def get_attribution_dataset_info(config: Dict[str, Any], dataset_id: str,
logger: logging.Logger) -> str:
client = PCGraphAPIClient(config, logger)
return json.loads(
client.get_attribution_dataset_info(
dataset_id,
[DATASETS_INFORMATION],
).text)
def _get_attribution_dataset_info(client: PCGraphAPIClient, dataset_id: str,
logger: logging.Logger) -> Any:
return json.loads(
client.get_attribution_dataset_info(
dataset_id,
[DATASETS_INFORMATION],
).text)
def _check_versions(
cell_obj_instances: Dict[str, Dict[str, Dict[str, Any]]],
config: Dict[str, Any],
client: PCGraphAPIClient,
) -> None:
"""Checks that the publisher version (graph api) and the partner version (config.yml) are the same
Arguments:
cell_obj_instances: theoretically is dict mapping cell->obj->instance.
config: The dict representation of a config.yml file
client: Interface for submitting graph API requests
Raises:
IncorrectVersionError: the publisher and partner are running with different versions
"""
config_tier = get_tier(config)
for cell_id in cell_obj_instances:
for objective_id in cell_obj_instances[cell_id]:
instance_data = cell_obj_instances[cell_id][objective_id]
instance_id = instance_data["instance_id"]
# if there is no tier for some reason (e.g. old study?), let's just assume
# the tier is correct
tier_str = json.loads(client.get_instance(instance_id).text).get("tier")
if tier_str:
expected_tier = PCSTier.from_str(tier_str)
if expected_tier is not config_tier:
raise IncorrectVersionError.make_error(
instance_id, expected_tier, config_tier
)
def test_get_graph_api_token_dict_and_env(self) -> None:
expected_token = "from_dict"
with patch.dict("os.environ", {FBPCS_GRAPH_API_TOKEN: "from_env"}):
config = {"graphapi": {"access_token": expected_token}}
actual_token = PCGraphAPIClient(config,
self.mock_logger).access_token
self.assertEqual(expected_token, actual_token)
def _create_instance_retry(
client: PCGraphAPIClient,
study_id: str,
cell_id: str,
objective_id: str,
logger: logging.Logger,
) -> str:
tries = 0
while tries < CREATE_INSTANCE_TRIES:
tries += 1
try:
instance_id = json.loads(
client.create_instance(
study_id, {"cell_id": cell_id, "objective_id": objective_id}
).text
)["id"]
logger.info(
f"Created instance {instance_id} for cell {cell_id} and objective {objective_id}"
)
return instance_id
except GraphAPIGenericException as err:
if tries >= CREATE_INSTANCE_TRIES:
logger.error(
f"Error: Instance not created for cell {cell_id} and {objective_id}"
)
raise err
logger.info(
f"Instance not created for cell {cell_id} and {objective_id}. Retrying:"
)
return "" # this is to make pyre happy
def test_get_graph_api_token_from_env_config_no_field(self) -> None:
expected_token = "from_env"
with patch.dict("os.environ", {FBPCS_GRAPH_API_TOKEN: expected_token}):
config = {"graphapi": {"random_field": "not_a_token"}}
actual_token = PCGraphAPIClient(config,
self.mock_logger).access_token
self.assertEqual(expected_token, actual_token)
def _get_study_data(study_id: str, client: PCGraphAPIClient) -> Any:
return json.loads(
client.get_study_data(
study_id,
[
TYPE,
START_TIME,
OBSERVATION_END_TIME,
OBJECTIVES,
OPP_DATA_INFORMATION,
INSTANCES,
],
).text
)
def run_instance(
*,
config: Dict[str, Any],
instance_id: str,
input_path: str,
num_mpc_containers: int,
num_pid_containers: int,
stage_flow: Type[PrivateComputationBaseStageFlow],
logger: logging.Logger,
game_type: PrivateComputationGameType,
attribution_rule: Optional[AttributionRule] = None,
aggregation_type: Optional[AggregationType] = None,
concurrency: Optional[int] = None,
num_files_per_mpc_container: Optional[int] = None,
k_anonymity_threshold: Optional[int] = None,
num_tries: Optional[int] = 2, # this is number of tries per stage
dry_run: Optional[bool] = False,
result_visibility: Optional[ResultVisibility] = None,
) -> None:
num_tries = num_tries if num_tries is not None else MAX_TRIES
if num_tries < MIN_TRIES or num_tries > MAX_TRIES:
raise PCStudyValidationException(
"Number of retries not allowed",
f"num_tries must be between {MIN_TRIES} and {MAX_TRIES}.",
)
client = PCGraphAPIClient(config, logger)
instance_runner = PLInstanceRunner(
config,
instance_id,
input_path,
