
+                    vall = self.get_value(alias[ub], ridx)
+                    if vall:
+                        val = vall[0]
+                elif bound.replace('.', '1').isdigit():
+                    val = float(bound)
+                else:
+                    print("Wrong bound: {0}".format(bound))
+            else:
+                print("Wrong bound: {0}".format(bound))
+            return val
+
+        ubv = get_bound_value(ub, -1, ridx)
+        lbv = get_bound_value(lb, float('inf'), ridx)
+        t = get_bound_value(error, self.tolerance, ridx)
+
+        # denormalize error threshold
+        denormerr = t * ubv / 100 if ubv != 100 and ubv > 0 else t
+
+        return lbv, ubv, denormerr
+
+    def get_value(self, name:str, ridx:int = 0) -> list:
+        """
+        Get value of the metric from self.results.
+        If result of this metric is not provided, the metric name will be added into self.ignoremetics and self.errlist.
+        All future test(s) on this metric will fail.
+
+        @param name: name of the metric
+        @returns: list with value found in self.results; list is empty when value is not found.
+        """
+        results = []
+        data = self.results[ridx] if ridx in self.results else self.results[0]
+        if name not in self.ignoremetrics:
+            if name in data:
+                results.append(data[name])
+            elif name.replace('.', '1').isdigit():
+                results.append(float(name))
+            else:
+                self.ignoremetrics.add(name)
+        return results
+
+    def check_bound(self, val, lb, ub, err):
+        return True if val <= ub + err and val >= lb - err else False
+
+    # Positive Value Sanity check
+    def pos_val_test(self):
+        """
+        Check if metrics value are non-negative.
+        One metric is counted as one test.
+        Failure: when metric value is negative or not provided.
+        Metrics with negative value will be added into the self.failtests['PositiveValueTest'] and self.ignoremetrics.
+        """
+        negmetric = dict()
+        pcnt = 0
+        tcnt = 0
+        rerun = list()
+        for name, val in self.get_results().items():
+            if val < 0:
+                negmetric[name] = val
+                rerun.append(name)
+            else:
+                pcnt += 1
+            tcnt += 1
+        if len(rerun) > 0 and len(rerun) < 20:
+            second_results = dict()
+            self.second_test(rerun, second_results)
+            for name, val in second_results.items():
+                if name not in negmetric: continue
+                if val >= 0:
+                    del negmetric[name]
+                    pcnt += 1
+
+        self.failtests['PositiveValueTest']['Total Tests'] = tcnt
+        self.failtests['PositiveValueTest']['Passed Tests'] = pcnt
+        if len(negmetric.keys()):
+            self.ignoremetrics.update(negmetric.keys())
+            negmessage = ["{0}(={1:.4f})".format(name, val) for name, val in negmetric.items()]
+            self.failtests['PositiveValueTest']['Failed Tests'].append({'NegativeValue': negmessage})
+
+        return
+
+    def evaluate_formula(self, formula:str, alias:dict, ridx:int = 0):
+        """
+        Evaluate the value of formula.
+
+        @param formula: the formula to be evaluated
+        @param alias: the dict has alias to metric name mapping
+        @returns: value of the formula is success; -1 if the one or more metric value not provided
+        """
+        stack = []
+        b = 0
+        errs = []
+        sign = "+"
+        f = str()
+
+        #TODO: support parenthesis?
+        for i in range(len(formula)):
+            if i+1 == len(formula) or formula[i] in ('+', '-', '*', '/'):
+                s = alias[formula[b:i]] if i+1 < len(formula) else alias[formula[b:]]
+                v = self.get_value(s, ridx)
+                if not v:
+                    errs.append(s)
+                else:
+                    f = f + "{0}(={1:.4f})".format(s, v[0])
+                    if sign == "*":
+                        stack[-1] = stack[-1] * v
+                    elif sign == "/":
+                        stack[-1] = stack[-1] / v
+                    elif sign == '-':
+                        stack.append(-v[0])
+                    else:
+                        stack.append(v[0])
+                if i + 1 < len(formula):
+                    sign = formula[i]
+                    f += sign
+                    b = i + 1
+
+        if len(errs) > 0:
+            return -1, "Metric value missing: "+','.join(errs)
+
+        val = sum(stack)
+        return val, f
+
+    # Relationships Tests
+    def relationship_test(self, rule: dict):
+        """
+        Validate if the metrics follow the required relationship in the rule.
+        eg. lower_bound <= eval(formula)<= upper_bound
+        One rule is counted as ont test.
+        Failure: when one or more metric result(s) not provided, or when formula evaluated outside of upper/lower bounds.
+
+        @param rule: dict with metric name(+alias), formula, and required upper and lower bounds.
