.github/scripts/run_torchbench.py - platform/external/pytorch - Git at Google


            
            """

            
            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 470.82.01

            
            - Python 3.8

            
            - CUDA 11.3

            
            """

            
            # 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 boto3  # type: ignore[import]

            
            import git  # type: ignore[import]

            
            import pathlib

            
            import argparse

            
            import subprocess

            
            from pathlib import Path


            from typing import List, Tuple


            TORCHBENCH_CONFIG_NAME = "config.yaml"

            
            TORCHBENCH_USERBENCHMARK_CONFIG_NAME = "ub-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:"""

            
            S3_BUCKET = "ossci-metrics"

            
            S3_PREFIX = "torchbench-pr-test"

            
            S3_URL_BASE = f"https://{S3_BUCKET}.s3.amazonaws.com/"


            class S3Client:

            
                def __init__(self, bucket: str = S3_BUCKET, prefix: str = S3_PREFIX):

            
                    self.s3 = boto3.client('s3')

            
                    self.resource = boto3.resource('s3')

            
                    self.bucket = bucket

            
                    self.prefix = prefix


                def upload_file(self, file_path: Path, filekey_prefix: str) -> None:

            
                    assert file_path.is_file(), f"Specified file path {file_path} does not exist or not file."

            
                    file_name = file_path.name

            
                    s3_key = f"{self.prefix}/{filekey_prefix}/{file_name}"

            
                    print(f"Uploading file {file_name} to S3 with key: {s3_key}")

            
                    self.s3.upload_file(str(file_path), self.bucket, s3_key)

            
                    # output the result URL

            
                    print(f"Uploaded the result file {file_name} to {S3_URL_BASE}{s3_key}")


            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, config_name: str = TORCHBENCH_CONFIG_NAME) -> 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, config_name)

            
                with open(config_path, "w") as fp:

            
                    fp.write(config)


            def get_valid_models(torchbench_path: str) -> List[str]:

            
                benchmark_path = os.path.join(torchbench_path, "torchbenchmark", "models")

            
                valid_models = [model for model in os.listdir(benchmark_path) if os.path.isdir(os.path.join(benchmark_path, model))]

            
                return valid_models


            def get_valid_userbenchmarks(torchbench_path: str) -> List[str]:

            
                def is_valid_ub_dir(ub_path: str) -> bool:

            
                    return os.path.isdir(ub_path) and os.path.exists(os.path.join(ub_path, "__init__.py"))

            
                ub_path = os.path.join(os.path.abspath(torchbench_path), "userbenchmark")

            
                ubs = list(filter(is_valid_ub_dir, [os.path.join(ub_path, ubdir) for ubdir in os.listdir(ub_path)]))

            
                valid_ubs = list(map(lambda x: os.path.basename(x), ubs))

            
                return valid_ubs


            def extract_models_from_pr(torchbench_path: str, prbody_file: str) -> Tuple[List[str], List[str]]:

            
                model_list = []

            
                userbenchmark_list = []

            
                pr_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.

            
                        pr_list = list(map(lambda x: x.strip(), magic_lines[0][len(MAGIC_PREFIX):].split(",")))

            
                valid_models = get_valid_models(torchbench_path)

            
                valid_ubs = get_valid_userbenchmarks(torchbench_path)

            
                for pr_bm in pr_list:

            
                    if pr_bm in valid_models or pr_bm == "ALL":

            
                        model_list.append(pr_bm)

            
                    elif pr_bm in valid_ubs:

            
                        userbenchmark_list.append(pr_bm)

            
                    else:

            
                        print(f"The model or benchmark {pr_bm} you specified does not exist in TorchBench suite. Please double check.")

            
                        exit(-1)

            
                # Shortcut: if pr_list is ["ALL"], run all the model tests

            
                if "ALL" in model_list:

            
                    model_list = ["ALL"]

            
                return model_list, userbenchmark_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, TORCHBENCH_CONFIG_NAME),

            
                           "--output", os.path.join(output_dir, "result.txt")]

            
                print(f"Running torchbench command: {command}")

            
                subprocess.check_call(command, cwd=torchbench_path, env=env)


            def run_userbenchmarks(pytorch_path: str, torchbench_path: str, base_sha: str, head_sha: str,

            
                                   userbenchmark: str, output_dir: str) -> None:

