| """ |
| Generate a torchbench test report from a file containing the PR body. |
| Currently, only supports running tests on specified model names |
| |
| Testing environment: |
| - Intel Xeon 8259CL @ 2.50 GHz, 24 Cores with disabled Turbo and HT |
| - Nvidia Tesla T4 |
| - Nvidia Driver 450.51.06 |
| - Python 3.7 |
| - CUDA 10.2 |
| """ |
| # Known issues: |
| # 1. Does not reuse the build artifact in other CI workflows |
| # 2. CI jobs are serialized because there is only one worker |
| import os |
| import git # type: ignore[import] |
| import pathlib |
| import argparse |
| import subprocess |
| |
| from typing import List |
| |
| TORCHBENCH_CONFIG_NAME = "config.yaml" |
| MAGIC_PREFIX = "RUN_TORCHBENCH:" |
| MAGIC_TORCHBENCH_PREFIX = "TORCHBENCH_BRANCH:" |
| ABTEST_CONFIG_TEMPLATE = """# This config is automatically generated by run_torchbench.py |
| start: {control} |
| end: {treatment} |
| threshold: 100 |
| direction: decrease |
| timeout: 720 |
| tests:""" |
| |
| def gen_abtest_config(control: str, treatment: str, models: List[str]) -> str: |
| d = {} |
| d["control"] = control |
| d["treatment"] = treatment |
| config = ABTEST_CONFIG_TEMPLATE.format(**d) |
| if models == ["ALL"]: |
| return config + "\n" |
| for model in models: |
| config = f"{config}\n - {model}" |
| config = config + "\n" |
| return config |
| |
| def setup_gha_env(name: str, val: str) -> None: |
| fname = os.environ["GITHUB_ENV"] |
| content = f"{name}={val}\n" |
| with open(fname, "a") as fo: |
| fo.write(content) |
| |
| def find_current_branch(repo_path: str) -> str: |
| repo = git.Repo(repo_path) |
| name: str = repo.active_branch.name |
| return name |
| |
| def deploy_torchbench_config(output_dir: str, config: str) -> None: |
| # Create test dir if needed |
| pathlib.Path(output_dir).mkdir(exist_ok=True) |
| # TorchBench config file name |
| config_path = os.path.join(output_dir, TORCHBENCH_CONFIG_NAME) |
| with open(config_path, "w") as fp: |
| fp.write(config) |
| |
| def extract_models_from_pr(torchbench_path: str, prbody_file: str) -> List[str]: |
| model_list = [] |
| with open(prbody_file, "r") as pf: |
| lines = map(lambda x: x.strip(), pf.read().splitlines()) |
| magic_lines = list(filter(lambda x: x.startswith(MAGIC_PREFIX), lines)) |
| if magic_lines: |
| # Only the first magic line will be recognized. |
| model_list = list(map(lambda x: x.strip(), magic_lines[0][len(MAGIC_PREFIX):].split(","))) |
| # Shortcut: if model_list is ["ALL"], run all the tests |
| if model_list == ["ALL"]: |
| return model_list |
| # Sanity check: make sure all the user specified models exist in torchbench repository |
| benchmark_path = os.path.join(torchbench_path, "torchbenchmark", "models") |
| full_model_list = [model for model in os.listdir(benchmark_path) if os.path.isdir(os.path.join(benchmark_path, model))] |
| for m in model_list: |
| if m not in full_model_list: |
| print(f"The model {m} you specified does not exist in TorchBench suite. Please double check.") |
| return [] |
| return model_list |
| |
| def find_torchbench_branch(prbody_file: str) -> str: |
| branch_name: str = "" |
| with open(prbody_file, "r") as pf: |
| lines = map(lambda x: x.strip(), pf.read().splitlines()) |
| magic_lines = list(filter(lambda x: x.startswith(MAGIC_TORCHBENCH_PREFIX), lines)) |
| if magic_lines: |
| # Only the first magic line will be recognized. |
| branch_name = magic_lines[0][len(MAGIC_TORCHBENCH_PREFIX):].strip() |
| # If not specified, use main as the default branch |
| if not branch_name: |
| branch_name = "main" |
| return branch_name |
| |
| def run_torchbench(pytorch_path: str, torchbench_path: str, output_dir: str) -> None: |
| # Copy system environment so that we will not override |
| env = dict(os.environ) |
| command = ["python", "bisection.py", "--work-dir", output_dir, |
| "--pytorch-src", pytorch_path, "--torchbench-src", torchbench_path, |
| "--config", os.path.join(output_dir, "config.yaml"), |
| "--output", os.path.join(output_dir, "result.txt")] |
| subprocess.check_call(command, cwd=torchbench_path, env=env) |
| |
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser(description='Run TorchBench tests based on PR') |
| parser.add_argument('--pr-body', required=True, help="The file that contains body of a Pull Request") |
| |
| subparsers = parser.add_subparsers(dest='command') |
| # parser for setup the torchbench branch name env |
| branch_parser = subparsers.add_parser("set-torchbench-branch") |
| # parser to run the torchbench branch |
| run_parser = subparsers.add_parser("run") |
| run_parser.add_argument('--pr-num', required=True, type=str, help="The Pull Request number") |
| run_parser.add_argument('--pr-base-sha', required=True, type=str, help="The Pull Request base hash") |
| run_parser.add_argument('--pr-head-sha', required=True, type=str, help="The Pull Request head hash") |
| run_parser.add_argument('--pytorch-path', required=True, type=str, help="Path to pytorch repository") |
| run_parser.add_argument('--torchbench-path', required=True, type=str, help="Path to TorchBench repository") |
| args = parser.parse_args() |
| |
| if args.command == 'set-torchbench-branch': |
| branch_name = find_torchbench_branch(args.pr_body) |
| # env name: "TORCHBENCH_BRANCH" |
| setup_gha_env(MAGIC_TORCHBENCH_PREFIX[:-1], branch_name) |
| elif args.command == 'run': |
| output_dir: str = os.path.join(os.environ["HOME"], ".torchbench", "bisection", f"pr{args.pr_num}") |
| # Identify the specified models and verify the input |
| models = extract_models_from_pr(args.torchbench_path, args.pr_body) |
| if not models: |
| print("Can't parse the model filter from the pr body. Currently we only support allow-list.") |
| exit(-1) |
| # Assert the current branch in args.torchbench_path is the same as the one specified in pr body |
| branch_name = find_torchbench_branch(args.pr_body) |
| current_branch = find_current_branch(args.torchbench_path) |
| assert branch_name == current_branch, f"Torchbench repo {args.torchbench_path} is on branch {current_branch}, \ |
| but user specified to run on branch {branch_name}." |
| print(f"Ready to run TorchBench with benchmark. Result will be saved in the directory: {output_dir}.") |
| # Run TorchBench with the generated config |
| torchbench_config = gen_abtest_config(args.pr_base_sha, args.pr_head_sha, models) |
| deploy_torchbench_config(output_dir, torchbench_config) |
| run_torchbench(pytorch_path=args.pytorch_path, torchbench_path=args.torchbench_path, output_dir=output_dir) |
| else: |
| print(f"The command {args.command} is not supported.") |
| exit(-1) |
