mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
2cb983ab97
adds a `default` tag to experiment configurations, allowing to remove some experiments by default on the random draw:
```
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 25
default: false
---
```
and includes the configuration to filter what experiments are of interest for a particular workflow (comma separated):
```
get-test-label-type:
name: get-test-label-type
uses: ./.github/workflows/_runner-determinator.yml
with:
...
check_experiments: "awsa100"
```
The end goal, is to enable us to run multiple experiments, that are independent from one another. For example, while we still runs the LF infra experiment, we want to migrate other runners leveraging the current solution. A immediate UC is for the A100 instances, where we want to migrate to AWS.
Those new instances will during the migration period be labeled both `awsa100.linux.gcp.a100` and `linux.aws.a100`. Once the experiment ends, we will remove the first confusing one.
```
jobs:
get-build-label-type:
name: get-build-label-type
uses: ./.github/workflows/_runner-determinator.yml
with:
...
get-test-label-type:
name: get-test-label-type
uses: ./.github/workflows/_runner-determinator.yml
with:
...
check_experiments: "awsa100"
linux-focal-cuda12_1-py3_10-gcc9-inductor-build:
name: cuda12.1-py3.10-gcc9-sm80
uses: ./.github/workflows/_linux-build.yml
needs:
- get-build-label-type
- get-test-label-type
with:
runner_prefix: "${{ needs.get-build-label-type.outputs.label-type }}"
...
test-matrix: |
{ include: [
{ config: "inductor_huggingface_perf_compare", shard: 1, num_shards: 1, runner: "${{ needs.get-test-label-type.outputs.label-type }}linux.gcp.a100" },
...
]}
...
```
```
experiments:
lf:
rollout_perc: 50
awsa100:
rollout_perc: 50
default: false
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137614
Approved by: https://github.com/malfet
491 lines
16 KiB
Python
491 lines
16 KiB
Python
# flake8: noqa: G004
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# Note: Copies of this script in runner_determinator.py and _runner-determinator.yml
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# must be kept in sync. You can do it easily by running the following command:
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# python .github/scripts/update_runner_determinator.py
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"""
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This runner determinator is used to determine which set of runners to run a
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GitHub job on. It uses the first comment of a GitHub issue (by default
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https://github.com/pytorch/test-infra/issues/5132) to define the configuration
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of which runners should be used to run which job.
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The configuration has two parts, the settings and a list of opted-in users,
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separated by a line containing "---". If the line is not present, the
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settings are considered to be empty with only the second part, the user
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list, defined.
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The first part is a YAML block that defines the rollout settings. This can be
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used to define any settings that are needed to determine which runners to use.
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It's fields are defined by the RolloutSettings class below.
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The second part is a list of users who are explicitly opted in to the LF fleet.
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The user list is also a comma separated list of additional features or
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experiments which the user could be opted in to.
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The user list has the following rules:
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- Users are GitHub usernames, which must start with the @ prefix
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- Each user is also a comma-separated list of features/experiments to enable
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- A "#" prefix opts the user out of all experiments
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Example config:
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# A list of experiments that can be opted into.
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# This defines the behavior they'll induce when opted into.
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# Expected syntax is:
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# [experiment_name]: # Name of the experiment. Also used for the label prefix.
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# rollout_perc: [int] # % of workflows to run with this experiment when users are not opted in.
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experiments:
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lf:
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rollout_percent: 25
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all_branches: false
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default: true
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---
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# Opt-ins:
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# Users can opt into the LF fleet by adding their GitHub username to this list
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# and specifying experiments to enable in a comma-separated list.
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# Experiments should be from the above list.
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@User1,lf,split_build
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@User2,lf
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@User3,split_build
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"""
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import logging
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import os
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import random
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from argparse import ArgumentParser
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from logging import LogRecord
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from typing import Any, Dict, FrozenSet, Iterable, List, NamedTuple, Tuple
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import yaml
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from github import Auth, Github
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from github.Issue import Issue
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DEFAULT_LABEL_PREFIX = "" # use meta runners
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WORKFLOW_LABEL_LF = "lf." # use runners from the linux foundation
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WORKFLOW_LABEL_LF_CANARY = "lf.c." # use canary runners from the linux foundation
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GITHUB_OUTPUT = os.getenv("GITHUB_OUTPUT", "")
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GH_OUTPUT_KEY_AMI = "runner-ami"
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GH_OUTPUT_KEY_LABEL_TYPE = "label-type"
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SETTING_EXPERIMENTS = "experiments"
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LF_FLEET_EXPERIMENT = "lf"
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CANARY_FLEET_SUFFIX = ".c"
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class Experiment(NamedTuple):
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rollout_perc: float = (
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0 # Percentage of workflows to experiment on when user is not opted-in.
