From 43f93813a9cff5e610799d0283de8baed0c7b361 Mon Sep 17 00:00:00 2001 From: mehmet yusufoglu Date: Fri, 19 Jul 2024 17:34:13 +0200 Subject: [PATCH] update script and add test input and output --- script/benchmark/benchmarkXml2Json.py | 149 +- .../BabelStreamBenchmarkResults.json | 1582 +++++++++++++++++ .../BabelStreamBenchmarkResults.xml | 223 +++ 3 files changed, 1919 insertions(+), 35 deletions(-) create mode 100644 script/benchmark/test-files/BabelStreamBenchmarkResults.json create mode 100644 script/benchmark/test-files/BabelStreamBenchmarkResults.xml diff --git a/script/benchmark/benchmarkXml2Json.py b/script/benchmark/benchmarkXml2Json.py index 19ee0d904c7..073b510d345 100644 --- a/script/benchmark/benchmarkXml2Json.py +++ b/script/benchmark/benchmarkXml2Json.py @@ -1,65 +1,141 @@ -import xml.etree.ElementTree as ET +#!/usr/bin/env python3 + +import os +import sys import json +import hashlib +import xml.etree.ElementTree as ET import html -import sys -import os -import re +# Function to parse the content of a node into a dictionary +# This is needed because Catch2 INFO macro is not printed if the test case passes, only WARNING is printed def parse_warning_node(warning_text): warning_dict = {} for line in warning_text.strip().split('\n'): if ':' in line: field_name, value = line.split(':', 1) - warning_dict[field_name.strip()] = convert_to_number(value.strip()) + warning_dict[field_name.strip()] = value.strip() return warning_dict -def convert_to_number(value): - # Check if the value is an integer - if re.match(r'^-?\d+$', value): - return int(value) - # Check if the value is a real number - elif re.match(r'^-?\d*\.\d+$', value): - return float(value) - # Return the value as a string if it's neither an integer nor a real number - return value - +# Function to recursively convert XML elements into a dictionary def xml_to_dict(element): node_dict = {} - # Directly add attributes without '@' + # Add XML attributes directly to the dictionary for key, value in element.attrib.items(): - node_dict[key] = convert_to_number(value) + node_dict[key] = html.unescape(value) - if element.text and element.text.strip(): - node_dict[element.tag] = convert_to_number(html.unescape(element.text.strip())) + # Add text content of the element if it exists + text = element.text.strip() if element.text else '' + if text: + node_dict['text'] = html.unescape(text) special_tags = {'mean', 'standardDeviation', 'outliers'} + # Process each child element recursively for child in list(element): child_dict = xml_to_dict(child) if child.tag == 'Warning': warning_dict = parse_warning_node(child.text) node_dict['MetaBenchmarkTestData'] = warning_dict elif child.tag in special_tags: - node_dict[child.tag] = child_dict[child.tag] + if child.tag not in node_dict: + node_dict[child.tag] = {} + node_dict[child.tag].update(child_dict) else: if child.tag not in node_dict: node_dict[child.tag] = [] node_dict[child.tag].append(child_dict) - return {element.tag: node_dict} + # Convert single-item lists to single objects + for key, value in node_dict.items(): + if isinstance(value, list) and len(value) == 1: + node_dict[key] = value[0] -def consolidate_dict(d): - if isinstance(d, dict): - for key in d: - if isinstance(d[key], list) and len(d[key]) == 1: - d[key] = d[key][0] - consolidate_dict(d[key]) + return node_dict +# Function to convert the XML string into a JSON-compatible dictionary def xml_to_json(xml_string): root = ET.fromstring(xml_string) - xml_dict = xml_to_dict(root) - consolidate_dict(xml_dict) - return json.dumps(xml_dict, indent=4) + root_dict = xml_to_dict(root) + + # Extract and structure TestCase elements + test_cases = root_dict.pop('TestCase', []) + + if not isinstance(test_cases, list): + test_cases = [test_cases] + + # Create the final dictionary with the root tag and TestCase entries + final_dict = { + root.tag: root_dict, + 'TestCase': test_cases + } + + return final_dict + +# Function to transform the JSON data into the desired format +def transform(json_data): + output_object = [] + + if 'Catch2TestRun' not in json_data: + print("Error: 'Catch2TestRun' not found in input JSON") + return [] + + # Extract general metadata + o1 = json_data['Catch2TestRun'] + + # Create metadata and environment subobjects + meta = { + 'creator': "unknown", + 'datetime': "unknown", + 'hostname': "unknown", + 'moreinterestingmetadata': "unknown" + } + + env = { + 'type': 'Catch2TestRun', + 'kind': o1.get('name', 'unknown'), + 'rng-seed': o1.get('rng-seed', 'unknown'), + 'catch2-version': o1.get('catch2-version', 'unknown'), + 'OverallResults': o1.get('OverallResults', {}), + 'OverallResultsCases': o1.get('OverallResultsCases', {}) + } + + # Process each TestCase if it exists + test_cases = json_data.get('TestCase', []) + if not isinstance(test_cases, list): + test_cases = [test_cases] + + for tc in test_cases: + # Ensure BenchmarkResults is a list + benchmark_results = tc.get('BenchmarkResults', []) + if not isinstance(benchmark_results, list): + benchmark_results = [benchmark_results] + + for m in benchmark_results: + out = {} + out['id'] = "" + out['meta'] = meta + out['env'] = env + out['TestCase'] = { + 'name': tc.get('name', 'unknown'), + 'tags': tc.get('tags', 'unknown'), + 'filename': tc.get('filename', 'unknown'), + 'line': tc.get('line', 'unknown'), + 'OverallResult': tc.get('OverallResult', {}), + 'MetaBenchmarkTestData': tc.get('MetaBenchmarkTestData', {}) + } + + # Add benchmark result details + for k in m: + out[k] = m[k] + + # Insert md5sum as unique id of the dataset + hash = hashlib.md5(json.dumps(out, sort_keys=True).encode('utf-8')).hexdigest() + out['id'] = hash + + output_object.append(out) + + return output_object if __name__ == "__main__": if len(sys.argv) != 3: @@ -87,11 +163,14 @@ def xml_to_json(xml_string): xml_content = xml_file.read() # Convert XML to JSON - json_output = xml_to_json(xml_content) + json_data = xml_to_json(xml_content) + + # Transform JSON to desired format + transformed_data = transform(json_data) - # Save the JSON output to the output file - with open(output_file, 'w') as json_file: - json_file.write(json_output) + # Save the transformed JSON output to the output file + with open(output_file, 'w', encoding='utf-8') as json_file: + json.dump(transformed_data, json_file, indent=4) - print(f"XML content has been converted to JSON and saved as '{output_file}'") + print(f"XML content has been converted to JSON and transformed, saved as '{output_file}'") diff --git a/script/benchmark/test-files/BabelStreamBenchmarkResults.json b/script/benchmark/test-files/BabelStreamBenchmarkResults.json new file mode 100644 index 00000000000..21ae3e8f557 --- /dev/null +++ b/script/benchmark/test-files/BabelStreamBenchmarkResults.json @@ -0,0 +1,1582 @@ +[ + { + "id": "f7e25eb8d1b146e52d271039f989483d", + "meta": { + "creator": "unknown", + "datetime": "unknown", + "hostname": "unknown", + "moreinterestingmetadata": "unknown" + }, + "env": { + "type": "Catch2TestRun", + "kind": "babelstream", + "rng-seed": "1458339043", + "catch2-version": "3.5.2", + "OverallResults": { + "successes": "14", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + }, + "OverallResultsCases": { + "successes": "8", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + } + }, + "TestCase": { + "name": "TEST: Babelstream Bechmark DotKernel Test-Float - TestAccs1D - 1", + "tags": "[benchmark-test]", + "filename": "/home/yusufo81/projects/alpaka-dir/alpaka/benchmarks/babelstream/src/babelStreamDotKernelTest.cpp", + "line": "188", + "OverallResult": { + "success": "true", + "skips": "0" + }, + "MetaBenchmarkTestData": { + "TimeStamp": "2024-07-19 16:39:38", + "DataArraySize": "1048576", + "DataType": "single-precision", + "Work Division": "{gridBlockExtent: (1024), blockThreadExtent: (1024), threadElemExtent: (1)}", + "AcceleratorType": "AccGpuCudaRt<1,unsigned int>", + "TimeUnitForXMLReport": "Nano Seconds" + } + }, + "name": "DotKernel", + "samples": "2", + "resamples": "100000", + "iterations": "1", + "clockResolution": "18.0074", + "estimatedDuration": "309408", + "mean": { + "value": "171410", + "lowerBound": "149170", + "upperBound": "171410", + "ci": "0.95" + }, + "standardDeviation": { + "value": "22240.5", + "lowerBound": "0", + "upperBound": "0", + "ci": "0.95" + }, + "outliers": { + "variance": "0.383985", + "lowMild": "0", + "lowSevere": "0", + "highMild": "0", + "highSevere": "0" + } + }, + { + "id": "65bce41c65b0fa85401690a9e96e3998", + "meta": { + "creator": "unknown", + "datetime": "unknown", + "hostname": "unknown", + "moreinterestingmetadata": "unknown" + }, + "env": { + "type": "Catch2TestRun", + "kind": "babelstream", + "rng-seed": "1458339043", + "catch2-version": "3.5.2", + "OverallResults": { + "successes": "14", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + }, + "OverallResultsCases": { + "successes": "8", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + } + }, + "TestCase": { + "name": "TEST: Babelstream Bechmark DotKernel Test-Double - TestAccs1D - 1", + "tags": "[benchmark-test]", + "filename": "/home/yusufo81/projects/alpaka-dir/alpaka/benchmarks/babelstream/src/babelStreamDotKernelTest.cpp", + "line": "198", + "OverallResult": { + "success": "true", + "skips": "0" + }, + "MetaBenchmarkTestData": { + "TimeStamp": "2024-07-19 16:39:41", + "DataArraySize": "1048576", + "DataType": "double-precision", + "Work Division": "{gridBlockExtent: (1024), blockThreadExtent: (1024), threadElemExtent: (1)}", + "AcceleratorType": "AccGpuCudaRt<1,unsigned int>", + "TimeUnitForXMLReport": "Nano Seconds" + } + }, + "name": "DotKernel", + "samples": "2", + "resamples": "100000", + "iterations": "1", + "clockResolution": "18.0074", + "estimatedDuration": "659236", + "mean": { + "value": "352676", + "lowerBound": "329799", + "upperBound": "352676", + "ci": "0.95" + }, + "standardDeviation": { + "value": "22877.5", + "lowerBound": "0", + "upperBound": "0", + "ci": "0.95" + }, + "outliers": { + "variance": "0.25", + "lowMild": "0", + "lowSevere": "0", + "highMild": "0", + "highSevere": "0" + } + }, + { + "id": "5b88e2275115817a27590b8d3b4dc950", + "meta": { + "creator": "unknown", + "datetime": "unknown", + "hostname": "unknown", + "moreinterestingmetadata": "unknown" + }, + "env": { + "type": "Catch2TestRun", + "kind": "babelstream", + "rng-seed": "1458339043", + "catch2-version": "3.5.2", + "OverallResults": { + "successes": "14", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + }, + "OverallResultsCases": { + "successes": "8", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + } + }, + "TestCase": { + "name": "TEST: Babelstream Copy,Add,Mull,Triad Kernels-Float - TestAccs1D - 