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Support rolling over a percentage of workflows (#134816)

Browse Source 
In order to support adding a rollover percentage, this ended up being a complete rewrite of runner_determinator.py.

Details of the new format are in the comments up top.

On the plus side, this now includes some unit tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134816
Approved by: https://github.com/PaliC, https://github.com/zxiiro
This commit is contained in:
Zain Rizvi
2024-09-11 12:31:55 -05:00
committed by PyTorch MergeBot
parent 5314ae2660
commit 09519eb195
3 changed files with 711 additions and 226 deletions
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350
.github/scripts/runner_determinator.py vendored
View File

@ -3,49 +3,94 @@
"""
This runner determinator is used to determine which set of runners to run a
GitHub job on. It uses the first comment of a GitHub issue (by default
https://github.com/pytorch/test-infra/issues/5132) as a user list to determine
which users will get their jobs to run on experimental runners. This user list
is also a comma separated list of additional features or experiments which the
user could be opted in to.
https://github.com/pytorch/test-infra/issues/5132) to define the configuration
of which runners should be used to run which job.
The configuration has two parts, the settings and a list of opted-in users,
separated by a line containing "---". If the line is not present, the
settings are considered to be empty with only the second part, the user
list, defined.
The first part is a YAML block that defines the rollout settings. This can be
used to define any settings that are needed to determine which runners to use.
It's fields are defined by the RolloutSettings class below.
The second part is a list of users who are explicitly opted in to the LF fleet.
The user list is also a comma separated list of additional features or
experiments which the user could be opted in to.
The user list has the following rules:
- Users are GitHub usernames with the @ prefix
- If the first line is a "*" then all users will use the new runners
- If the first line is a "!" then all users will use the old runners
- Users are GitHub usernames, which must start with the @ prefix
- Each user is also a comma-separated list of features/experiments to enable
- A "#" prefix indicates the user is opted out of the new runners but is opting
into features/experiments.
- A "#" prefix opts the user out of all experiments
Example user list:
Example config:
# A list of experiments that can be opted into.
# This defines the behavior they'll induce when opted into.
# Expected syntax is:
# [experiment_name]: # Name of the experiment. Also used for the label prefix.
# rollout_perc: [int] # % of workflows to run with this experiment when users are not opted in.
@User1
@User2,amz2023
#@UserOptOutOfNewRunner,amz2023
experiments:
lf:
rollout_percent: 25
---
# Opt-ins:
# Users can opt into the LF fleet by adding their GitHub username to this list
# and specifying experiments to enable in a comma-separated list.
# Experiments should be from the above list.
@User1,lf,split_build
@User2,lf
@User3,split_build
"""
import logging
import os
import random
from argparse import ArgumentParser
from logging import LogRecord
from typing import Any, Iterable
from typing import Any, Dict, Iterable, List, NamedTuple, Tuple
import yaml
from github import Auth, Github
from github.Issue import Issue
WORKFLOW_LABEL_META = "" # use meta runners
DEFAULT_LABEL_PREFIX = "" # use meta runners
WORKFLOW_LABEL_LF = "lf." # use runners from the linux foundation
WORKFLOW_LABEL_LF_CANARY = "lf.c." # use canary runners from the linux foundation
RUNNER_AMI_LEGACY = ""
RUNNER_AMI_AMZ2023 = "amz2023"
GITHUB_OUTPUT = os.getenv("GITHUB_OUTPUT", "")
GH_OUTPUT_KEY_AMI = "runner-ami"
GH_OUTPUT_KEY_LABEL_TYPE = "label-type"
SETTING_EXPERIMENTS = "experiments"
LF_FLEET_EXPERIMENT = "lf"
CANARY_FLEET_SUFFIX = ".c"
class Experiment(NamedTuple):
rollout_perc: float = (
0 # Percentage of workflows to experiment on when user is not opted-in.
)
# Add more fields as needed
class Settings(NamedTuple):
"""
Settings for the experiments that can be opted into.
"""
experiments: Dict[str, Experiment] = {}
class ColorFormatter(logging.Formatter):
"""Color codes the log messages based on the log level"""
@ -172,85 +217,180 @@ def is_exception_branch(branch: str) -> bool:
return branch.split("/")[0] in {"main", "nightly", "release", "landchecks"}
def get_fleet(rollout_state: str, workflow_requestors: Iterable[str]) -> str:
"""
Determines if the job should run on the LF fleet or the Meta fleet
Returns:
The appropriate label prefix for the runner, corresponding to the fleet to use.