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)
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all_branches: bool = (
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False # If True, the experiment is also enabled on the exception branches
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)
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default: bool = (
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True # If True, the experiment is enabled by default for all queries
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)
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# Add more fields as needed
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class Settings(NamedTuple):
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"""
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Settings for the experiments that can be opted into.
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"""
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experiments: Dict[str, Experiment] = {}
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class ColorFormatter(logging.Formatter):
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"""Color codes the log messages based on the log level"""
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COLORS = {
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"WARNING": "\033[33m", # Yellow
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"ERROR": "\033[31m", # Red
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"CRITICAL": "\033[31m", # Red
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"INFO": "\033[0m", # Reset
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"DEBUG": "\033[0m", # Reset
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}
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def format(self, record: LogRecord) -> str:
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log_color = self.COLORS.get(record.levelname, "\033[0m") # Default to reset
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record.msg = f"{log_color}{record.msg}\033[0m"
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return super().format(record)
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handler = logging.StreamHandler()
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handler.setFormatter(ColorFormatter(fmt="%(levelname)-8s: %(message)s"))
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log = logging.getLogger(os.path.basename(__file__))
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log.addHandler(handler)
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log.setLevel(logging.INFO)
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def set_github_output(key: str, value: str) -> None:
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"""
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Defines outputs of the github action that invokes this script
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"""
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if not GITHUB_OUTPUT:
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# See https://github.blog/changelog/2022-10-11-github-actions-deprecating-save-state-and-set-output-commands/ for deprecation notice
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log.warning(
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"No env var found for GITHUB_OUTPUT, you must be running this code locally. Falling back to the deprecated print method."
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)
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print(f"::set-output name={key}::{value}")
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return
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with open(GITHUB_OUTPUT, "a") as f:
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log.info(f"Setting output: {key}='{value}'")
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f.write(f"{key}={value}\n")
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def _str_comma_separated_to_set(value: str) -> FrozenSet[str]:
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return frozenset(
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filter(lambda itm: itm != "", map(str.strip, value.strip(" \n\t").split(",")))
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)
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def parse_args() -> Any:
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parser = ArgumentParser("Get dynamic rollout settings")
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parser.add_argument("--github-token", type=str, required=True, help="GitHub token")
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parser.add_argument(
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"--github-issue-repo",
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type=str,
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required=False,
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default="pytorch/test-infra",
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help="GitHub repo to get the issue",
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)
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parser.add_argument(
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"--github-repo",
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type=str,
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required=True,
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help="GitHub repo where CI is running",
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)
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parser.add_argument(
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"--github-issue", type=int, required=True, help="GitHub issue number"
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)
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parser.add_argument(
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"--github-actor", type=str, required=True, help="GitHub triggering_actor"
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)
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parser.add_argument(
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"--github-issue-owner", type=str, required=True, help="GitHub issue owner"
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)
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parser.add_argument(
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"--github-branch", type=str, required=True, help="Current GitHub branch or tag"
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)
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parser.add_argument(
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"--github-ref-type",
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type=str,
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required=True,
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help="Current GitHub ref type, branch or tag",
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)
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parser.add_argument(
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"--eligible-experiments",
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type=_str_comma_separated_to_set,
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required=False,
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default="",
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help="comma separated list of experiments to check, if omitted all experiments marked with default=True are checked",
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)
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return parser.parse_args()
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def get_gh_client(github_token: str) -> Github:
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auth = Auth.Token(github_token)
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return Github(auth=auth)
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def get_issue(gh: Github, repo: str, issue_num: int) -> Issue:
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repo = gh.get_repo(repo)
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return repo.get_issue(number=issue_num)
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def get_potential_pr_author(
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github_token: str, repo: str, username: str, ref_type: str, ref_name: str
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) -> str:
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# If the trigger was a new tag added by a bot, this is a ciflow case
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# Fetch the actual username from the original PR. The PR number is
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# embedded in the tag name: ciflow/<name>/<pr-number>
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gh = get_gh_client(github_token)
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if username == "pytorch-bot[bot]" and ref_type == "tag":
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split_tag = ref_name.split("/")
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if (
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len(split_tag) == 3
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and split_tag[0] == "ciflow"
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and split_tag[2].isnumeric()
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):
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pr_number = split_tag[2]
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try:
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repository = gh.get_repo(repo)
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pull = repository.get_pull(number=int(pr_number))
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except Exception as e:
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raise Exception( # noqa: TRY002
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f"issue with pull request {pr_number} from repo {repository}"
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) from e
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return pull.user.login
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# In all other cases, return the original input username
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return username
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def is_exception_branch(branch: str) -> bool:
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"""
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Branches that get opted out of experiments by default, until they're explicitly enabled.