0", + "tags": "[benchmark-test]", + "filename": "/home/yusufo81/projects/alpaka-dir/alpaka/benchmarks/babelstream/src/babelStreamMainTest.cpp", + "line": "332", + "OverallResult": { + "success": "true", + "skips": "0" + }, + "MetaBenchmarkTestData": { + "TimeStamp": "2024-07-19 16:39:42", + "DataArraySize": "1048576", + "DataType": "single-precision", + "Work Division": "{gridBlockExtent: (1048576), blockThreadExtent: (1), threadElemExtent: (1)}", + "AcceleratorType": "AccCpuSerial<1,unsigned int>", + "TimeUnitForXMLReport": "Nano Seconds", + "KernelNames": "InitKernel,CopyKernel,ScalingKernel,AddKernel,TriadKernel", + "KernelDataUsageValues(KByte)": "8192,8192,8192,12288,12288" + } + }, + "name": "InitKernel", + "samples": "2", + "resamples": "100000", + "iterations": "1", + "clockResolution": "18.0074", + "estimatedDuration": "233802", + "mean": { + "value": "333750", + "lowerBound": "236143", + "upperBound": "333750", + "ci": "0.95" + }, + "standardDeviation": { + "value": "97607", + "lowerBound": "0", + "upperBound": "0", + "ci": "0.95" + }, + "outliers": { + "variance": "0.477164", + "lowMild": "0", + "lowSevere": "0", + "highMild": "0", + "highSevere": "0" + } + }, + { + "id": "733ddd1dc133ea8ce845cd1838ca957d", + "meta": { + "creator": "unknown", + "datetime": "unknown", + "hostname": "unknown", + "moreinterestingmetadata": "unknown" + }, + "env": { + "type": "Catch2TestRun", + "kind": "babelstream", + "rng-seed": "1458339043", + "catch2-version": "3.5.2", + "OverallResults": { + "successes": "14", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + }, + "OverallResultsCases": { + "successes": "8", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + } + }, + "TestCase": { + "name": "TEST: Babelstream Copy,Add,Mull,Triad Kernels-Float - TestAccs1D - 0", + "tags": "[benchmark-test]", + "filename": "/home/yusufo81/projects/alpaka-dir/alpaka/benchmarks/babelstream/src/babelStreamMainTest.cpp", + "line": "332", + "OverallResult": { + "success": "true", + "skips": "0" + }, + "MetaBenchmarkTestData": { + "TimeStamp": "2024-07-19 16:39:42", + "DataArraySize": "1048576", + "DataType": "single-precision", + "Work Division": "{gridBlockExtent: (1048576), blockThreadExtent: (1), threadElemExtent: (1)}", + "AcceleratorType": "AccCpuSerial<1,unsigned int>", + "TimeUnitForXMLReport": "Nano Seconds", + "KernelNames": "InitKernel,CopyKernel,ScalingKernel,AddKernel,TriadKernel", + "KernelDataUsageValues(KByte)": "8192,8192,8192,12288,12288" + } + }, + "name": "CopyKernel", + "samples": "2", + "resamples": "100000", + "iterations": "1", + "clockResolution": "18.0074", + "estimatedDuration": "343392", + "mean": { + "value": "438985", + "lowerBound": "326398", + "upperBound": "438985", + "ci": "0.95" + }, + "standardDeviation": { + "value": "112587", + "lowerBound": "0", + "upperBound": "0", + "ci": "0.95" + }, + "outliers": { + "variance": "0.470307", + "lowMild": "0", + "lowSevere": "0", + "highMild": "0", + "highSevere": "0" + } + }, + { + "id": "12c8e78c88d11b35f5519e9cb0aeea95", + "meta": { + "creator": "unknown", + "datetime": "unknown", + "hostname": "unknown", + "moreinterestingmetadata": "unknown" + }, + "env": { + "type": "Catch2TestRun", + "kind": "babelstream", + "rng-seed": "1458339043", + "catch2-version": "3.5.2", + "OverallResults": { + "successes": "14", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + }, + "OverallResultsCases": { + "successes": "8", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + } + }, + "TestCase": { + "name": "TEST: Babelstream Copy,Add,Mull,Triad Kernels-Float - TestAccs1D - 