            
                # Copy system environment so that we will not override

            
                env = dict(os.environ)

            
                command = ["python", "./.github/scripts/abtest.py",

            
                           "--pytorch-repo", pytorch_path,

            
                           "--base", base_sha,

            
                           "--head", head_sha,

            
                           "--userbenchmark", userbenchmark,

            
                           "--output-dir", output_dir]

            
                print(f"Running torchbench userbenchmark command: {command}")

            
                subprocess.check_call(command, cwd=torchbench_path, env=env)


            def process_upload_s3(result_dir: str) -> None:

            
                # validate result directory

            
                result_dir_path = Path(result_dir)

            
                assert result_dir_path.exists(), f"Specified result directory {result_dir} doesn't exist."

            
                # upload all files to S3 bucket oss-ci-metrics

            
                files = [x for x in result_dir_path.iterdir() if x.is_file()]

            
                # upload file to S3 bucket

            
                s3_client: S3Client = S3Client()

            
                filekey_prefix = result_dir_path.name

            
                for f in files:

            
                    s3_client.upload_file(f, filekey_prefix)


            if __name__ == "__main__":

            
                parser = argparse.ArgumentParser(description='Run TorchBench tests based on PR')

            
                parser.add_argument('--pr-body', 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")

            
                # parser to upload results to S3

            
                upload_parser = subparsers.add_parser("upload-s3")

            
                upload_parser.add_argument('--result-dir', required=True, type=str, help="Path to benchmark output")

            
                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}")

            
                    # 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}.")

            
                    # Identify the specified models and userbenchmarks

            
                    models, userbenchmarks = extract_models_from_pr(args.torchbench_path, args.pr_body)

            
                    if models:

            
                        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)

            
                    if userbenchmarks:

            
                        assert len(userbenchmarks) == 1, \

            
                            "We don't support running multiple userbenchmarks in single workflow yet." \

            
                            "If you need, please submit a feature request."

            
                        run_userbenchmarks(pytorch_path=args.pytorch_path, torchbench_path=args.torchbench_path,

            
                                           base_sha=args.pr_base_sha, head_sha=args.pr_head_sha,

            
                                           userbenchmark=userbenchmarks[0], output_dir=output_dir)

            
                    if not models and not userbenchmarks:

            
                        print("Can't parse valid models or userbenchmarks from the pr body. Quit.")

            
                        exit(-1)

            
                elif args.command == 'upload-s3':

            
                    process_upload_s3(args.result_dir)

            
                else:

            
                    print(f"The command {args.command} is not supported.")

            
                    exit(-1)
	"""
	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 470.82.01
	- Python 3.8
	- CUDA 11.3
	"""
	# 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 boto3 # type: ignore[import]
	import git # type: ignore[import]
	import pathlib
	import argparse
	import subprocess
	from pathlib import Path

	from typing import List, Tuple

	TORCHBENCH_CONFIG_NAME = "config.yaml"
	TORCHBENCH_USERBENCHMARK_CONFIG_NAME = "ub-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:"""
	S3_BUCKET = "ossci-metrics"
	S3_PREFIX = "torchbench-pr-test"
	S3_URL_BASE = f"https://{S3_BUCKET}.s3.amazonaws.com/"

	class S3Client:
	def __init__(self, bucket: str = S3_BUCKET, prefix: str = S3_PREFIX):
	self.s3 = boto3.client('s3')
	self.resource = boto3.resource('s3')
	self.bucket = bucket
	self.prefix = prefix

	def upload_file(self, file_path: Path, filekey_prefix: str) -> None:
	assert file_path.is_file(), f"Specified file path {file_path} does not exist or not file."
	file_name = file_path.name
	s3_key = f"{self.prefix}/{filekey_prefix}/{file_name}"
	print(f"Uploading file {file_name} to S3 with key: {s3_key}")
	self.s3.upload_file(str(file_path), self.bucket, s3_key)
	# output the result URL
	print(f"Uploaded the result file {file_name} to {S3_URL_BASE}{s3_key}")