+        """
+        alias = dict()
+        for m in rule['Metrics']:
+            alias[m['Alias']] = m['Name']
+        lbv, ubv, t = self.get_bounds(rule['RangeLower'], rule['RangeUpper'], rule['ErrorThreshold'], alias, ridx=rule['RuleIndex'])
+        val, f = self.evaluate_formula(rule['Formula'], alias, ridx=rule['RuleIndex'])
+        if val == -1:
+            self.failtests['RelationshipTest']['Failed Tests'].append({'RuleIndex': rule['RuleIndex'], 'Description':f})
+        elif not self.check_bound(val, lbv, ubv, t):
+            lb = rule['RangeLower']
+            ub = rule['RangeUpper']
+            if isinstance(lb, str):
+                if lb in alias:
+                    lb = alias[lb]
+            if isinstance(ub, str):
+                if ub in alias:
+                    ub = alias[ub]
+            self.failtests['RelationshipTest']['Failed Tests'].append({'RuleIndex': rule['RuleIndex'], 'Formula':f,
+                                                                       'RangeLower': lb, 'LowerBoundValue': self.get_value(lb),
+                                                                       'RangeUpper': ub, 'UpperBoundValue':self.get_value(ub),
+                                                                       'ErrorThreshold': t, 'CollectedValue': val})
+        else:
+            self.passedcnt += 1
+            self.failtests['RelationshipTest']['Passed Tests'] += 1
+        self.totalcnt += 1
+        self.failtests['RelationshipTest']['Total Tests'] += 1
+
+        return
+
+
+    # Single Metric Test
+    def single_test(self, rule:dict):
+        """
+        Validate if the metrics are in the required value range.
+        eg. lower_bound <= metrics_value <= upper_bound
+        One metric is counted as one test in this type of test.
+        One rule may include one or more metrics.
+        Failure: when the metric value not provided or the value is outside the bounds.
+        This test updates self.total_cnt and records failed tests in self.failtest['SingleMetricTest'].
+
+        @param rule: dict with metrics to validate and the value range requirement
+        """
+        lbv, ubv, t = self.get_bounds(rule['RangeLower'], rule['RangeUpper'], rule['ErrorThreshold'])
+        metrics = rule['Metrics']
+        passcnt = 0
+        totalcnt = 0
+        faillist = list()
+        failures = dict()
+        rerun = list()
+        for m in metrics:
+            totalcnt += 1
+            result = self.get_value(m['Name'])
+            if len(result) > 0 and self.check_bound(result[0], lbv, ubv, t) or m['Name'] in self.skiplist:
+                passcnt += 1
+            else:
+                failures[m['Name']] = result
+                rerun.append(m['Name'])
+
+        if len(rerun) > 0 and len(rerun) < 20:
+            second_results = dict()
+            self.second_test(rerun, second_results)
+            for name, val in second_results.items():
+                if name not in failures: continue
+                if self.check_bound(val, lbv, ubv, t):
+                    passcnt += 1
+                    del failures[name]
+                else:
+                    failures[name] = val
+                    self.results[0][name] = val
+
+        self.totalcnt += totalcnt
+        self.passedcnt += passcnt
+        self.failtests['SingleMetricTest']['Total Tests'] += totalcnt
+        self.failtests['SingleMetricTest']['Passed Tests'] += passcnt
+        if len(failures.keys()) != 0:
+            faillist = [{'MetricName':name, 'CollectedValue':val} for name, val in failures.items()]
+            self.failtests['SingleMetricTest']['Failed Tests'].append({'RuleIndex':rule['RuleIndex'],
+                                                                       'RangeLower': rule['RangeLower'],
+                                                                       'RangeUpper': rule['RangeUpper'],
+                                                                       'ErrorThreshold':rule['ErrorThreshold'],
+                                                                       'Failure':faillist})
+
+        return
+
+    def create_report(self):
+        """
+        Create final report and write into a JSON file.
+        """
+        alldata = list()
+        for i in range(0, len(self.workloads)):
+            reportstas = {"Total Rule Count": self.alltotalcnt[i], "Passed Rule Count": self.allpassedcnt[i]}
+            data = {"Metric Validation Statistics": reportstas, "Tests in Category": self.allfailtests[i],
+                    "Errors":self.allerrlist[i]}
+            alldata.append({"Workload": self.workloads[i], "Report": data})
+
+        json_str = json.dumps(alldata, indent=4)
+        print("Test validation finished. Final report: ")
+        print(json_str)
+
+        if self.debug:
+            allres = [{"Workload": self.workloads[i], "Results": self.allresults[i]} for i in range(0, len(self.workloads))]
+            self.json_dump(allres, self.datafname)
+
+    def check_rule(self, testtype, metric_list):
+        """
+        Check if the rule uses metric(s) that not exist in current platform.
