From 14852e949ac0ac9e499dba61af0ee1a104759978 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Fri, 22 Sep 2023 18:16:57 +0200 Subject: [PATCH 01/46] work on configurable prefetched dataloader --- .../criteo_1TB/pipelines/exp0_finetune.yml | 1 + black.toml | 1 + modyn/config/schema/pipeline-schema.yaml | 4 + modyn/protos/trainer_server.proto | 5 +- modyn/supervisor/internal/grpc_handler.py | 6 + .../internal/data/test_data_utils.py | 2 +- .../internal/data/test_online_dataset.py | 42 ++++- .../data/test_per_class_online_dataset.py | 4 + .../internal/trainer/test_pytorch_trainer.py | 1 + .../internal/dataset/data_utils.py | 5 + .../internal/dataset/online_dataset.py | 120 +++++++++++--- .../dataset/per_class_online_dataset.py | 2 + .../grpc/generated/trainer_server_pb2.py | 36 ++--- .../grpc/generated/trainer_server_pb2.pyi | 5 +- .../internal/trainer/pytorch_trainer.py | 1 + .../internal/utils/training_info.py | 1 + plotting/common/common.py | 151 ++++++++++++++++++ plotting/system/next_batch_distribution.py | 43 +++++ setup.cfg | 3 +- 19 files changed, 383 insertions(+), 50 deletions(-) create mode 100644 plotting/common/common.py create mode 100644 plotting/system/next_batch_distribution.py diff --git a/benchmark/criteo_1TB/pipelines/exp0_finetune.yml b/benchmark/criteo_1TB/pipelines/exp0_finetune.yml index 0df038fb6..3f0916f85 100644 --- a/benchmark/criteo_1TB/pipelines/exp0_finetune.yml +++ b/benchmark/criteo_1TB/pipelines/exp0_finetune.yml @@ -45,6 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 + prefetched_partitions: 4 use_previous_model: True initial_model: random initial_pass: diff --git a/black.toml b/black.toml index b42befc20..0be9cced6 100644 --- a/black.toml +++ b/black.toml @@ -7,4 +7,5 @@ extend-exclude = """\ .*/*\\_pb2.py|\ .*/generated/.*\ .*/benchmark/.*\ + .*/plotting/.*\ """ diff --git a/modyn/config/schema/pipeline-schema.yaml b/modyn/config/schema/pipeline-schema.yaml index 7984fb27e..8b1bd617d 100644 --- a/modyn/config/schema/pipeline-schema.yaml +++ b/modyn/config/schema/pipeline-schema.yaml @@ -48,6 +48,10 @@ properties: type: number description: | The number of epochs per trigger. Defaults to 1, if not given. + prefetched_partitions: + type: number + description: | + The number of partitions that are prefetched per DataLoader worker. Defaults to 1, if not given. device: type: string description: | diff --git a/modyn/protos/trainer_server.proto b/modyn/protos/trainer_server.proto index 9f52f7f6d..12c68e492 100644 --- a/modyn/protos/trainer_server.proto +++ b/modyn/protos/trainer_server.proto @@ -53,8 +53,9 @@ message StartTrainingRequest { PythonString label_transformer = 19; JsonString grad_scaler_configuration = 20; int32 epochs_per_trigger = 21; - optional int32 seed = 22; - optional PythonString tokenizer = 23; + int32 prefetched_partitions = 22; + optional int32 seed = 23; + optional PythonString tokenizer = 24; } message StartTrainingResponse { diff --git a/modyn/supervisor/internal/grpc_handler.py b/modyn/supervisor/internal/grpc_handler.py index c1328461b..01f83ee75 100644 --- a/modyn/supervisor/internal/grpc_handler.py +++ b/modyn/supervisor/internal/grpc_handler.py @@ -300,6 +300,11 @@ def start_training( else: epochs_per_trigger = 1 + if "prefetched_partitions" in pipeline_config["training"]: + prefetched_partitions = pipeline_config["training"]["prefetched_partitions"] + else: + prefetched_partitions = 1 + if "seed" in pipeline_config["training"]: seed = pipeline_config["training"]["seed"] else: @@ -366,6 +371,7 @@ def start_training( "lr_scheduler": TrainerServerJsonString(value=json.dumps(lr_scheduler_configs)), "grad_scaler_configuration": TrainerServerJsonString(value=json.dumps(grad_scaler_config)), "epochs_per_trigger": epochs_per_trigger, + "prefetched_partitions": prefetched_partitions, "seed": seed, "tokenizer": PythonString(value=tokenizer) if tokenizer is not None else None, } diff --git a/modyn/tests/trainer_server/internal/data/test_data_utils.py b/modyn/tests/trainer_server/internal/data/test_data_utils.py index 8a2729d67..440b807cf 100644 --- a/modyn/tests/trainer_server/internal/data/test_data_utils.py +++ b/modyn/tests/trainer_server/internal/data/test_data_utils.py @@ -30,7 +30,7 @@ def test_prepare_dataloaders( test_weights, test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector ): train_dataloader, _ = prepare_dataloaders( - 1, 1, "MNIST", 4, 128, get_mock_bytes_parser(), [], "", "", 42, None, None + 1, 1, "MNIST", 4, 128, get_mock_bytes_parser(), [], "", "", 42, 5, None, None ) assert train_dataloader.num_workers == 4 diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index 7a391290a..a90498795 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -67,6 +67,7 @@ def test_invalid_bytes_parser(test_weights, test_grpc_connection_established): training_id=42, tokenizer=None, log_path=None, + prefetched_partitions=1, )._init_transforms() with pytest.raises(ValueError): @@ -81,6 +82,7 @@ def test_invalid_bytes_parser(test_weights, test_grpc_connection_established): training_id=42, tokenizer="", log_path=None, + prefetched_partitions=1, )._init_transforms() @@ -104,6 +106,7 @@ def test_init(test_insecure_channel, test_grpc_connection_established, test_grpc training_id=42, tokenizer=None, log_path=None, + prefetched_partitions=1, ) assert online_dataset._pipeline_id == 1 assert online_dataset._trigger_id == 1 @@ -136,6 +139,7 @@ def test_get_keys_and_weights_from_selector( "training_id": 42, "tokenizer": None, "log_path": None, + "prefetched_partitions": 1, } online_dataset = OnlineDataset(**kwargs) @@ -170,6 +174,7 @@ def test_get_data_from_storage( training_id=42, tokenizer=None, log_path=None, + prefetched_partitions=0, ) online_dataset._init_grpc() assert online_dataset._get_data_from_storage(list(range(10))) == ( @@ -229,6 +234,7 @@ def test_deserialize_torchvision_transforms( training_id=42, tokenizer=None, log_path=None, + prefetched_partitions=1, ) online_dataset._bytes_parser_function = bytes_parser_function online_dataset._setup_composed_transform() @@ -238,6 +244,7 @@ def test_deserialize_torchvision_transforms( assert transform1.__dict__ == transform2.