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"""
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return branch.split("/")[0] in {"main", "nightly", "release", "landchecks"}
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def load_yaml(yaml_text: str) -> Any:
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try:
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data = yaml.safe_load(yaml_text)
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return data
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except yaml.YAMLError as exc:
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log.exception("Error loading YAML")
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raise
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def extract_settings_user_opt_in_from_text(rollout_state: str) -> Tuple[str, str]:
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"""
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Extracts the text with settings, if any, and the opted in users from the rollout state.
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If the issue body contains "---" then the text above that is the settings
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and the text below is the list of opted in users.
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If it doesn't contain "---" then the settings are empty and the rest is the users.
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"""
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rollout_state_parts = rollout_state.split("---")
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if len(rollout_state_parts) >= 2:
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return rollout_state_parts[0], rollout_state_parts[1]
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else:
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return "", rollout_state
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class UserOptins(Dict[str, List[str]]):
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"""
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Dictionary of users with a list of features they have opted into
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"""
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def parse_user_opt_in_from_text(user_optin_text: str) -> UserOptins:
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"""
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Parse the user opt-in text into a key value pair of username and the list of features they have opted into
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Users are GitHub usernames with the @ prefix. Each user is also a comma-separated list of features/experiments to enable.
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- Example line: "@User1,lf,split_build"
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- A "#" prefix indicates the user is opted out of all experiments
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"""
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optins = UserOptins()
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for user in user_optin_text.split("\n"):
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user = user.strip("\r\n\t -")
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if not user or not user.startswith("@"):
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# Not a valid user. Skip
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continue
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if user:
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usr_name = user.split(",")[0].strip("@")
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optins[usr_name] = [exp.strip(" ") for exp in user.split(",")[1:]]
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return optins
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def parse_settings_from_text(settings_text: str) -> Settings:
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"""
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Parse the experiments from the issue body into a list of ExperimentSettings
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"""
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try:
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if settings_text:
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# Escape the backtick as well so that we can have the settings in a code block on the GH issue
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# for easy reading
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# Note: Using ascii for the backtick so that the cat step in _runner-determinator.yml doesn't choke on
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# the backtick character in shell commands.
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backtick = chr(96) # backtick character
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settings_text = settings_text.strip(f"\r\n\t{backtick} ")
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settings = load_yaml(settings_text)
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# For now we just load experiments. We can expand this if/when we add more settings
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experiments = {}
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for exp_name, exp_settings in settings.get(SETTING_EXPERIMENTS).items():
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valid_settings = {}
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for setting in exp_settings:
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if setting not in Experiment._fields:
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log.warning(
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f"Unexpected setting in experiment: {setting} = {exp_settings[setting]}"
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)
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else:
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valid_settings[setting] = exp_settings[setting]
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experiments[exp_name] = Experiment(**valid_settings)
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return Settings(experiments)
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except Exception:
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log.exception("Failed to parse settings")
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return Settings()
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def parse_settings(rollout_state: str) -> Settings:
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"""
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Parse settings, if any, from the rollout state.
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If the issue body contains "---" then the text above that is the settings
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and the text below is the list of opted in users.
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If it doesn't contain "---" then the settings are empty and the default values are used.
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"""
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settings_text, _ = extract_settings_user_opt_in_from_text(rollout_state)
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return parse_settings_from_text(settings_text)
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def parse_users(rollout_state: str) -> UserOptins:
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"""
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Parse users from the rollout state.