+
+        @param metric_list: list of metrics from the rule.
+        @return: False when find one metric out in Metric file. (This rule should not skipped.)
+                 True when all metrics used in the rule are found in Metric file.
+        """
+        if testtype == "RelationshipTest":
+            for m in metric_list:
+                if m['Name'] not in self.metrics:
+                    return False
+        return True
+
+    # Start of Collector and Converter
+    def convert(self, data: list, metricvalues:dict):
+        """
+        Convert collected metric data from the -j output to dict of {metric_name:value}.
+        """
+        for json_string in data:
+            try:
+                result =json.loads(json_string)
+                if "metric-unit" in result and result["metric-unit"] != "(null)" and result["metric-unit"] != "":
+                    name = result["metric-unit"].split("  ")[1] if len(result["metric-unit"].split("  ")) > 1 \
+                        else result["metric-unit"]
+                    metricvalues[name.lower()] = float(result["metric-value"])
+            except ValueError as error:
+                continue
+        return
+
+    def _run_perf(self, metric, workload: str):
+        tool = 'perf'
+        command = [tool, 'stat', '-j', '-M', f"{metric}", "-a"]
+        wl = workload.split()
+        command.extend(wl)
+        print(" ".join(command))
+        cmd = subprocess.run(command, stderr=subprocess.PIPE, encoding='utf-8')
+        data = [x+'}' for x in cmd.stderr.split('}\n') if x]
+        return data
+
+
+    def collect_perf(self, workload: str):
+        """
+        Collect metric data with "perf stat -M" on given workload with -a and -j.
+        """
+        self.results = dict()
+        print(f"Starting perf collection")
+        print(f"Long workload: {workload}")
+        collectlist = dict()
+        if self.collectlist != "":
+            collectlist[0] = {x for x in self.collectlist.split(",")}
+        else:
+            collectlist[0] = set(list(self.metrics))
+        # Create metric set for relationship rules
+        for rule in self.rules:
+            if rule["TestType"] == "RelationshipTest":
+                metrics = [m["Name"] for m in rule["Metrics"]]
+                if not any(m not in collectlist[0] for m in metrics):
+                    collectlist[rule["RuleIndex"]] = [",".join(list(set(metrics)))]
+
+        for idx, metrics in collectlist.items():
+            if idx == 0: wl = "true"
+            else: wl = workload
+            for metric in metrics:
+                data = self._run_perf(metric, wl)
+                if idx not in self.results: self.results[idx] = dict()
+                self.convert(data, self.results[idx])
+        return
+
+    def second_test(self, collectlist, second_results):
+        workload = self.workloads[self.wlidx]
+        for metric in collectlist:
+            data = self._run_perf(metric, workload)
+            self.convert(data, second_results)
+
+    # End of Collector and Converter
+
+    # Start of Rule Generator
+    def parse_perf_metrics(self):
+        """
+        Read and parse perf metric file:
+        1) find metrics with '1%' or '100%' as ScaleUnit for Percent check
+        2) create metric name list
+        """
+        command = ['perf', 'list', '-j', '--details', 'metrics']
+        cmd = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding='utf-8')
+        try:
+            data = json.loads(cmd.stdout)
+            for m in data:
+                if 'MetricName' not in m:
+                    print("Warning: no metric name")
+                    continue
+                name = m['MetricName'].lower()
+                self.metrics.add(name)
+                if 'ScaleUnit' in m and (m['ScaleUnit'] == '1%' or m['ScaleUnit'] == '100%'):
+                    self.pctgmetrics.add(name.lower())
+        except ValueError as error:
+            print(f"Error when parsing metric data")
+            sys.exit()
+
+        return
+
+    def remove_unsupported_rules(self, rules):
+        new_rules = []
+        for rule in rules:
+            add_rule = True
+            for m in rule["Metrics"]:
+                if m["Name"] in self.skiplist or m["Name"] not in self.metrics:
+                    add_rule = False
+                    break
+            if add_rule:
+                new_rules.append(rule)
+        return new_rules
+
+    def create_rules(self):
+        """
+        Create full rules which includes:
+        1) All the rules from the "relationshi_rules" file
+        2) SingleMetric rule for all the 'percent' metrics
+
+        Reindex all the rules to avoid repeated RuleIndex
+        """
+        data = self.read_json(self.rulefname)
+        rules = data['RelationshipRules']
+        self.skiplist = set([name.lower() for name in data['SkipList']])
+        self.rules = self.remove_unsupported_rules(rules)
+        pctgrule = {'RuleIndex':0,
+                    'TestType':'SingleMetricTest',
+                    'RangeLower':'0',
+                    'RangeUpper': '100',
+                    'ErrorThreshold': self.tolerance,
+                    'Description':'Metrics in percent unit have value with in [0, 100]',
+                    'Metrics': [{'Name': m.lower()} for m in self.pctgmetrics]}
+        self.rules.append(pctgrule)
+
+        # Re-index all rules to avoid repeated RuleIndex
+        idx = 1
+        for r in self.rules:
+            r['RuleIndex'] = idx
+            idx += 1
+
+        if self.debug:
+            #TODO: need to test and generate file name correctly
+            data = {'RelationshipRules':self.rules, 'SupportedMetrics': [{"MetricName": name} for name in self.metrics]}
+            self.json_dump(data, self.fullrulefname)
+
+        return
+    # End of Rule Generator
+
+    def _storewldata(self, key):
+        '''
+        Store all the data of one workload into the corresponding data structure for all workloads.