__dict__ +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -258,6 +265,7 @@ def test_dataset_iter( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -268,6 +276,7 @@ def test_dataset_iter( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -278,6 +287,7 @@ def test_dataset_iter( assert [x[2] for x in all_data] == [1] * 10 +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -298,6 +308,7 @@ def test_dataset_iter_with_parsing( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -308,6 +319,7 @@ def test_dataset_iter_with_parsing( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -318,6 +330,7 @@ def test_dataset_iter_with_parsing( assert [x[2] for x in all_data] == [1] * 10 +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -338,6 +351,7 @@ def test_dataloader_dataset( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -348,6 +362,7 @@ def test_dataloader_dataset( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -359,6 +374,7 @@ def test_dataloader_dataset( assert torch.equal(batch[2], torch.ones(4, dtype=torch.float64)) +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", WeightedMockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -379,6 +395,7 @@ def test_dataloader_dataset_weighted( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -389,6 +406,7 @@ def test_dataloader_dataset_weighted( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -401,6 +419,7 @@ def test_dataloader_dataset_weighted( assert torch.equal(batch[3], 2 * torch.ones(4, dtype=torch.float64)) +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -419,6 +438,7 @@ def test_dataloader_dataset_multi_worker( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): if platform.system() == "Darwin": # On macOS, spawn is the default, which loses the mocks @@ -434,6 +454,7 @@ def test_dataloader_dataset_multi_worker( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -463,6 +484,7 @@ def test_init_grpc(test_insecure_channel, test_grpc_connection_established, test storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=1, tokenizer=None, log_path=None, ) @@ -485,7 +507,9 @@ def test_init_grpc(test_insecure_channel, test_grpc_connection_established, test @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) def test_init_transforms( - test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector + test_insecure_channel, + test_grpc_connection_established, + test_grpc_connection_established_selector, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -496,6 +520,7 @@ def test_init_transforms( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=1, tokenizer=None, log_path=None, ) @@ -513,6 +538,7 @@ def test_init_transforms( tv_ds.assert_called_once() +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -549,6 +575,7 @@ def test_iter_multi_partition( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -559,6 +586,7 @@ def test_iter_multi_partition( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -573,6 +601,7 @@ def test_iter_multi_partition( assert idx == 15 +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", WeightedMockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -609,6 +638,7 @@ def test_iter_multi_partition_weighted( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -619,6 +649,7 @@ def test_iter_multi_partition_weighted( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -635,6 +666,7 @@ def test_iter_multi_partition_weighted( assert idx == 15 +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -671,6 +703,7 @@ def test_iter_multi_partition_cross( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -681,6 +714,7 @@ def test_iter_multi_partition_cross( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -709,6 +743,7 @@ def test_iter_multi_partition_cross( assert idx == 10 +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -738,6 +773,7 @@ def test_iter_multi_partition_multi_workers( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): if platform.system() == "Darwin": # On macOS, spawn is the default, which loses the mocks @@ -753,6 +789,7 @@ def test_iter_multi_partition_multi_workers( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -766,6 +803,7 @@ def test_iter_multi_partition_multi_workers( assert idx == 7 +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -786,6 +824,7 @@ def test_multi_epoch_dataloader_dataset( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selecotr, + prefetched_partitions, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -796,6 +835,7 @@ def test_multi_epoch_dataloader_dataset( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, + prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) diff --git a/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py index 5f554fdd3..5c9cf10d2 100644 --- a/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py @@ -3,6 +3,7 @@ from unittest.mock import patch import grpc +import pytest import torch from modyn.selector.internal.grpc.generated.selector_pb2 import SamplesResponse, UsesWeightsResponse from modyn.storage.internal.grpc.generated.storage_pb2 import GetResponse @@ -34,6 +35,7 @@ def Get(self, request): # pylint: disable=invalid-name ) +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -56,6 +58,7 @@ def test_dataloader_dataset( test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector, + prefetched_partitions, ): online_dataset = PerClassOnlineDataset( pipeline_id=1, @@ -67,6 +70,7 @@ def test_dataloader_dataset( selector_address="localhost:1234", training_id=42, initial_filtered_label=0, + prefetched_partitions=prefetched_partitions, tokenizer=None, ) dataloader = torch.utils.data.DataLoader(online_dataset, batch_size=4) diff --git a/modyn/tests/trainer_server/internal/trainer/test_pytorch_trainer.py b/modyn/tests/trainer_server/internal/trainer/test_pytorch_trainer.py index 9117b3f76..7a18fbb18 100644 --- a/modyn/tests/trainer_server/internal/trainer/test_pytorch_trainer.py +++ b/modyn/tests/trainer_server/internal/trainer/test_pytorch_trainer.py @@ -126,6 +126,7 @@ def mock_get_dataloaders( storage_address, selector_address, training_id, + prefetched_partitions, tokenizer, log_path, ): diff --git a/modyn/trainer_server/internal/dataset/data_utils.py b/modyn/trainer_server/internal/dataset/data_utils.py index a10545ed7..f03a16abe 100644 --- a/modyn/trainer_server/internal/dataset/data_utils.py +++ b/modyn/trainer_server/internal/dataset/data_utils.py @@ -8,6 +8,8 @@ logger = logging.getLogger(__name__) +# pylint: disable=too-many-locals + def prepare_dataloaders( pipeline_id: int, @@ -20,6 +22,7 @@ def prepare_dataloaders( storage_address: str, selector_address: str, training_id: int, + prefetched_partitions: int, tokenizer: Optional[str], log_path: Optional[pathlib.Path], ) -> tuple[torch.utils.data.DataLoader, Optional[torch.utils.data.DataLoader]]: @@ -52,6 +55,7 @@ def prepare_dataloaders( storage_address, selector_address, training_id, + prefetched_partitions, tokenizer, log_path, ) @@ -77,6 +81,7 @@ def prepare_per_class_dataloader_from_online_dataset( online_dataset._selector_address, online_dataset._training_id, initial_filtered_label, + online_dataset._prefetched_partitions, online_dataset._tokenizer_name, ) return torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers) diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index c97becff2..90bb00aca 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -3,6 +3,7 @@ import logging import os import pathlib +import threading from typing import Any, Callable, Generator, Optional, Tuple, Union import grpc @@ -40,6 +41,7 @@ def __init__( storage_address: str, selector_address: str, training_id: int, + prefetched_partitions: int, tokenizer: Optional[str], log_path: Optional[pathlib.Path], ): @@ -48,6 +50,7 @@ def __init__( self._training_id = training_id self._dataset_id = dataset_id self._first_call = True + self._prefetched_partitions = prefetched_partitions self._bytes_parser = bytes_parser self._serialized_transforms = serialized_transforms @@ -65,6 +68,11 @@ def __init__( self._log: dict[str, Any] = {"partitions": {}} self._sw = Stopwatch() + self._data_threads: dict[int, threading.Thread] = {} + self._pref_started: dict[int, bool] = {} + self._thread_data_container: dict[int, dict[str, Any]] = {} + self._next_partition_to_fetch = 0 + if log_path is None: logger.warning("Did not provide log path for OnlineDataset - logging disabled.") @@ -134,23 +142,24 @@ def _info(self, msg: str, worker_id: Optional[int]) -> None: # pragma: no cover def _debug(self, msg: str, worker_id: Optional[int]) -> None: # pragma: no cover logger.debug(f"[Training {self._training_id}][PL {self._pipeline_id}][Worker {worker_id}] {msg}") - def _get_data( - self, worker_id: int, partition_id: int - ) -> tuple[list[int], list[bytes], list[int], Optional[list[float]]]: + def _get_data(self, data_container: dict, worker_id: int, partition_id: int) -> None: get_data_log = {} - self._sw.start("GetKeysAndWeights", overwrite=True) + self._sw.start(f"GetKeysAndWeightsPart{partition_id}", overwrite=True) keys, weights = self._key_source.get_keys_and_weights(worker_id, partition_id) - get_data_log["get_keys_and_weights"] = self._sw.stop("GetKeysAndWeights") + get_data_log["get_keys_and_weights"] = self._sw.stop(f"GetKeysAndWeightsPart{partition_id}") get_data_log["num_items"] = len(keys) self._info("Getting data from storage", worker_id) - self._sw.start("GetData", overwrite=True) + self._sw.start(f"GetDataPart{partition_id}", overwrite=True) data, labels = self._get_data_from_storage(keys) - get_data_log["get_data"] = self._sw.stop("GetData") + get_data_log["get_data"] = self._sw.stop(f"GetDataPart{partition_id}") self._log["partitions"][str(partition_id)] = get_data_log - return keys, data, labels, weights + data_container["data"] = data + data_container["keys"] = keys + data_container["labels"] = labels + data_container["weights"] = weights def _get_data_iterator( self, keys: list[int], data: list[bytes], labels: list[int], weights: Optional[list[float]] @@ -200,11 +209,59 @@ def _persist_log(self, worker_id: int) -> None: log_file = f"{self._log_path / str(worker_id)}.log" self._log["transform"] = self._sw.measurements.get("transform", 0) + self._log["wait_for_later_partitions"] = self._sw.measurements.get("wait_for_later_partitions", 0) + self._log["wait_for_initial_partition"] = self._sw.measurements.get("wait_for_initial_partition", 0) with open(log_file, "w", encoding="utf-8") as logfile: json.dump(self._log, logfile) - # pylint: disable=too-many-locals, too-many-branches + def _prefetch_partition(self, worker_id: int) -> None: + if self._prefetched_partitions < 1 or self._next_partition_to_fetch >= self._num_partitions: + return # Prefetching disabled or nothing more to prefetch + + assert self._next_partition_to_fetch >= 0 + assert ( + self._next_partition_to_fetch not in self._data_threads + ), f"Prefetching for partition {self._next_partition_to_fetch} has already been started" + + self._thread_data_container[self._next_partition_to_fetch]: dict[str, Any] = {} + + self._data_threads[self._next_partition_to_fetch] = threading.Thread( + target=self._get_data, + args=(self._thread_data_container[self._next_partition_to_fetch], worker_id, self._next_partition_to_fetch), + ) + + self._data_threads[self._next_partition_to_fetch].start() + self._pref_started[self._next_partition_to_fetch] = True + + self._next_partition_to_fetch += 1 + + def _wait_for_partition( + self, worker_id: int, partition_id: int + ) -> tuple[list[int], list[bytes], list[int], Optional[list[float]]]: + container: dict[str, Any] = {} + + if self._prefetched_partitions < 1: + # Prefetching disabled + self._get_data(container, worker_id, partition_id) + else: + # Prefetching enabled + assert self._pref_started[partition_id], f"Prefetching for partition {partition_id} has not been started" + self._info(f"Joining thread for partition {partition_id}", worker_id) + self._data_threads[partition_id].join() + + container = self._thread_data_container[partition_id] + + assert "data" in container and "labels" in container and "keys" in container and "weights" in container + keys, data, labels, weights = (container["keys"], container["data"], container["labels"], container["weights"]) + container.clear() + del container + gc.collect() + + return keys, data, labels, weights + + # pylint: disable=too-many-locals, too-many-branches, too-many-statements + def __iter__(self) -> Generator: worker_info = get_worker_info() if worker_info is None: @@ -224,49 +281,60 @@ def __iter__(self) -> Generator: self._uses_weights = self._key_source.uses_weights() self._silence_pil() self._debug("gRPC initialized.", worker_id) - # Reinit logging