num_mpc_containers,
num_pid_containers,
logger,
client,
num_tries,
game_type,
dry_run,
stage_flow,
attribution_rule,
aggregation_type,
concurrency,
num_files_per_mpc_container,
k_anonymity_threshold,
result_visibility,
)
logger.info(
f"Running private {game_type.name.lower()} for instance {instance_id}")
instance_runner.run()
def _create_new_instance(
dataset_id: str,
timestamp: int,
attribution_rule: str,
client: PCGraphAPIClient,
logger: logging.Logger,
) -> str:
instance_id = json.loads(
client.create_pa_instance(
dataset_id,
timestamp,
attribution_rule,
2,
).text)["id"]
logger.info(
f"Created instance {instance_id} for dataset {dataset_id} and attribution rule {attribution_rule}"
)
return instance_id
def run_attribution(
config: Dict[str, Any],
dataset_id: str,
input_path: str,
timestamp: str,
attribution_rule: AttributionRule,
aggregation_type: AggregationType,
concurrency: int,
num_files_per_mpc_container: int,
k_anonymity_threshold: int,
stage_flow: Type[PrivateComputationBaseStageFlow],
logger: logging.Logger,
num_tries: Optional[int] = 2, # this is number of tries per stage
) -> None:
## Step 1: Validation. Function arguments and for private attribution run.
# obtain the values in the dataset info vector.
client = PCGraphAPIClient(config, logger)
datasets_info = _get_attribution_dataset_info(client, dataset_id, logger)
datasets = datasets_info[DATASETS_INFORMATION]
matched_data = {}
attribution_rule_str = attribution_rule.name
attribution_rule_val = attribution_rule.value
instance_id = None
pacific_timezone = pytz.timezone("US/Pacific")
# Validate if input is datetime or timestamp
is_date_format = _iso_date_validator(timestamp)
if is_date_format:
dt = pacific_timezone.localize(datetime.strptime(
timestamp, "%Y-%m-%d"))
else:
dt = datetime.fromtimestamp(int(timestamp), tz=timezone.utc)
# Compute the argument after the timestamp has been input
dt_arg = int(datetime.timestamp(dt))
# Verify that input has matching dataset info:
# a. attribution rule
# b. timestamp
if len(datasets) == 0:
raise ValueError("Dataset for given parameters and dataset invalid")
for data in datasets:
if data["key"] == attribution_rule_str:
matched_attr = data["value"]
for m_data in matched_attr:
m_time = dateutil.parser.parse(m_data[TIMESTAMP])
if m_time == dt:
matched_data = m_data
break
if len(matched_data) == 0:
raise ValueError("No dataset matching to the information provided")
# Step 2: Validate what instances need to be created vs what already exist
# Conditions for retry:
# 1. Not in a terminal status
# 2. Instance has been created > 1d ago
dataset_instance_data = _get_existing_pa_instances(client, dataset_id)
existing_instances = dataset_instance_data["data"]
for inst in existing_instances:
inst_time = dateutil.parser.parse(inst[TIMESTAMP])
creation_time = dateutil.parser.parse(inst[CREATED_TIME])
exp_time = datetime.now(tz=timezone.utc) - timedelta(days=1)
expired = exp_time > creation_time
if (inst[ATTRIBUTION_RULE] == attribution_rule_val and inst_time == dt
and inst[STATUS] not in TERMINAL_STATUSES and not expired):
instance_id = inst["id"]
break
if instance_id is None:
instance_id = _create_new_instance(
dataset_id,
int(dt_arg),
attribution_rule_val,
client,
logger,
)
instance_data = _get_pa_instance_info(client, instance_id, logger)
_check_version(instance_data, config)
num_pid_containers = instance_data[NUM_SHARDS]
num_mpc_containers = instance_data[NUM_CONTAINERS]
## Step 3. Run Instances. Run maximum number of instances in parallel
logger.info(f"Start running instance {instance_id}.")
instance_parameters = {
"config": config,
"instance_id": instance_id,
"input_path": input_path,
"num_mpc_containers": num_mpc_containers,
"num_pid_containers": num_pid_containers,
"stage_flow": stage_flow,
"logger": logger,
"game_type": PrivateComputationGameType.ATTRIBUTION,
"attribution_rule": attribution_rule,
"aggregation_type": AggregationType.MEASUREMENT,
"concurrency": concurrency,
"num_files_per_mpc_container": num_files_per_mpc_container,
"k_anonymity_threshold": k_anonymity_threshold,
"num_tries": num_tries,
}
run_instance(**instance_parameters)
logger.info(f"Finished running instances {instance_id}.")