	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, config_name: str = TORCHBENCH_CONFIG_NAME) -> 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, config_name)
	with open(config_path, "w") as fp:
	fp.write(config)

	def get_valid_models(torchbench_path: str) -> List[str]:
	benchmark_path = os.path.join(torchbench_path, "torchbenchmark", "models")
	valid_models = [model for model in os.listdir(benchmark_path) if os.path.isdir(os.path.join(benchmark_path, model))]
	return valid_models

	def get_valid_userbenchmarks(torchbench_path: str) -> List[str]:
	def is_valid_ub_dir(ub_path: str) -> bool:
	return os.path.isdir(ub_path) and os.path.exists(os.path.join(ub_path, "__init__.py"))
	ub_path = os.path.join(os.path.abspath(torchbench_path), "userbenchmark")
	ubs = list(filter(is_valid_ub_dir, [os.path.join(ub_path, ubdir) for ubdir in os.listdir(ub_path)]))
	valid_ubs = list(map(lambda x: os.path.basename(x), ubs))
	return valid_ubs

	def extract_models_from_pr(torchbench_path: str, prbody_file: str) -> Tuple[List[str], List[str]]:
	model_list = []
	userbenchmark_list = []
	pr_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.
	pr_list = list(map(lambda x: x.strip(), magic_lines[0][len(MAGIC_PREFIX):].split(",")))
	valid_models = get_valid_models(torchbench_path)
	valid_ubs = get_valid_userbenchmarks(torchbench_path)
	for pr_bm in pr_list:
	if pr_bm in valid_models or pr_bm == "ALL":
	model_list.append(pr_bm)
	elif pr_bm in valid_ubs:
	userbenchmark_list.append(pr_bm)
	else:
	print(f"The model or benchmark {pr_bm} you specified does not exist in TorchBench suite. Please double check.")
	exit(-1)
	# Shortcut: if pr_list is ["ALL"], run all the model tests
	if "ALL" in model_list:
	model_list = ["ALL"]
	return model_list, userbenchmark_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, TORCHBENCH_CONFIG_NAME),
	"--output", os.path.join(output_dir, "result.txt")]
	print(f"Running torchbench command: {command}")
	subprocess.check_call(command, cwd=torchbench_path, env=env)

	def run_userbenchmarks(pytorch_path: str, torchbench_path: str, base_sha: str, head_sha: str,
	userbenchmark: str, output_dir: str) -> None:
	# Copy system environment so that we will not override
	env = dict(os.environ)
	command = ["python", "./.github/scripts/abtest.py",
	"--pytorch-repo", pytorch_path,
	"--base", base_sha,
	"--head", head_sha,
	"--userbenchmark", userbenchmark,
	"--output-dir", output_dir]
	print(f"Running torchbench userbenchmark command: {command}")
	subprocess.check_call(command, cwd=torchbench_path, env=env)

	def process_upload_s3(result_dir: str) -> None:
	# validate result directory
	result_dir_path = Path(result_dir)
	assert result_dir_path.exists(), f"Specified result directory {result_dir} doesn't exist."
	# upload all files to S3 bucket oss-ci-metrics
	files = [x for x in result_dir_path.iterdir() if x.is_file()]
	# upload file to S3 bucket
	s3_client: S3Client = S3Client()
	filekey_prefix = result_dir_path.name
	for f in files:
	s3_client.upload_file(f, filekey_prefix)

	if __name__ == "__main__":
	parser = argparse.ArgumentParser(description='Run TorchBench tests based on PR')
	parser.add_argument('--pr-body', 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")
	# parser to upload results to S3
	upload_parser = subparsers.add_parser("upload-s3")
	upload_parser.add_argument('--result-dir', required=True, type=str, help="Path to benchmark output")
	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}")
	# 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}.")
	# Identify the specified models and userbenchmarks
	models, userbenchmarks = extract_models_from_pr(args.torchbench_path, args.pr_body)
	if models:
	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)
	if userbenchmarks:
	assert len(userbenchmarks) == 1, \
	"We don't support running multiple userbenchmarks in single workflow yet." \
	"If you need, please submit a feature request."
	run_userbenchmarks(pytorch_path=args.pytorch_path, torchbench_path=args.torchbench_path,
	base_sha=args.pr_base_sha, head_sha=args.pr_head_sha,
	userbenchmark=userbenchmarks[0], output_dir=output_dir)
	if not models and not userbenchmarks:
	print("Can't parse valid models or userbenchmarks from the pr body. Quit.")
	exit(-1)
	elif args.command == 'upload-s3':
	process_upload_s3(args.result_dir)
	else:
	print(f"The command {args.command} is not supported.")
	exit(-1)