This gets prefixed to the very start of the runner label.
"""
def load_yaml(yaml_text: str) -> Any:
try:
if rollout_state[0] == "!":
log.info("LF Workflows are disabled for everyone. Using meta runners.")
return WORKFLOW_LABEL_META
elif rollout_state[0] == "*":
log.info("LF Workflows are enabled for everyone. Using LF runners.")
return WORKFLOW_LABEL_LF
else:
all_opted_in_users = {
usr_raw.strip("\n\t@ ").split(",")[0]
for usr_raw in rollout_state.split()
}
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
if opted_in_requestors:
log.info(
f"LF Workflows are enabled for {', '.join(opted_in_requestors)}. Using LF runners."
)
return WORKFLOW_LABEL_LF
else:
log.info(
f"LF Workflows are disabled for {', '.join(workflow_requestors)}. Using meta runners."
)
return WORKFLOW_LABEL_META
except Exception as e:
log.error(
f"Failed to get determine workflow type. Falling back to meta runners. Exception: {e}"
)
return WORKFLOW_LABEL_META
data = yaml.safe_load(yaml_text)
return data
except yaml.YAMLError as exc:
log.exception("Error loading YAML")
raise
def get_optin_feature(
rollout_state: str, workflow_requestors: Iterable[str], feature: str, fallback: str
def extract_settings_user_opt_in_from_text(rollout_state: str) -> Tuple[str, str]:
"""
Extracts the text with settings, if any, and the opted in users from the rollout state.
If the issue body contains "---" then the text above that is the settings
and the text below is the list of opted in users.
If it doesn't contain "---" then the settings are empty and the rest is the users.
"""
rollout_state_parts = rollout_state.split("---")
if len(rollout_state_parts) >= 2:
return rollout_state_parts[0], rollout_state_parts[1]
else:
return "", rollout_state
class UserOptins(Dict[str, List[str]]):
"""
Dictionary of users with a list of features they have opted into
"""
def parse_user_opt_in_from_text(user_optin_text: str) -> UserOptins:
"""
Parse the user opt-in text into a key value pair of username and the list of features they have opted into
Users are GitHub usernames with the @ prefix. Each user is also a comma-separated list of features/experiments to enable.
- Example line: "@User1,lf,split_build"
- A "#" prefix indicates the user is opted out of all experiments
"""
optins = UserOptins()
for user in user_optin_text.split("\n"):
user = user.strip("\r\n\t -")
if not user or not user.startswith("@"):
# Not a valid user. Skip
continue
if user:
usr_name = user.split(",")[0].strip("@")
optins[usr_name] = [exp.strip(" ") for exp in user.split(",")[1:]]
return optins
def parse_settings_from_text(settings_text: str) -> Settings:
"""
Parse the experiments from the issue body into a list of ExperimentSettings
"""
try:
if settings_text:
# Escape the backtick as well so that we can have the settings in a code block on the GH issue
# for easy reading
# Note: Using ascii for the backtick so that the cat step in _runner-determinator.yml doesn't choke on
# the backtick character in shell commands.
backtick = chr(96) # backtick character
settings_text = settings_text.strip(f"\r\n\t{backtick} ")
settings = load_yaml(settings_text)
# For now we just load experiments. We can expand this if/when we add more settings
experiments = {}
for exp_name, exp_settings in settings.get(SETTING_EXPERIMENTS).items():
valid_settings = {}
for setting in exp_settings:
if setting not in Experiment._fields:
log.warning(
f"Unexpected setting in experiment: {setting} = {exp_settings[setting]}"
)
else:
valid_settings[setting] = exp_settings[setting]
experiments[exp_name] = Experiment(**valid_settings)
return Settings(experiments)
except Exception:
log.exception("Failed to parse settings")
return Settings()
def parse_settings(rollout_state: str) -> Settings:
"""
Parse settings, if any, from the rollout state.
If the issue body contains "---" then the text above that is the settings
and the text below is the list of opted in users.
If it doesn't contain "---" then the settings are empty and the default values are used.
"""
settings_text, _ = extract_settings_user_opt_in_from_text(rollout_state)
return parse_settings_from_text(settings_text)
def parse_users(rollout_state: str) -> UserOptins:
"""
Parse users from the rollout state.
