"""Scribe Uploader for Pytorch Benchmark V2 Data Currently supports data in pytest-benchmark format but can be extended. New fields can be added just by modifying the schema in this file, schema checking is only here to encourage reusing existing fields and avoiding typos. """ import argparse import time import multiprocessing import json import os import requests import subprocess from collections import defaultdict TORCHBENCH_V2_SCORE_SCHEMA = [ 'total', 'delta', 'cuda-train-overall', 'cuda-train-nlp', 'cuda-train-classification', 'cuda-train-segmentation', 'cuda-train-speech', 'cuda-train-recommendation', 'cuda-eval-overall', 'cuda-eval-nlp', 'cuda-eval-classification', 'cuda-eval-segmentation', 'cuda-eval-speech', 'cuda-eval-recommendation', 'cpu-train-overall', 'cpu-train-nlp', 'cpu-train-classification', 'cpu-train-segmentation', 'cpu-train-speech', 'cpu-train-recommendation', 'cpu-eval-overall', 'cpu-eval-nlp', 'cpu-eval-classification', 'cpu-eval-segmentation', 'cpu-eval-speech', 'cpu-eval-recommendation', ] def decorate_torchbench_score_schema(schema): return f"torchbench_score_{schema}" class ScribeUploader: def __init__(self, category): self.category = category def format_message(self, field_dict): assert 'time' in field_dict, "Missing required Scribe field 'time'" message = defaultdict(dict) for field, value in field_dict.items(): if field in self.schema['normal']: message['normal'][field] = str(value) elif field in self.schema['int']: message['int'][field] = int(value) elif field in self.schema['float']: message['float'][field] = float(value) else: raise ValueError("Field {} is not currently used, " "be intentional about adding new fields".format(field)) return message def _upload_intern(self, messages: list): for m in messages: json_str = json.dumps(m) cmd = ['scribe_cat', self.category, json_str] subprocess.run(cmd) def upload(self, messages: list): if os.environ.get('SCRIBE_INTERN'): return self._upload_intern(messages) access_token = os.environ.get("SCRIBE_GRAPHQL_ACCESS_TOKEN") if not access_token: raise ValueError("Can't find access token from environment variable") url = "https://graph.facebook.com/scribe_logs" r = requests.post( url, data={ "access_token": access_token, "logs": json.dumps( [ { "category": self.category, "message": json.dumps(message), "line_escape": False, } for message in messages ] ), }, ) print(r.text) r.raise_for_status() class PytorchBenchmarkUploader(ScribeUploader): def __init__(self): super().__init__('perfpipe_pytorch_benchmarks') self.schema = { 'int': [ 'time', 'rounds', ], 'normal': [ 'benchmark_group', 'benchmark_name', 'benchmark_class', 'benchmark_time', 'git_repo', 'git_commit_id', 'git_branch', 'git_commit_time', 'git_dirty', 'pytorch_version', 'python_version', 'torchtext_version', 'torchvision_version', 'machine_kernel', 'machine_processor', 'machine_hostname', 'github_run_id', 'torchbench_score_version', ], 'float': [ 'stddev', 'min', 'median', 'max', 'mean', 'runtime', ] } # Append the TorchBench score schema self.schema['float'].extend(list(map(decorate_torchbench_score_schema, TORCHBENCH_V2_SCORE_SCHEMA))) def post_pytest_benchmarks(self, pytest_json, max_data_upload=100): machine_info = pytest_json['machine_info'] commit_info = pytest_json['commit_info'] upload_time = int(time.time()) messages = [] for b in pytest_json['benchmarks']: base_msg = { "time": upload_time, "benchmark_group": b['group'], "benchmark_name": b['name'], "benchmark_class": b['fullname'], "benchmark_time": pytest_json['datetime'], "git_repo": commit_info['project'], "git_commit_id": commit_info['id'], "git_branch": commit_info['branch'], "git_commit_time": commit_info['time'], "git_dirty": commit_info['dirty'], "pytorch_version": machine_info.get('pytorch_version', None), "torchtext_version": machine_info.get('torchtext_version', None), "torchvision_version": machine_info.get('torchvision_version', None), "python_version": machine_info['python_implementation_version'], "machine_kernel": machine_info['release'], "machine_processor": machine_info['processor'], "machine_hostname": machine_info['node'], "github_run_id": machine_info.get('github_run_id', None), "torchbench_score_version": machine_info.get('torchbench_score_version', None), } stats_msg = {"stddev": b['stats']['stddev'], "rounds": b['stats']['rounds'], "min": b['stats']['min'], "median": b['stats']['median'], "max": b['stats']['max'], "mean": b['stats']['mean'], } stats_msg.update(base_msg) messages.append(self.format_message(stats_msg)) if 'data' in b['stats']: for runtime in b['stats']['data'][:max_data_upload]: runtime_msg = {"runtime": runtime} runtime_msg.update(base_msg) messages.append(self.format_message(runtime_msg)) self.upload(messages) def post_torchbench_score(self, pytest_json, score): machine_info = pytest_json['machine_info'] commit_info = pytest_json['commit_info'] upload_time = int(time.time()) scribe_message = { "time": upload_time, "benchmark_time": pytest_json['datetime'], "git_repo": commit_info['project'], "git_commit_id": commit_info['id'], "git_branch": commit_info['branch'], "git_commit_time": commit_info['time'], "git_dirty": commit_info['dirty'], "pytorch_version": machine_info.get('pytorch_version', None), "torchtext_version": machine_info.get('torchtext_version', None), "torchvision_version": machine_info.get('torchvision_version', None), "python_version": machine_info['python_implementation_version'], "machine_kernel": machine_info['release'], "machine_processor": machine_info['processor'], "machine_hostname": machine_info['node'], "github_run_id": machine_info.get('github_run_id', None), "torchbench_score_version": machine_info.get('torchbench_score_version', None), } for s in TORCHBENCH_V2_SCORE_SCHEMA: decorated_schema = decorate_torchbench_score_schema(s) if s == "total" or s == "delta": scribe_message[decorated_schema] = score["score"][s] else: scribe_message[decorated_schema] = score["score"]["domain"][s] m = self.format_message(scribe_message) self.upload([m]) if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--pytest_bench_json", required=True, type=argparse.FileType('r'), help='Upload json data formatted by pytest-benchmark module') parser.add_argument("--torchbench_score_file", required=True, type=argparse.FileType('r'), help="torchbench score file to include") args = parser.parse_args() # Result sanity check json_name = os.path.basename(args.pytest_bench_json.name) json_score = json.load(args.torchbench_score_file) score_data = None for data in json_score: if os.path.basename(data["file"]) == json_name: score_data = data assert score_data, f"Can't find {json_name} score in {args.torchbench_score_file}. Stop." benchmark_uploader = PytorchBenchmarkUploader() json_data = json.load(args.pytest_bench_json) benchmark_uploader.post_pytest_benchmarks(json_data) benchmark_uploader.post_torchbench_score(json_data, score_data)