+        @param key: key to the dictionaries (index of self.workloads).
+        '''
+        self.allresults[key] = self.results
+        self.allignoremetrics[key] = self.ignoremetrics
+        self.allfailtests[key] = self.failtests
+        self.alltotalcnt[key] = self.totalcnt
+        self.allpassedcnt[key] = self.passedcnt
+        self.allerrlist[key] = self.errlist
+
+    #Initialize data structures before data validation of each workload
+    def _init_data(self):
+
+        testtypes = ['PositiveValueTest', 'RelationshipTest', 'SingleMetricTest']
+        self.results = dict()
+        self.ignoremetrics= set()
+        self.errlist = list()
+        self.failtests = {k:{'Total Tests':0, 'Passed Tests':0, 'Failed Tests':[]} for k in testtypes}
+        self.totalcnt = 0
+        self.passedcnt = 0
+
+    def test(self):
+        '''
+        The real entry point of the test framework.
+        This function loads the validation rule JSON file and Standard Metric file to create rules for
+        testing and namemap dictionaries.
+        It also reads in result JSON file for testing.
+
+        In the test process, it passes through each rule and launch correct test function bases on the
+        'TestType' field of the rule.
+
+        The final report is written into a JSON file.
+        '''
+        if not self.collectlist:
+            self.parse_perf_metrics()
+        self.create_rules()
+        for i in range(0, len(self.workloads)):
+            self.wlidx = i
+            self._init_data()
+            self.collect_perf(self.workloads[i])
+            # Run positive value test
+            self.pos_val_test()
+            for r in self.rules:
+                # skip rules that uses metrics not exist in this platform
+                testtype = r['TestType']
+                if not self.check_rule(testtype, r['Metrics']):
+                    continue
+                if  testtype == 'RelationshipTest':
+                    self.relationship_test(r)
+                elif testtype == 'SingleMetricTest':
+                    self.single_test(r)
+                else:
+                    print("Unsupported Test Type: ", testtype)
+                    self.errlist.append("Unsupported Test Type from rule: " + r['RuleIndex'])
+            self._storewldata(i)
+            print("Workload: ", self.workloads[i])
+            print("Total metrics collected: ", self.failtests['PositiveValueTest']['Total Tests'])
+            print("Non-negative metric count: ", self.failtests['PositiveValueTest']['Passed Tests'])
+            print("Total Test Count: ", self.totalcnt)
+            print("Passed Test Count: ", self.passedcnt)
+
+        self.create_report()
+        return sum(self.alltotalcnt.values()) != sum(self.allpassedcnt.values())
+# End of Class Validator
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Launch metric value validation")
+
+    parser.add_argument("-rule", help="Base validation rule file", required=True)
+    parser.add_argument("-output_dir", help="Path for validator output file, report file", required=True)
+    parser.add_argument("-debug", help="Debug run, save intermediate data to files", action="store_true", default=False)
+    parser.add_argument("-wl", help="Workload to run while data collection", default="true")
+    parser.add_argument("-m", help="Metric list to validate", default="")
+    args = parser.parse_args()
+    outpath = Path(args.output_dir)
+    reportf = Path.joinpath(outpath, 'perf_report.json')
+    fullrule = Path.joinpath(outpath, 'full_rule.json')
+    datafile = Path.joinpath(outpath, 'perf_data.json')
+
+    validator = Validator(args.rule, reportf, debug=args.debug,
+                        datafname=datafile, fullrulefname=fullrule, workload=args.wl,
+                        metrics=args.m)
+    ret = validator.test()
+
+    return ret
+
+
+if __name__ == "__main__":
+    import sys
+    sys.exit(main())
+
+
+