"""
_, users_text = extract_settings_user_opt_in_from_text(rollout_state)
return parse_user_opt_in_from_text(users_text)
def is_user_opted_in(user: str, user_optins: UserOptins, experiment_name: str) -> bool:
"""
Check if a user is opted into an experiment
"""
return experiment_name in user_optins.get(user, [])
def get_runner_prefix(
rollout_state: str, workflow_requestors: Iterable[str], is_canary: bool = False
) -> str:
"""
Used to dynamically opt in jobs to specific runner-type variants.
settings = parse_settings(rollout_state)
user_optins = parse_users(rollout_state)
Returns:
The runner-type's variant name if the user has opted in to the feature, otherwise returns an empty string.
This variant name is prefixed to the runner-type in the label.
"""
try:
userlist = {u.lstrip("#").strip("\n\t@ ") for u in rollout_state.split()}
all_opted_in_users = set()
for user in userlist:
for i in user.split(","):
if i == feature:
all_opted_in_users.add(user.split(",")[0])
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
fleet_prefix = ""
prefixes = []
for experiment_name, experiment_settings in settings.experiments.items():
enabled = False
if opted_in_requestors:
# Is any workflow_requestor opted in to this experiment?
opted_in_users = [
requestor
for requestor in workflow_requestors
if is_user_opted_in(requestor, user_optins, experiment_name)
]
if opted_in_users:
log.info(
f"Feature {feature} is enabled for {', '.join(opted_in_requestors)}. Using feature {feature}."
f"{', '.join(opted_in_users)} have opted into experiment {experiment_name}."
)
return feature
else:
log.info(
f"Feature {feature} is disabled for {', '.join(workflow_requestors)}. Using fallback \"{fallback}\"."
)
return fallback
enabled = True
elif experiment_settings.rollout_perc:
# If no user is opted in, then we randomly enable the experiment based on the rollout percentage
if random.uniform(0, 100) <= experiment_settings.rollout_perc:
log.info(
f"Based on rollout percentage of {experiment_settings.rollout_perc}%, enabling experiment {experiment_name}."
)
enabled = True
except Exception as e:
if enabled:
label = experiment_name
if experiment_name == LF_FLEET_EXPERIMENT:
# We give some special treatment to the "lf" experiment since determines the fleet we use
# - If it's enabled, then we always list it's prefix first
# - If we're in the canary branch, then we append ".c" to the lf prefix
if is_canary:
label += CANARY_FLEET_SUFFIX
fleet_prefix = label
else:
prefixes.append(label)
if len(prefixes) > 1:
log.error(
f'Failed to determine if user has opted-in to feature {feature}. Using fallback "{fallback}". Exception: {e}'
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:])}"
)
return fallback
prefixes = prefixes[:1]
# Fleet always comes first
if fleet_prefix:
prefixes.insert(0, fleet_prefix)
return ".".join(prefixes) + "." if prefixes else ""
def get_rollout_state_from_issue(github_token: str, repo: str, issue_num: int) -> str:
@ -268,9 +408,10 @@ def main() -> None:
args = parse_args()
if args.github_ref_type == "branch" and is_exception_branch(args.github_branch):
log.info(f"Exception branch: '{args.github_branch}', using meta runners")
label_type = WORKFLOW_LABEL_META
runner_ami = RUNNER_AMI_LEGACY
log.info(
f"Exception branch: '{args.github_branch}', using Meta runners and no experiments."