0", + "tags": "[benchmark-test]", + "filename": "/home/yusufo81/projects/alpaka-dir/alpaka/benchmarks/babelstream/src/babelStreamMainTest.cpp", + "line": "332", + "OverallResult": { + "success": "true", + "skips": "0" + }, + "MetaBenchmarkTestData": { + "TimeStamp": "2024-07-19 16:39:42", + "DataArraySize": "1048576", + "DataType": "single-precision", + "Work Division": "{gridBlockExtent: (1048576), blockThreadExtent: (1), threadElemExtent: (1)}", + "AcceleratorType": "AccCpuSerial<1,unsigned int>", + "TimeUnitForXMLReport": "Nano Seconds", + "KernelNames": "InitKernel,CopyKernel,ScalingKernel,AddKernel,TriadKernel", + "KernelDataUsageValues(KByte)": "8192,8192,8192,12288,12288" + } + }, + "name": "ScalingKernel", + "samples": "2", + "resamples": "100000", + "iterations": "1", + "clockResolution": "18.0074", + "estimatedDuration": "345536", + "mean": { + "value": "489504", + "lowerBound": "435460", + "upperBound": "489504", + "ci": "0.95" + }, + "standardDeviation": { + "value": "54043.5", + "lowerBound": "0", + "upperBound": "0", + "ci": "0.95" + }, + "outliers": { + "variance": "0.339766", + "lowMild": "0", + "lowSevere": "0", + "highMild": "0", + "highSevere": "0" + } + }, + { + "id": "d392dc5259b29c25300993ed2f67b241", + "meta": { + "creator": "unknown", + "datetime": "unknown", + "hostname": "unknown", + "moreinterestingmetadata": "unknown" + }, + "env": { + "type": "Catch2TestRun", + "kind": "babelstream", + "rng-seed": "1458339043", + "catch2-version": "3.5.2", + "OverallResults": { + "successes": "14", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + }, + "OverallResultsCases": { + "successes": "8", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + } + }, + "TestCase": { + "name": "TEST: Babelstream Copy,Add,Mull,Triad Kernels-Float - TestAccs1D - 0", + "tags": "[benchmark-test]", + "filename": "/home/yusufo81/projects/alpaka-dir/alpaka/benchmarks/babelstream/src/babelStreamMainTest.cpp", + "line": "332", + "OverallResult": { + "success": "true", + "skips": "0" + }, + "MetaBenchmarkTestData": { + "TimeStamp": "2024-07-19 16:39:42", + "DataArraySize": "1048576", + "DataType": "single-precision", + "Work Division": "{gridBlockExtent: (1048576), blockThreadExtent: (1), threadElemExtent: (1)}", + "AcceleratorType": "AccCpuSerial<1,unsigned int>", + "TimeUnitForXMLReport": "Nano Seconds", + "KernelNames": "InitKernel,CopyKernel,ScalingKernel,AddKernel,TriadKernel", + "KernelDataUsageValues(KByte)": "8192,8192,8192,12288,12288" + } + }, + "name": "AddKernel", + "samples": "2", + "resamples": "100000", + "iterations": "1", + "clockResolution": "18.0074", + "estimatedDuration": "494478", + "mean": { + "value": "660886", + "lowerBound": "580261", + "upperBound": "660886", + "ci": "0.95" + }, + "standardDeviation": { + "value": "80624.5", + "lowerBound": "0", + "upperBound": "0", + "ci": "0.95" + }, + "outliers": { + "variance": "0.368765", + "lowMild": "0", + "lowSevere": "0", + "highMild": "0", + "highSevere": "0" + } + }, + { + "id": "96394b73f36092b42574afb977615cfd", + "meta": { + "creator": "unknown", + "datetime": "unknown", + "hostname": "unknown", + "moreinterestingmetadata": "unknown" + }, + "env": { + "type": "Catch2TestRun", + "kind": "babelstream", + "rng-seed": "1458339043", + "catch2-version": "3.5.2", + "OverallResults": { + "successes": "14", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + }, + "OverallResultsCases": { + "successes": "8", + "failures": "0", + "expectedFailures": "0", + "skips": "0" + } + }, + "TestCase": { + "name": "TEST: Babelstream Copy,Add,Mull,Triad Kernels-Float - 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