def _get_existing_pa_instances(client: PCGraphAPIClient,
dataset_id: str) -> Any:
return json.loads(client.get_existing_pa_instances(dataset_id).text)
def _get_pa_instance_info(client: PCGraphAPIClient, instance_id: str,
logger: logging.Logger) -> Any:
return json.loads(client.get_instance(instance_id).text)
def run_study(
config: Dict[str, Any],
study_id: str,
objective_ids: List[str],
input_paths: List[str],
logger: logging.Logger,
stage_flow: Type[PrivateComputationBaseStageFlow],
num_tries: Optional[int] = 2, # this is number of tries per stage
dry_run: Optional[bool] = False, # if set to true, it will only run one stage
result_visibility: Optional[ResultVisibility] = None,
) -> None:
## Step 1: Validation. Function arguments and study metadata must be valid for private lift run.
_validate_input(objective_ids, input_paths)
# obtain study information
client = PCGraphAPIClient(config, logger)
study_data = _get_study_data(study_id, client)
# Verify study can run private lift:
_verify_study_type(study_data)
# verify mpc objectives
_verify_mpc_objs(study_data, objective_ids)
# verify study opp_data_information is non-empty
if OPP_DATA_INFORMATION not in study_data:
raise PCStudyValidationException(
f"Study {study_id} has no opportunity datasets.",
f"Check {study_id} study data to include {OPP_DATA_INFORMATION}",
)
## Step 2. Preparation. Find which cell-obj pairs should have new instances created for and which should use existing
## valid ones. If a valid instance exists for a particular cell-obj pair, use it. Otherwise, try to create one.
cell_obj_instance = _get_cell_obj_instance(
study_data,
objective_ids,
input_paths,
)
_print_json(
"Existing valid instances for cell-obj pairs", cell_obj_instance, logger
)
# create new instances
_create_new_instances(cell_obj_instance, study_id, client, logger)
_print_json("Instances to run for cell-obj pairs", cell_obj_instance, logger)
# create a dict with {instance_id, input_path} pairs
instances_input_path = _instance_to_input_path(cell_obj_instance)
_print_json(
"Instances will be calculated with corresponding input paths",
instances_input_path,
logger,
)
# check that the version in config.yml is same as from graph api
_check_versions(cell_obj_instance, config, client)
## Step 3. Run Instances. Run maximum number of instances in parallel
all_instance_ids = []
chunks = _get_chunks(instances_input_path, MAX_NUM_INSTANCES)
for chunk in chunks:
instance_ids = list(chunk.keys())
all_instance_ids.extend(instance_ids)
chunk_input_paths = list(map(lambda x: x["input_path"], chunk.values()))
chunk_num_shards = list(map(lambda x: x["num_shards"], chunk.values()))
logger.info(f"Start running instances {instance_ids}.")
run_instances(
config,
instance_ids,
chunk_input_paths,
chunk_num_shards,
stage_flow,
logger,
num_tries,
dry_run,
result_visibility,
)
logger.info(f"Finished running instances {instance_ids}.")
## Step 4: Print out the initial and end states
new_cell_obj_instances = _get_cell_obj_instance(
_get_study_data(study_id, client), objective_ids, input_paths
)
_print_json(
"Pre-run statuses for instance of each cell-objective pair",
cell_obj_instance,
logger,
)
_print_json(
"Post-run statuses for instance of each cell-objective pair",
new_cell_obj_instances,
logger,
)
for instance_id in all_instance_ids:
if (
get_instance(config, instance_id, logger).status
is not PrivateComputationInstanceStatus.AGGREGATION_COMPLETED
):
raise OneCommandRunnerBaseException(
f"{instance_id=} FAILED.",
"Status is not aggregation completed",
"Check logs for more information",
)
def test_get_graph_api_token_no_field(self) -> None:
config = {"graphapi": {"random_field": "not_a_token"}}
with self.assertRaises(GraphAPITokenNotFound):
PCGraphAPIClient(config, self.mock_logger).access_token
def test_get_graph_api_token_no_token_todo(self) -> None:
config = {"graphapi": {"access_token": "TODO"}}
with self.assertRaises(GraphAPITokenNotFound):
PCGraphAPIClient(config, self.mock_logger).access_token
def test_get_graph_api_token_from_dict(self) -> None:
expected_token = "from_dict"
config = {"graphapi": {"access_token": expected_token}}
actual_token = PCGraphAPIClient(config, self.mock_logger).access_token
self.assertEqual(expected_token, actual_token)