)
runner_label_prefix = DEFAULT_LABEL_PREFIX
else:
try:
rollout_state = get_rollout_state_from_issue(
@ -285,35 +426,18 @@ def main() -> None:
args.github_branch,
)
label_type = get_fleet(
rollout_state,
(
args.github_issue_owner,
username,
),
)
runner_ami = get_optin_feature(
rollout_state=rollout_state,
workflow_requestors=(
args.github_issue_owner,
username,
),
feature=RUNNER_AMI_AMZ2023,
fallback=RUNNER_AMI_LEGACY,
is_canary = args.github_repo == "pytorch/pytorch-canary"
runner_label_prefix = get_runner_prefix(
rollout_state, (args.github_issue_owner, username), is_canary
)
except Exception as e:
log.error(
f"Failed to get issue. Falling back to meta runners. Exception: {e}"
f"Failed to get issue. Defaulting to Meta runners and no experiments. Exception: {e}"
)
label_type = WORKFLOW_LABEL_META
runner_ami = RUNNER_AMI_LEGACY
# For Canary builds use canary runners
if args.github_repo == "pytorch/pytorch-canary" and label_type == WORKFLOW_LABEL_LF:
label_type = WORKFLOW_LABEL_LF_CANARY
set_github_output(GH_OUTPUT_KEY_LABEL_TYPE, label_type)
set_github_output(GH_OUTPUT_KEY_AMI, runner_ami)
set_github_output(GH_OUTPUT_KEY_LABEL_TYPE, runner_label_prefix)
if __name__ == "__main__":

237
.github/scripts/test_runner_determinator.py vendored Normal file
View File

@ -0,0 +1,237 @@
from unittest import main, TestCase
from unittest.mock import Mock, patch
import runner_determinator as rd
class TestRunnerDeterminatorIssueParser(TestCase):
def test_parse_settings(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 0
---
Users:
@User1,lf
@User2,lf,otherExp
"""
settings = rd.parse_settings(settings_text)
self.assertTupleEqual(
rd.Experiment(rollout_perc=25),
settings.experiments["lf"],
"lf settings not parsed correctly",
)
self.assertTupleEqual(
rd.Experiment(rollout_perc=0),
settings.experiments["otherExp"],
"otherExp settings not parsed correctly",
)
def test_parse_settings_in_code_block(self) -> None:
settings_text = """
```
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 0
```
---
Users:
@User1,lf
@User2,lf,otherExp
"""
settings = rd.parse_settings(settings_text)
self.assertTupleEqual(
rd.Experiment(rollout_perc=25),
settings.experiments["lf"],
"lf settings not parsed correctly",
)
self.assertTupleEqual(
rd.Experiment(rollout_perc=0),
settings.experiments["otherExp"],
"otherExp settings not parsed correctly",
)
def test_parse_users(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
---
Users:
@User1,lf
@User2,lf,otherExp
"""
users = rd.parse_users(settings_text)
self.assertDictEqual(
{"User1": ["lf"], "User2": ["lf", "otherExp"]},
users,
"Users not parsed correctly",
)
def test_parse_users_without_settings(self) -> None:
settings_text = """
@User1,lf
@User2,lf,otherExp
"""
users = rd.parse_users(settings_text)
self.assertDictEqual(
{"User1": ["lf"], "User2": ["lf", "otherExp"]},
users,
"Users not parsed correctly",
)
class TestRunnerDeterminatorGetRunnerPrefix(TestCase):
def test_opted_in_user(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
---
Users:
@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User1"])
self.assertEqual("lf.", prefix, "Runner prefix not correct for User1")
def test_opted_in_user_two_experiments(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
---
Users:
@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User2"])
self.assertEqual("lf.otherExp.", prefix, "Runner prefix not correct for User2")
@patch("random.uniform", return_value=50)
def test_opted_out_user(self, mock_uniform: Mock) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 25
---
Users:
@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User3"])
self.assertEqual("", prefix, "Runner prefix not correct for user")
@patch("random.uniform", return_value=10)
def test_opted_out_user_was_pulled_in_by_rollout(self, mock_uniform: Mock) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 25
otherExp:
rollout_perc: 25
---
Users:
@User1,lf
@User2,lf,otherExp
"""
# User3 is opted out, but is pulled into both experiments by the 10% rollout
prefix = rd.get_runner_prefix(settings_text, ["User3"])
self.assertEqual("lf.otherExp.", prefix, "Runner prefix not correct for user")
def test_lf_prefix_always_comes_first(self) -> None:
settings_text = """
experiments:
otherExp:
rollout_perc: 0
lf:
rollout_perc: 0
---
Users:
@User1,lf
@User2,otherExp,lf
"""
prefix = rd.get_runner_prefix(settings_text, ["User2"])
self.assertEqual("lf.otherExp.", prefix, "Runner prefix not correct for user")
def test_ignores_commented_users(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
---
Users:
#@User1,lf
@User2,lf,otherExp
"""
prefix = rd.get_runner_prefix(settings_text, ["User1"])
self.assertEqual("", prefix, "Runner prefix not correct for user")
def test_ignores_extra_experiments(self) -> None:
settings_text = """
experiments:
lf:
rollout_perc: 0
otherExp:
rollout_perc: 0
foo:
rollout_perc: 0
---
Users:
@User1,lf,otherExp,foo
"""
prefix = rd.get_runner_prefix(settings_text, ["User1"])
self.assertEqual("lf.otherExp.", prefix, "Runner prefix not correct for user")
if __name__ == "__main__":
main()

350
.github/workflows/_runner-determinator.yml vendored
View File

@ -62,49 +62,94 @@ jobs:
"""
This runner determinator is used to determine which set of runners to run a
GitHub job on. It uses the first comment of a GitHub issue (by default
https://github.com/pytorch/test-infra/issues/5132) as a user list to determine
which users will get their jobs to run on experimental runners. This user list
is also a comma separated list of additional features or experiments which the
user could be opted in to.
https://github.com/pytorch/test-infra/issues/5132) to define the configuration
of which runners should be used to run which job.