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"""
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_, users_text = extract_settings_user_opt_in_from_text(rollout_state)
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return parse_user_opt_in_from_text(users_text)
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def is_user_opted_in(user: str, user_optins: UserOptins, experiment_name: str) -> bool:
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"""
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Check if a user is opted into an experiment
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"""
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return experiment_name in user_optins.get(user, [])
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def get_runner_prefix(
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rollout_state: str,
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workflow_requestors: Iterable[str],
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branch: str,
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eligible_experiments: FrozenSet[str] = frozenset(),
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is_canary: bool = False,
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) -> str:
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settings = parse_settings(rollout_state)
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user_optins = parse_users(rollout_state)
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fleet_prefix = ""
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prefixes = []
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for experiment_name, experiment_settings in settings.experiments.items():
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if not experiment_settings.all_branches and is_exception_branch(branch):
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log.info(
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f"Branch {branch} is an exception branch. Not enabling experiment {experiment_name}."
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)
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continue
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if eligible_experiments:
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if experiment_name not in eligible_experiments:
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exp_list = ", ".join(eligible_experiments)
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log.info(
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f"Skipping experiment '{experiment_name}', as it is not in the eligible_experiments list: {exp_list}"
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)
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continue
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elif not experiment_settings.default:
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log.info(
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f"Skipping experiment '{experiment_name}', as it is not a default experiment"
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)
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continue
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# Is any workflow_requestor opted in to this experiment?
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opted_in_users = [
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requestor
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for requestor in workflow_requestors
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if is_user_opted_in(requestor, user_optins, experiment_name)
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]
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enabled = False
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if opted_in_users:
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log.info(
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f"{', '.join(opted_in_users)} have opted into experiment {experiment_name}."
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)
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enabled = True
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elif experiment_settings.rollout_perc:
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# If no user is opted in, then we randomly enable the experiment based on the rollout percentage
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if random.uniform(0, 100) <= experiment_settings.rollout_perc:
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log.info(
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f"Based on rollout percentage of {experiment_settings.rollout_perc}%, enabling experiment {experiment_name}."
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)
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enabled = True
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if enabled:
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label = experiment_name
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if experiment_name == LF_FLEET_EXPERIMENT:
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# We give some special treatment to the "lf" experiment since determines the fleet we use
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# - If it's enabled, then we always list it's prefix first
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# - If we're in the canary branch, then we append ".c" to the lf prefix
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if is_canary:
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label += CANARY_FLEET_SUFFIX
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fleet_prefix = label
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else:
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prefixes.append(label)
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if len(prefixes) > 1:
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log.error(
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f"Only a fleet and one other experiment can be enabled for a job at any time. Enabling {prefixes[0]} and ignoring the rest, which are {', '.join(prefixes[1:])}"
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)
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prefixes = prefixes[:1]
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# Fleet always comes first
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if fleet_prefix:
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prefixes.insert(0, fleet_prefix)
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return ".".join(prefixes) + "." if prefixes else ""
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def get_rollout_state_from_issue(github_token: str, repo: str, issue_num: int) -> str:
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"""
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Gets the first comment of the issue, which contains the desired rollout state.
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The default issue we use - https://github.com/pytorch/test-infra/issues/5132
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"""
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gh = get_gh_client(github_token)
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issue = get_issue(gh, repo, issue_num)
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return str(issue.get_comments()[0].body.strip("\n\t "))
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def main() -> None:
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args = parse_args()
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runner_label_prefix = DEFAULT_LABEL_PREFIX
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try:
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rollout_state = get_rollout_state_from_issue(
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args.github_token, args.github_issue_repo, args.github_issue
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)
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username = get_potential_pr_author(
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args.github_token,
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args.github_repo,
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args.github_actor,
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args.github_ref_type,
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args.github_branch,
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)
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is_canary = args.github_repo == "pytorch/pytorch-canary"
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|
|
runner_label_prefix = get_runner_prefix(
|
|
rollout_state,
|
|
(args.github_issue_owner, username),
|
|
args.github_branch,
|
|
args.eligible_experiments,
|
|
is_canary,
|
|
)
|
|
|
|
except Exception as e:
|
|
log.error(
|
|
f"Failed to get issue. Defaulting to Meta runners and no experiments. Exception: {e}"
|
|
)
|
|
|
|
set_github_output(GH_OUTPUT_KEY_LABEL_TYPE, runner_label_prefix)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|