The configuration has two parts, the settings and a list of opted-in users,
separated by a line containing "---". If the line is not present, the
settings are considered to be empty with only the second part, the user
list, defined.
The first part is a YAML block that defines the rollout settings. This can be
used to define any settings that are needed to determine which runners to use.
It's fields are defined by the RolloutSettings class below.
The second part is a list of users who are explicitly opted in to the LF fleet.
The user list is also a comma separated list of additional features or
experiments which the user could be opted in to.
The user list has the following rules:
- Users are GitHub usernames with the @ prefix
- If the first line is a "*" then all users will use the new runners
- If the first line is a "!" then all users will use the old runners
- Users are GitHub usernames, which must start with the @ prefix
- Each user is also a comma-separated list of features/experiments to enable
- A "#" prefix indicates the user is opted out of the new runners but is opting
into features/experiments.
- A "#" prefix opts the user out of all experiments
Example user list:
Example config:
# A list of experiments that can be opted into.
# This defines the behavior they'll induce when opted into.
# Expected syntax is:
# [experiment_name]: # Name of the experiment. Also used for the label prefix.
# rollout_perc: [int] # % of workflows to run with this experiment when users are not opted in.
@User1
@User2,amz2023
#@UserOptOutOfNewRunner,amz2023
experiments:
lf:
rollout_percent: 25
---
# Opt-ins:
# Users can opt into the LF fleet by adding their GitHub username to this list
# and specifying experiments to enable in a comma-separated list.
# Experiments should be from the above list.
@User1,lf,split_build
@User2,lf
@User3,split_build
"""
import logging
import os
import random
from argparse import ArgumentParser
from logging import LogRecord
from typing import Any, Iterable
from typing import Any, Dict, Iterable, List, NamedTuple, Tuple
import yaml
from github import Auth, Github
from github.Issue import Issue
WORKFLOW_LABEL_META = "" # use meta runners
DEFAULT_LABEL_PREFIX = "" # use meta runners
WORKFLOW_LABEL_LF = "lf." # use runners from the linux foundation
WORKFLOW_LABEL_LF_CANARY = "lf.c." # use canary runners from the linux foundation
RUNNER_AMI_LEGACY = ""
RUNNER_AMI_AMZ2023 = "amz2023"
GITHUB_OUTPUT = os.getenv("GITHUB_OUTPUT", "")
GH_OUTPUT_KEY_AMI = "runner-ami"
GH_OUTPUT_KEY_LABEL_TYPE = "label-type"
SETTING_EXPERIMENTS = "experiments"
LF_FLEET_EXPERIMENT = "lf"
CANARY_FLEET_SUFFIX = ".c"
class Experiment(NamedTuple):
rollout_perc: float = (
0 # Percentage of workflows to experiment on when user is not opted-in.
)
# Add more fields as needed
class Settings(NamedTuple):
"""
Settings for the experiments that can be opted into.
"""
experiments: Dict[str, Experiment] = {}
class ColorFormatter(logging.Formatter):
"""Color codes the log messages based on the log level"""
@ -231,85 +276,180 @@ jobs:
return branch.split("/")[0] in {"main", "nightly", "release", "landchecks"}
def get_fleet(rollout_state: str, workflow_requestors: Iterable[str]) -> str:
"""
Determines if the job should run on the LF fleet or the Meta fleet
Returns:
The appropriate label prefix for the runner, corresponding to the fleet to use.
This gets prefixed to the very start of the runner label.
"""
def load_yaml(yaml_text: str) -> Any:
try:
if rollout_state[0] == "!":
log.info("LF Workflows are disabled for everyone. Using meta runners.")
return WORKFLOW_LABEL_META
elif rollout_state[0] == "*":
log.info("LF Workflows are enabled for everyone. Using LF runners.")
return WORKFLOW_LABEL_LF
else:
all_opted_in_users = {
usr_raw.strip("\n\t@ ").split(",")[0]
for usr_raw in rollout_state.split()
}
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
if opted_in_requestors:
log.info(
f"LF Workflows are enabled for {', '.join(opted_in_requestors)}. Using LF runners."
)
return WORKFLOW_LABEL_LF
else:
log.info(
f"LF Workflows are disabled for {', '.join(workflow_requestors)}. Using meta runners."
)
return WORKFLOW_LABEL_META
except Exception as e:
log.error(
f"Failed to get determine workflow type. Falling back to meta runners. Exception: {e}"
)
return WORKFLOW_LABEL_META
data = yaml.safe_load(yaml_text)
return data
except yaml.YAMLError as exc:
log.exception("Error loading YAML")
raise
def get_optin_feature(
rollout_state: str, workflow_requestors: Iterable[str], feature: str, fallback: str
def extract_settings_user_opt_in_from_text(rollout_state: str) -> Tuple[str, str]:
"""
Extracts the text with settings, if any, and the opted in users from the rollout state.
If the issue body contains "---" then the text above that is the settings
and the text below is the list of opted in users.
If it doesn't contain "---" then the settings are empty and the rest is the users.
"""
rollout_state_parts = rollout_state.split("---")
if len(rollout_state_parts) >= 2:
return rollout_state_parts[0], rollout_state_parts[1]
else:
return "", rollout_state
class UserOptins(Dict[str, List[str]]):
"""
Dictionary of users with a list of features they have opted into
"""
def parse_user_opt_in_from_text(user_optin_text: str) -> UserOptins:
"""
Parse the user opt-in text into a key value pair of username and the list of features they have opted into
Users are GitHub usernames with the @ prefix. Each user is also a comma-separated list of features/experiments to enable.
- Example line: "@User1,lf,split_build"
- A "#" prefix indicates the user is opted out of all experiments
"""
optins = UserOptins()
for user in user_optin_text.split("\n"):
user = user.strip("\r\n\t -")
if not user or not user.startswith("@"):
# Not a valid user. Skip
continue
if user:
usr_name = user.split(",")[0].strip("@")
optins[usr_name] = [exp.strip(" ") for exp in user.split(",")[1:]]
return optins
def parse_settings_from_text(settings_text: str) -> Settings:
"""
Parse the experiments from the issue body into a list of ExperimentSettings
"""
try:
if settings_text:
# Escape the backtick as well so that we can have the settings in a code block on the GH issue
# for easy reading
# Note: Using ascii for the backtick so that the cat step in _runner-determinator.yml doesn't choke on
# the backtick character in shell commands.
backtick = chr(96) # backtick character
settings_text = settings_text.strip(f"\r\n\t{backtick} ")
settings = load_yaml(settings_text)
# For now we just load experiments. We can expand this if/when we add more settings
experiments = {}
for exp_name, exp_settings in settings.get(SETTING_EXPERIMENTS).items():
valid_settings = {}
for setting in exp_settings:
if setting not in Experiment._fields:
log.warning(
f"Unexpected setting in experiment: {setting} = {exp_settings[setting]}"
)
else:
valid_settings[setting] = exp_settings[setting]
experiments[exp_name] = Experiment(**valid_settings)
return Settings(experiments)
except Exception:
log.exception("Failed to parse settings")
return Settings()
def parse_settings(rollout_state: str) -> Settings:
"""
Parse settings, if any, from the rollout state.
If the issue body contains "---" then the text above that is the settings
and the text below is the list of opted in users.
If it doesn't contain "---" then the settings are empty and the default values are used.
"""
settings_text, _ = extract_settings_user_opt_in_from_text(rollout_state)
return parse_settings_from_text(settings_text)
def parse_users(rollout_state: str) -> UserOptins:
"""
Parse users from the rollout state.
"""
_, users_text = extract_settings_user_opt_in_from_text(rollout_state)
return parse_user_opt_in_from_text(users_text)
def is_user_opted_in(user: str, user_optins: UserOptins, experiment_name: str) -> bool:
"""
Check if a user is opted into an experiment
"""
return experiment_name in user_optins.get(user, [])
def get_runner_prefix(
rollout_state: str, workflow_requestors: Iterable[str], is_canary: bool = False
) -> str:
"""
Used to dynamically opt in jobs to specific runner-type variants.
settings = parse_settings(rollout_state)
user_optins = parse_users(rollout_state)
Returns:
The runner-type's variant name if the user has opted in to the feature, otherwise returns an empty string.
This variant name is prefixed to the runner-type in the label.
"""
try:
userlist = {u.lstrip("#").strip("\n\t@ ") for u in rollout_state.split()}
all_opted_in_users = set()
for user in userlist:
for i in user.split(","):
if i == feature:
all_opted_in_users.add(user.split(",")[0])
opted_in_requestors = {
usr for usr in workflow_requestors if usr in all_opted_in_users
}
fleet_prefix = ""
prefixes = []
for experiment_name, experiment_settings in settings.experiments.items():
enabled = False
if opted_in_requestors:
# Is any workflow_requestor opted in to this experiment?
opted_in_users = [
requestor
for requestor in workflow_requestors
if is_user_opted_in(requestor, user_optins, experiment_name)
]
if opted_in_users:
log.info(
f"Feature {feature} is enabled for {', '.join(opted_in_requestors)}. Using feature {feature}."
f"{', '.join(opted_in_users)} have opted into experiment {experiment_name}."
)
return feature
else:
log.info(
f"Feature {feature} is disabled for {', '.join(workflow_requestors)}. Using fallback \"{fallback}\"."
)
return fallback
enabled = True
elif experiment_settings.rollout_perc:
# If no user is opted in, then we randomly enable the experiment based on the rollout percentage
if random.uniform(0, 100) <= experiment_settings.rollout_perc:
log.info(
f"Based on rollout percentage of {experiment_settings.rollout_perc}%, enabling experiment {experiment_name}."
)
enabled = True
except Exception as e:
if enabled:
label = experiment_name
if experiment_name == LF_FLEET_EXPERIMENT:
# We give some special treatment to the "lf" experiment since determines the fleet we use
# - If it's enabled, then we always list it's prefix first
# - If we're in the canary branch, then we append ".c" to the lf prefix
if is_canary:
label += CANARY_FLEET_SUFFIX
fleet_prefix = label
else:
prefixes.append(label)
if len(prefixes) > 1:
log.error(
f'Failed to determine if user has opted-in to feature {feature}. Using fallback "{fallback}". Exception: {e}'
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:])}"
)
return fallback
prefixes = prefixes[:1]
# Fleet always comes first
if fleet_prefix:
prefixes.insert(0, fleet_prefix)
return ".".join(prefixes) + "." if prefixes else ""
def get_rollout_state_from_issue(github_token: str, repo: str, issue_num: int) -> str:
@ -327,9 +467,10 @@ jobs:
args = parse_args()
if args.github_ref_type == "branch" and is_exception_branch(args.github_branch):
log.info(f"Exception branch: '{args.github_branch}', using meta runners")
label_type = WORKFLOW_LABEL_META
runner_ami = RUNNER_AMI_LEGACY
log.info(
f"Exception branch: '{args.github_branch}', using Meta runners and no experiments."
)
runner_label_prefix = DEFAULT_LABEL_PREFIX
else:
try:
rollout_state = get_rollout_state_from_issue(
@ -344,35 +485,18 @@ jobs:
args.github_branch,
)
label_type = get_fleet(
rollout_state,
(
args.github_issue_owner,
username,
),
)
runner_ami = get_optin_feature(
rollout_state=rollout_state,
workflow_requestors=(
args.github_issue_owner,
username,
),
feature=RUNNER_AMI_AMZ2023,
fallback=RUNNER_AMI_LEGACY,
is_canary = args.github_repo == "pytorch/pytorch-canary"
runner_label_prefix = get_runner_prefix(
rollout_state, (args.github_issue_owner, username), is_canary
)
except Exception as e:
log.error(
f"Failed to get issue. Falling back to meta runners. Exception: {e}"
f"Failed to get issue. Defaulting to Meta runners and no experiments. Exception: {e}"
)
label_type = WORKFLOW_LABEL_META
runner_ami = RUNNER_AMI_LEGACY
# For Canary builds use canary runners
if args.github_repo == "pytorch/pytorch-canary" and label_type == WORKFLOW_LABEL_LF:
label_type = WORKFLOW_LABEL_LF_CANARY
set_github_output(GH_OUTPUT_KEY_LABEL_TYPE, label_type)
set_github_output(GH_OUTPUT_KEY_AMI, runner_ami)
set_github_output(GH_OUTPUT_KEY_LABEL_TYPE, runner_label_prefix)
if __name__ == "__main__":