and timetracking in this worker + # Reinit logging, timetracking in this worker self._log = {"partitions": {}} self._sw = Stopwatch() + # Always reinitialize these structures for prefetching (for multiple epochs) + self._data_threads: dict[int, threading.Thread] = {} + self._thread_data_container: dict[str, Any] = {} + self._pref_started: dict[int, bool] = {} + self._next_partition_to_fetch = 0 + assert self._transform is not None self._num_partitions = self._key_source.get_num_data_partitions() - self._info(f"Total number of partitions will be {self._num_partitions}", worker_id) + self._info( + f"Total number of partitions will be {self._num_partitions}. Prefetch factor={self._prefetched_partitions}", + worker_id, + ) self._log["num_partitions"] = self._num_partitions + self._prefetched_partitions = min(self._prefetched_partitions, self._num_partitions) + + for partition in range(self._prefetched_partitions): + self._prefetch_partition(worker_id) - keys, data, labels, weights = self._get_data(worker_id=worker_id, partition_id=0) + self._sw.start("wait_for_initial_partition", overwrite=True) + keys, data, labels, weights = self._wait_for_partition(worker_id, 0) + self._sw.stop("wait_for_initial_partition") for partition in range(self._num_partitions): self._persist_log(worker_id) num_samples_on_this_partition = len(keys) - # We (arbitrarily) fetch the next partition when we have seen 80% of the current partition - fetch_next_partition_idx = int(num_samples_on_this_partition * 0.8) - self._info(f"Train on partition {partition}, on {num_samples_on_this_partition} batches", worker_id) + # We (arbitrarily) prefetch the next partition when we have seen 70% of the current partition + fetch_next_partition_idx = int(num_samples_on_this_partition * 0.7) + + self._info(f"Train on partition {partition} ({num_samples_on_this_partition} samples)", worker_id) for idx, data_tuple in self._get_data_iterator(keys, data, labels, weights): key, sample, label, weight = self._unpack_data_tuple(data_tuple) if partition < self._num_partitions - 1 and idx == fetch_next_partition_idx: - # TODO(#175) in case this blocks training - new_keys, new_data, new_labels, new_weights = self._get_data( - worker_id=worker_id, partition_id=partition + 1 - ) + self._prefetch_partition(worker_id) data_tuple = self._get_data_tuple(key, sample, label, weight) - if data_tuple is not None: + if data_tuple is not None: # Can happen in PerClassDataset yield data_tuple - # this should mean we keep only two partitions in mem if partition < self._num_partitions - 1: del keys del data del labels del weights - keys, data, labels, weights = new_keys, new_data, new_labels, new_weights - del new_keys - del new_data - del new_labels - del new_weights + self._info(f"Partition {partition} completed, waiting for next partition", worker_id) + self._sw.start("wait_for_later_partitions", resume=True) + keys, data, labels, weights = self._wait_for_partition(worker_id, partition + 1) + self._sw.stop("wait_for_later_partitions") gc.collect() self._persist_log(worker_id) diff --git a/modyn/trainer_server/internal/dataset/per_class_online_dataset.py b/modyn/trainer_server/internal/dataset/per_class_online_dataset.py index 9413297a0..6a98b8b1b 100644 --- a/modyn/trainer_server/internal/dataset/per_class_online_dataset.py +++ b/modyn/trainer_server/internal/dataset/per_class_online_dataset.py @@ -20,6 +20,7 @@ def __init__( selector_address: str, training_id: int, initial_filtered_label: int, + prefetched_partitions: int, tokenizer: Optional[str], ): super().__init__( @@ -31,6 +32,7 @@ def __init__( storage_address, selector_address, training_id, + prefetched_partitions, tokenizer, None, ) diff --git a/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.py b/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.py index d02654560..bf91935c3 100644 --- a/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.py +++ b/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.py @@ -14,7 +14,7 @@ -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14trainer_server.proto\x12\x07trainer\"\x1b\n\nJsonString\x12\r\n\x05value\x18\x01 \x01(\t\"\x1d\n\x0cPythonString\x12\r\n\x05value\x18\x01 \x01(\t\"3\n\x04\x44\x61ta\x12\x12\n\ndataset_id\x18\x01 \x01(\t\x12\x17\n\x0fnum_dataloaders\x18\x02 \x01(\x05\"\x19\n\x17TrainerAvailableRequest\"-\n\x18TrainerAvailableResponse\x12\x11\n\tavailable\x18\x01 \x01(\x08\"F\n\x0e\x43heckpointInfo\x12\x1b\n\x13\x63heckpoint_interval\x18\x01 \x01(\x05\x12\x17\n\x0f\x63heckpoint_path\x18\x02 \x01(\t\"\xb9\x06\n\x14StartTrainingRequest\x12\x13\n\x0bpipeline_id\x18\x01 \x01(\x05\x12\x12\n\ntrigger_id\x18\x02 \x01(\x05\x12\x0e\n\x06\x64\x65vice\x18\x03 \x01(\t\x12\x0b\n\x03\x61mp\x18\x04 \x01(\x08\x12\x10\n\x08model_id\x18\x05 \x01(\t\x12\x30\n\x13model_configuration\x18\x06 \x01(\x0b\x32\x13.trainer.JsonString\x12\x1c\n\x14use_pretrained_model\x18\x07 \x01(\x08\x12\x1c\n\x14load_optimizer_state\x18\x08 \x01(\x08\x12\x1b\n\x13pretrained_model_id\x18\t \x01(\x05\x12\x12\n\nbatch_size\x18\n \x01(\x05\x12;\n\x1etorch_optimizers_configuration\x18\x0b \x01(\x0b\x32\x13.trainer.JsonString\x12\x17\n\x0ftorch_criterion\x18\x0c \x01(\t\x12\x31\n\x14\x63riterion_parameters\x18\r \x01(\x0b\x32\x13.trainer.JsonString\x12 \n\tdata_info\x18\x0e \x01(\x0b\x32\r.trainer.Data\x12\x30\n\x0f\x63heckpoint_info\x18\x0f \x01(\x0b\x32\x17.trainer.CheckpointInfo\x12+\n\x0c\x62ytes_parser\x18\x10 \x01(\x0b\x32\x15.trainer.PythonString\x12\x16\n\x0etransform_list\x18\x11 \x03(\t\x12)\n\x0clr_scheduler\x18\x12 \x01(\x0b\x32\x13.trainer.JsonString\x12\x30\n\x11label_transformer\x18\x13 \x01(\x0b\x32\x15.trainer.PythonString\x12\x36\n\x19grad_scaler_configuration\x18\x14 \x01(\x0b\x32\x13.trainer.JsonString\x12\x1a\n\x12\x65pochs_per_trigger\x18\x15 \x01(\x05\x12\x11\n\x04seed\x18\x16 \x01(\x05H\x00\x88\x01\x01\x12-\n\ttokenizer\x18\x17 \x01(\x0b\x32\x15.trainer.PythonStringH\x01\x88\x01\x01\x42\x07\n\x05_seedB\x0c\n\n_tokenizer\"F\n\x15StartTrainingResponse\x12\x18\n\x10training_started\x18\x01 \x01(\x08\x12\x13\n\x0btraining_id\x18\x02 \x01(\x05\",\n\x15TrainingStatusRequest\x12\x13\n\x0btraining_id\x18\x01 \x01(\x05\"\xa6\x03\n\x16TrainingStatusResponse\x12\r\n\x05valid\x18\x01 \x01(\x08\x12\x12\n\nis_running\x18\x02 \x01(\x08\x12\x13\n\x0bis_training\x18\x03 \x01(\x08\x12\x17\n\x0fstate_available\x18\x04 \x01(\x08\x12\x0f\n\x07\x62locked\x18\x05 \x01(\x08\x12 \n\x03log\x18\x06 \x01(\x0b\x32\x13.trainer.JsonString\x12\x16\n\texception\x18\x07 \x01(\tH\x00\x88\x01\x01\x12\x19\n\x0c\x62\x61tches_seen\x18\x08 \x01(\x03H\x01\x88\x01\x01\x12\x19\n\x0csamples_seen\x18\t \x01(\x03H\x02\x88\x01\x01\x12&\n\x19\x64ownsampling_batches_seen\x18\n \x01(\x03H\x03\x88\x01\x01\x12&\n\x19\x64ownsampling_samples_seen\x18\x0b \x01(\x03H\x04\x88\x01\x01\x42\x0c\n\n_exceptionB\x0f\n\r_batches_seenB\x0f\n\r_samples_seenB\x1c\n\x1a_downsampling_batches_seenB\x1c\n\x1a_downsampling_samples_seen\"-\n\x16StoreFinalModelRequest\x12\x13\n\x0btraining_id\x18\x01 \x01(\x05\"@\n\x17StoreFinalModelResponse\x12\x13\n\x0bvalid_state\x18\x01 \x01(\x08\x12\x10\n\x08model_id\x18\x02 \x01(\x05\",\n\x15GetLatestModelRequest\x12\x13\n\x0btraining_id\x18\x01 \x01(\x05\"A\n\x16GetLatestModelResponse\x12\x13\n\x0bvalid_state\x18\x01 \x01(\x08\x12\x12\n\nmodel_path\x18\x02 \x01(\t2\xc9\x03\n\rTrainerServer\x12Z\n\x11trainer_available\x12 .trainer.TrainerAvailableRequest\x1a!.trainer.TrainerAvailableResponse\"\x00\x12Q\n\x0estart_training\x12\x1d.trainer.StartTrainingRequest\x1a\x1e.trainer.StartTrainingResponse\"\x00\x12X\n\x13get_training_status\x12\x1e.trainer.TrainingStatusRequest\x1a\x1f.trainer.TrainingStatusResponse\"\x00\x12X\n\x11store_final_model\x12\x1f.trainer.StoreFinalModelRequest\x1a .trainer.StoreFinalModelResponse\"\x00\x12U\n\x10get_latest_model\x12\x1e.trainer.GetLatestModelRequest\x1a\x1f.trainer.GetLatestModelResponse\"\x00\x62\x06proto3') +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14trainer_server.proto\x12\x07trainer\"\x1b\n\nJsonString\x12\r\n\x05value\x18\x01 \x01(\t\"\x1d\n\x0cPythonString\x12\r\n\x05value\x18\x01 \x01(\t\"3\n\x04\x44\x61ta\x12\x12\n\ndataset_id\x18\x01 \x01(\t\x12\x17\n\x0fnum_dataloaders\x18\x02 \x01(\x05\"\x19\n\x17TrainerAvailableRequest\"-\n\x18TrainerAvailableResponse\x12\x11\n\tavailable\x18\x01 \x01(\x08\"F\n\x0e\x43heckpointInfo\x12\x1b\n\x13\x63heckpoint_interval\x18\x01 \x01(\x05\x12\x17\n\x0f\x63heckpoint_path\x18\x02 \x01(\t\"\xd8\x06\n\x14StartTrainingRequest\x12\x13\n\x0bpipeline_id\x18\x01 \x01(\x05\x12\x12\n\ntrigger_id\x18\x02 \x01(\x05\x12\x0e\n\x06\x64\x65vice\x18\x03 \x01(\t\x12\x0b\n\x03\x61mp\x18\x04 \x01(\x08\x12\x10\n\x08model_id\x18\x05 \x01(\t\x12\x30\n\x13model_configuration\x18\x06 \x01(\x0b\x32\x13.trainer.JsonString\x12\x1c\n\x14use_pretrained_model\x18\x07 \x01(\x08\x12\x1c\n\x14load_optimizer_state\x18\x08 \x01(\x08\x12\x1b\n\x13pretrained_model_id\x18\t \x01(\x05\x12\x12\n\nbatch_size\x18\n \x01(\x05\x12;\n\x1etorch_optimizers_configuration\x18\x0b \x01(\x0b\x32\x13.trainer.JsonString\x12\x17\n\x0ftorch_criterion\x18\x0c \x01(\t\x12\x31\n\x14\x63riterion_parameters\x18\r \x01(\x0b\x32\x13.trainer.JsonString\x12 \n\tdata_info\x18\x0e \x01(\x0b\x32\r.trainer.Data\x12\x30\n\x0f\x63heckpoint_info\x18\x0f \x01(\x0b\x32\x17.trainer.CheckpointInfo\x12+\n\x0c\x62ytes_parser\x18\x10 \x01(\x0b\x32\x15.trainer.PythonString\x12\x16\n\x0etransform_list\x18\x11 \x03(\t\x12)\n\x0clr_scheduler\x18\x12 \x01(\x0b\x32\x13.trainer.JsonString\x12\x30\n\x11label_transformer\x18\x13 \x01(\x0b\x32\x15.trainer.PythonString\x12\x36\n\x19grad_scaler_configuration\x18\x14 \x01(\x0b\x32\x13.trainer.JsonString\x12\x1a\n\x12\x65pochs_per_trigger\x18\x15 \x01(\x05\x12\x1d\n\x15prefetched_partitions\x18\x16 \x01(\x05\x12\x11\n\x04seed\x18\x17 \x01(\x05H\x00\x88\x01\x01\x12-\n\ttokenizer\x18\x18 \x01(\x0b\x32\x15.trainer.PythonStringH\x01\x88\x01\x01\x42\x07\n\x05_seedB\x0c\n\n_tokenizer\"F\n\x15StartTrainingResponse\x12\x18\n\x10training_started\x18\x01 \x01(\x08\x12\x13\n\x0btraining_id\x18\x02 \x01(\x05\",\n\x15TrainingStatusRequest\x12\x13\n\x0btraining_id\x18\x01 \x01(\x05\"\xa6\x03\n\x16TrainingStatusResponse\x12\r\n\x05valid\x18\x01 \x01(\x08\x12\x12\n\nis_running\x18\x02 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\x01(\x08\x12\x12\n\nmodel_path\x18\x02 \x01(\t2\xc9\x03\n\rTrainerServer\x12Z\n\x11trainer_available\x12 .trainer.TrainerAvailableRequest\x1a!.trainer.TrainerAvailableResponse\"\x00\x12Q\n\x0estart_training\x12\x1d.trainer.StartTrainingRequest\x1a\x1e.trainer.StartTrainingResponse\"\x00\x12X\n\x13get_training_status\x12\x1e.trainer.TrainingStatusRequest\x1a\x1f.trainer.TrainingStatusResponse\"\x00\x12X\n\x11store_final_model\x12\x1f.trainer.StoreFinalModelRequest\x1a .trainer.StoreFinalModelResponse\"\x00\x12U\n\x10get_latest_model\x12\x1e.trainer.GetLatestModelRequest\x1a\x1f.trainer.GetLatestModelResponse\"\x00\x62\x06proto3') _globals = globals() _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) @@ -35,21 +35,21 @@ _globals['_CHECKPOINTINFO']._serialized_start=220 _globals['_CHECKPOINTINFO']._serialized_end=290 _globals['_STARTTRAININGREQUEST']._serialized_start=293 - _globals['_STARTTRAININGREQUEST']._serialized_end=1118 - _globals['_STARTTRAININGRESPONSE']._serialized_start=1120 - _globals['_STARTTRAININGRESPONSE']._serialized_end=1190 - _globals['_TRAININGSTATUSREQUEST']._serialized_start=1192 - _globals['_TRAININGSTATUSREQUEST']._serialized_end=1236 - _globals['_TRAININGSTATUSRESPONSE']._serialized_start=1239 - _globals['_TRAININGSTATUSRESPONSE']._serialized_end=1661 - _globals['_STOREFINALMODELREQUEST']._serialized_start=1663 - _globals['_STOREFINALMODELREQUEST']._serialized_end=1708 - _globals['_STOREFINALMODELRESPONSE']._serialized_start=1710 - _globals['_STOREFINALMODELRESPONSE']._serialized_end=1774 - _globals['_GETLATESTMODELREQUEST']._serialized_start=1776 - _globals['_GETLATESTMODELREQUEST']._serialized_end=1820 - _globals['_GETLATESTMODELRESPONSE']._serialized_start=1822 - _globals['_GETLATESTMODELRESPONSE']._serialized_end=1887 - _globals['_TRAINERSERVER']._serialized_start=1890 - _globals['_TRAINERSERVER']._serialized_end=2347 + _globals['_STARTTRAININGREQUEST']._serialized_end=1149 + _globals['_STARTTRAININGRESPONSE']._serialized_start=1151 + _globals['_STARTTRAININGRESPONSE']._serialized_end=1221 + _globals['_TRAININGSTATUSREQUEST']._serialized_start=1223 + _globals['_TRAININGSTATUSREQUEST']._serialized_end=1267 + _globals['_TRAININGSTATUSRESPONSE']._serialized_start=1270 + _globals['_TRAININGSTATUSRESPONSE']._serialized_end=1692 + _globals['_STOREFINALMODELREQUEST']._serialized_start=1694 + _globals['_STOREFINALMODELREQUEST']._serialized_end=1739 + _globals['_STOREFINALMODELRESPONSE']._serialized_start=1741 + _globals['_STOREFINALMODELRESPONSE']._serialized_end=1805 + _globals['_GETLATESTMODELREQUEST']._serialized_start=1807 + _globals['_GETLATESTMODELREQUEST']._serialized_end=1851 + _globals['_GETLATESTMODELRESPONSE']._serialized_start=1853 + _globals['_GETLATESTMODELRESPONSE']._serialized_end=1918 + _globals['_TRAINERSERVER']._serialized_start=1921 + _globals['_TRAINERSERVER']._serialized_end=2378 # @@protoc_insertion_point(module_scope) diff --git a/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.pyi b/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.pyi index 8b793c5a0..697e0b64e 100644 --- a/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.pyi +++ b/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.pyi @@ -133,6 +133,7 @@ class StartTrainingRequest(google.protobuf.message.Message): LABEL_TRANSFORMER_FIELD_NUMBER: builtins.int GRAD_SCALER_CONFIGURATION_FIELD_NUMBER: builtins.int EPOCHS_PER_TRIGGER_FIELD_NUMBER: builtins.int + PREFETCHED_PARTITIONS_FIELD_NUMBER: builtins.int SEED_FIELD_NUMBER: builtins.int TOKENIZER_FIELD_NUMBER: builtins.int pipeline_id: builtins.int @@ -166,6 +167,7 @@ class StartTrainingRequest(google.protobuf.message.Message): @property def grad_scaler_configuration(self) -> global___JsonString: ... epochs_per_trigger: builtins.int + prefetched_partitions: builtins.int seed: builtins.int @property def tokenizer(self) -> global___PythonString: ... @@ -193,11 +195,12 @@ class StartTrainingRequest(google.protobuf.message.Message): label_transformer: global___PythonString | None = ..., grad_scaler_configuration: global___JsonString | None = ..., epochs_per_trigger: builtins.int = ..., + prefetched_partitions: builtins.int = ..., seed: builtins.int | None = ..., tokenizer: global___PythonString | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["_seed", b"_seed", "_tokenizer", b"_tokenizer", "bytes_parser", b"bytes_parser", "checkpoint_info", b"checkpoint_info", "criterion_parameters", b"criterion_parameters", "data_info", b"data_info", "grad_scaler_configuration", b"grad_scaler_configuration", "label_transformer", b"label_transformer", "lr_scheduler", b"lr_scheduler", "model_configuration", b"model_configuration", "seed", b"seed", "tokenizer", b"tokenizer", "torch_optimizers_configuration", b"torch_optimizers_configuration"]) -> builtins.bool: ... - def ClearField(self, field_name: typing_extensions.Literal["_seed", b"_seed", "_tokenizer", b"_tokenizer", "amp", b"amp", "batch_size", b"batch_size", "bytes_parser", b"bytes_parser", "checkpoint_info", b"checkpoint_info", "criterion_parameters", b"criterion_parameters", "data_info", b"data_info", "device", b"device", "epochs_per_trigger", b"epochs_per_trigger", "grad_scaler_configuration", b"grad_scaler_configuration", "label_transformer", b"label_transformer", "load_optimizer_state", b"load_optimizer_state", "lr_scheduler", b"lr_scheduler", "model_configuration", b"model_configuration", "model_id", b"model_id", "pipeline_id", b"pipeline_id", "pretrained_model_id", b"pretrained_model_id", "seed", b"seed", "tokenizer", b"tokenizer", "torch_criterion", b"torch_criterion", "torch_optimizers_configuration", b"torch_optimizers_configuration", "transform_list", b"transform_list", "trigger_id", b"trigger_id", "use_pretrained_model", b"use_pretrained_model"]) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["_seed", b"_seed", "_tokenizer", b"_tokenizer", "amp", b"amp", "batch_size", b"batch_size", "bytes_parser", b"bytes_parser", "checkpoint_info", b"checkpoint_info", "criterion_parameters", b"criterion_parameters", "data_info", b"data_info", "device", b"device", "epochs_per_trigger", b"epochs_per_trigger", "grad_scaler_configuration", b"grad_scaler_configuration", "label_transformer", b"label_transformer", "load_optimizer_state", b"load_optimizer_state", "lr_scheduler", b"lr_scheduler", "model_configuration", b"model_configuration", "model_id", b"model_id", "pipeline_id", b"pipeline_id", "prefetched_partitions", b"prefetched_partitions", "pretrained_model_id", b"pretrained_model_id", "seed", b"seed", "tokenizer", b"tokenizer", "torch_criterion", b"torch_criterion", "torch_optimizers_configuration", b"torch_optimizers_configuration", "transform_list", b"transform_list", "trigger_id", b"trigger_id", "use_pretrained_model", b"use_pretrained_model"]) -> None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["_seed", b"_seed"]) -> typing_extensions.Literal["seed"] | None: ... @typing.overload diff --git a/modyn/trainer_server/internal/trainer/pytorch_trainer.py b/modyn/trainer_server/internal/trainer/pytorch_trainer.py index 47c118893..8fea848b8 100644 --- a/modyn/trainer_server/internal/trainer/pytorch_trainer.py +++ b/modyn/trainer_server/internal/trainer/pytorch_trainer.py @@ -161,6 +161,7 @@ def __init__( training_info.storage_address, training_info.selector_address, training_info.training_id, + training_info.prefetched_partitions, training_info.tokenizer, self._dataset_log_path, ) diff --git a/modyn/trainer_server/internal/utils/training_info.py b/modyn/trainer_server/internal/utils/training_info.py index 8fb7b30c6..7cce81b75 100644 --- a/modyn/trainer_server/internal/utils/training_info.py +++ b/modyn/trainer_server/internal/utils/training_info.py @@ -27,6 +27,7 @@ def __init__( self.pipeline_id = request.pipeline_id self.trigger_id = request.trigger_id self.training_id = training_id + self.prefetched_partitions = request.prefetched_partitions self.dataset_id = request.data_info.dataset_id self.num_dataloaders = request.data_info.num_dataloaders diff --git a/plotting/common/common.py b/plotting/common/common.py new file mode 100644 index 000000000..01a48ec14 --- /dev/null +++ b/plotting/common/common.py @@ -0,0 +1,151 @@ +# Credits to Lawrence Benson (https://github.com/hpides/perma-bench/tree/eval/scripts) + +import json +import os +import sys + +import matplotlib +import matplotlib.pyplot as plt + +####################################### +# Plotting +####################################### + +FS = 20 +MILLION = 1_000_000 +SINGLE_FIG_WIDTH = 5 +SINGLE_FIG_HEIGHT = 3.5 +SINGLE_FIG_SIZE = (SINGLE_FIG_WIDTH, SINGLE_FIG_HEIGHT) +DOUBLE_FIG_WIDTH = 10 +DOUBLE_FIG_HEIGHT = 3.5 +DOUBLE_FIG_SIZE = (DOUBLE_FIG_WIDTH, DOUBLE_FIG_HEIGHT) +PLOT_PATHS = [] +IMG_TYPES = ['.png'] # add .svg here to generate svg + +PIPELINE_COLOR = { + 'models_exp0_finetune': '#a1dab4', + 'retrain_noreset': '#378d54', + 'apache-512': '#41b6c4', + 'barlow-256': '#2c7fb8', + 'barlow-512': '#2c7fb8', + 'z-barlow-dram': '#253494', + 'z-apache-dram': '#0c1652', +} + +PIPELINE_MARKER = { + 'models_exp0_finetune': 'P', + 'retrain_noreset': 'o', + 'apache-512': 'd', + 'barlow-256': 's', + 'barlow-512': '.', + 'z-apache-dram': 'x', + 'z-barlow-dram': '^', +} + +PIPELINE_HATCH = { + 'models_exp0_finetune': '\\\\', + 'retrain_noreset': '//', + 'apache-512': '\\', + 'barlow-256': '/', + 'barlow-512': '.', + 'z-apache-dram': '.', + 'z-barlow-dram': 'x', +} + +PIPELINE_NAME = { + 'models_exp0_finetune': 'Finetuning', + 'retrain_noreset': 'Retrain', + 'apache-512': 'A-512', + 'barlow-256': 'B-256', + 'barlow-512': 'B-256-PF', + 'z-apache-dram': 'A-D', + 'z-barlow-dram': 'B-D', +} + + + +def INIT_PLOT(): + matplotlib.rcParams.update({ + 'font.size': FS, + 'svg.fonttype': 'none', + }) + + +def PRINT_PLOT_PATHS(): + print(f"To view new plots, run:\n\topen {' '.join(PLOT_PATHS)}") + +def BAR(system): + return { + "color": 'white', + "edgecolor": PIPELINE_COLOR[system], + "hatch": PIPELINE_HATCH[system], + "lw": 3 + } + +def LINE(system): + return { + "lw": 4, + "ms": 10, + "color": PIPELINE_COLOR[system], + "marker": PIPELINE_MARKER[system], + "markeredgewidth": 1, + "markeredgecolor": 'black', + } + +def BAR_X_TICKS_POS(bar_width, num_bars, num_xticks): + return [i - (bar_width / 2) + ((num_bars * bar_width) / 2) for i in range(num_xticks)] + +def RESIZE_TICKS(ax, x=FS, y=FS): + for tick in ax.xaxis.get_major_ticks(): + tick.label.set_fontsize(x) + for tick in ax.yaxis.get_major_ticks(): + tick.label.set_fontsize(y) + +def HATCH_WIDTH(width=4): + matplotlib.rcParams['hatch.linewidth'] = width + +def Y_GRID(ax): + ax.grid(axis='y', which='major') + ax.set_axisbelow(True) + +def HIDE_BORDERS(ax, show_left=False): + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.spines['bottom'].set_visible(True) + ax.spines['left'].set_visible(show_left) + +def FIG_LEGEND(fig): + fig.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=6, + frameon=False, columnspacing=1, handletextpad=0.3 + #, borderpad=0.1, labelspacing=0.1, handlelength=1.8 + ) + fig.tight_layout() + + +def LOAD_DATA(path): + with open(path) as json_file: + return json.load(json_file) + +def SAVE_PLOT(plot_path, img_types=None): + if img_types is None: + img_types = IMG_TYPES + + for img_type in img_types: + img_path = f"{plot_path}{img_type}" + PLOT_PATHS.append(img_path) + plt.savefig(img_path, bbox_inches='tight', dpi=300) + + plt.figure() + + +def INIT(args): + if len(args) != 3: + sys.exit("Need /path/to/results /path/to/plots") + + result_path = args[1] + plot_dir = args[2] + + os.makedirs(plot_dir, exist_ok=True) + INIT_PLOT() + + return result_path, plot_dir \ No newline at end of file diff --git a/plotting/system/next_batch_distribution.py b/plotting/system/next_batch_distribution.py new file mode 100644 index 000000000..fdc412c8b --- /dev/null +++ b/plotting/system/next_batch_distribution.py @@ -0,0 +1,43 @@ +import sys + +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +from plotting.common.common import * + + +def plot_nbd(pipeline_log, ax, trigger): + relevant_data = pipeline_log["supervisor"]["triggers"][trigger]["trainer_log"] + all_epoch_timings = [] + for epoch in relevant_data["epochs"]: + all_epoch_timings.extend(epoch["BatchTimings"]) + all_epoch_timings = np.array(all_epoch_timings) / 1000 # ms to seconds + + + sns.histplot(data=all_epoch_timings, ax=ax, log_scale=True) + + #ax.set_xticks(list(x)) + #ax.set_xticklabels([f"{idx + 1}" for idx, _ in enumerate(x)]) + ax.set_xlabel("Waiting time for next batch (seconds)") + + ax.set_ylabel("Count") + + ax.set_title("Histogram of waiting times") + + +if __name__ == '__main__': + data_path, plot_dir = INIT(sys.argv) + data = LOAD_DATA(data_path) + + fig, ax = plt.subplots(1, 1, figsize=DOUBLE_FIG_SIZE) + + plot_nbd(data, ax, "0") + + HATCH_WIDTH() + #FIG_LEGEND(fig) + Y_GRID(ax) + HIDE_BORDERS(ax) + + plot_path = os.path.join(plot_dir, "next_batch_distribution") + SAVE_PLOT(plot_path) + PRINT_PLOT_PATHS() \ No newline at end of file diff --git a/setup.cfg b/setup.cfg index 7daf584ec..91336a874 100644 --- a/setup.cfg +++ b/setup.cfg @@ -13,7 +13,8 @@ addopts = max-line-length = 120 exclude = *_grpc.py, *_pb2.py, - benchmark/**/* + benchmark/**/*, + plotting/**/* extend-ignore = E203 # E203 is not pep8-compliant From 1e4c972489319e835df7c15f9a289d5f96a5f8dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Mon, 25 Sep 2023 11:19:28 +0200 Subject: [PATCH 02/46] focus on first partition --- .../trainer_server/internal/dataset/online_dataset.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index 90bb00aca..9d33288ab 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -249,6 +249,7 @@ def _wait_for_partition( assert self._pref_started[partition_id], f"Prefetching for partition {partition_id} has not been started" self._info(f"Joining thread for partition {partition_id}", worker_id) self._data_threads[partition_id].join() + self._info(f"Thread for partition {partition_id} joined", worker_id) container = self._thread_data_container[partition_id] @@ -300,13 +301,18 @@ def __iter__(self) -> Generator: self._log["num_partitions"] = self._num_partitions self._prefetched_partitions = min(self._prefetched_partitions, self._num_partitions) - for partition in range(self._prefetched_partitions): - self._prefetch_partition(worker_id) + # Start prefetching first partition (avoid overloading by not fetching the other ones) + if self._prefetched_partitions > 0: + self._prefetch_partition(0) self._sw.start("wait_for_initial_partition", overwrite=True) keys, data, labels, weights = self._wait_for_partition(worker_id, 0) self._sw.stop("wait_for_initial_partition") + # Now prefetch next partitions + for partition in range(1, self._prefetched_partitions): + self._prefetch_partition(worker_id) + for partition in range(self._num_partitions): self._persist_log(worker_id) num_samples_on_this_partition = len(keys) From 0814863ef081e98461396e13e486ab0109f372ed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Mon, 25 Sep 2023 11:30:31 +0200 Subject: [PATCH 03/46] add some experiment files --- prefetch0.yml | 120 +++++++++++++++++++++++++++++++++++++++++ prefetch1.yml | 120 +++++++++++++++++++++++++++++++++++++++++ prefetch4.yml | 120 +++++++++++++++++++++++++++++++++++++++++ prefetch8.yml | 120 +++++++++++++++++++++++++++++++++++++++++ prefetch8_4workers.yml | 120 +++++++++++++++++++++++++++++++++++++++++ run_prefetch_exp.sh | 8 +++ 6 files changed, 608 insertions(+) create mode 100644 prefetch0.yml create mode 100644 prefetch1.yml create mode 100644 prefetch4.yml create mode 100644 prefetch8.yml create mode 100644 prefetch8_4workers.yml create mode 100644 run_prefetch_exp.sh diff --git a/prefetch0.yml b/prefetch0.yml new file mode 100644 index 000000000..2a2b0b210 --- /dev/null +++ b/prefetch0.yml @@ -0,0 +1,120 @@ +pipeline: + name: prefetch0 + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 8 + prefetched_partitions: 0 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 50000000 + diff --git a/prefetch1.yml b/prefetch1.yml new file mode 100644 index 000000000..94b4e1fa3 --- /dev/null +++ b/prefetch1.yml @@ -0,0 +1,120 @@ +pipeline: + name: prefetch1 + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 8 + prefetched_partitions: 1 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 50000000 + diff --git a/prefetch4.yml b/prefetch4.yml new file mode 100644 index 000000000..f23d1faf8 --- /dev/null +++ b/prefetch4.yml @@ -0,0 +1,120 @@ +pipeline: + name: prefetch4 + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 8 + prefetched_partitions: 4 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 50000000 + diff --git a/prefetch8.yml b/prefetch8.yml new file mode 100644 index 000000000..18f45ed9d --- /dev/null +++ b/prefetch8.yml @@ -0,0 +1,120 @@ +pipeline: + name: prefetch8 + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 8 + prefetched_partitions: 8 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 50000000 + diff --git a/prefetch8_4workers.yml b/prefetch8_4workers.yml new file mode 100644 index 000000000..6c69c6c02 --- /dev/null +++ b/prefetch8_4workers.yml @@ -0,0 +1,120 @@ +pipeline: + name: prefetch8_4workers + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 4 + prefetched_partitions: 8 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 50000000 + diff --git a/run_prefetch_exp.sh b/run_prefetch_exp.sh new file mode 100644 index 000000000..cd9c09ed6 --- /dev/null +++ b/run_prefetch_exp.sh @@ -0,0 +1,8 @@ +modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch0.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch0 +modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch1.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch1 +modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch4.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch4 +modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch8.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch8 +modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch8_4workers.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch8_4workers + + + From 5452fa2c36be331fd01db27be9adedec1a0cc84e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Mon, 25 Sep 2023 12:23:22 +0200 Subject: [PATCH 04/46] fix mypy --- modyn/trainer_server/internal/dataset/online_dataset.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index 9d33288ab..bf1e4c4e3 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -224,7 +224,7 @@ def _prefetch_partition(self, worker_id: int) -> None: self._next_partition_to_fetch not in self._data_threads ), f"Prefetching for partition {self._next_partition_to_fetch} has already been started" - self._thread_data_container[self._next_partition_to_fetch]: dict[str, Any] = {} + self._thread_data_container[self._next_partition_to_fetch] = {} self._data_threads[self._next_partition_to_fetch] = threading.Thread( target=self._get_data, @@ -287,9 +287,9 @@ def __iter__(self) -> Generator: self._sw = Stopwatch() # Always reinitialize these structures for prefetching (for multiple epochs) - self._data_threads: dict[int, threading.Thread] = {} - self._thread_data_container: dict[str, Any] = {} - self._pref_started: dict[int, bool] = {} + self._data_threads = {} + self._thread_data_container = {} + self._pref_started = {} self._next_partition_to_fetch = 0 assert self._transform is not None From 33d7d695d60f71e569d845d05a1a3cec59137594 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Mon, 25 Sep 2023 12:48:28 +0200 Subject: [PATCH 05/46] increase grpc workers in stoarge and selector --- modyn/selector/internal/grpc/selector_server.py | 2 +- modyn/storage/internal/grpc/grpc_server.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/modyn/selector/internal/grpc/selector_server.py b/modyn/selector/internal/grpc/selector_server.py index 0d4345be0..3ca0fc4d9 100644 --- a/modyn/selector/internal/grpc/selector_server.py +++ b/modyn/selector/internal/grpc/selector_server.py @@ -21,7 +21,7 @@ def __init__(self, modyn_config: dict) -> None: def prepare_server(self) -> grpc.server: server = grpc.server( - futures.ThreadPoolExecutor(max_workers=10), + futures.ThreadPoolExecutor(max_workers=64), options=[ ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), ("grpc.max_send_message_length", MAX_MESSAGE_SIZE), diff --git a/modyn/storage/internal/grpc/grpc_server.py b/modyn/storage/internal/grpc/grpc_server.py index 7adcaf298..0a76d6652 100644 --- a/modyn/storage/internal/grpc/grpc_server.py +++ b/modyn/storage/internal/grpc/grpc_server.py @@ -23,7 +23,7 @@ def __init__(self, modyn_config: dict) -> None: self.modyn_config = modyn_config self.server = grpc.server( futures.ThreadPoolExecutor( - max_workers=10, + max_workers=64, ), options=[ ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), From e9e3929dc3b72ef0b5c8450c93b39c54fdb2b8b9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Mon, 25 Sep 2023 12:56:17 +0200 Subject: [PATCH 06/46] add response time tracking --- .../internal/dataset/online_dataset.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index bf1e4c4e3..6dfd6589d 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -87,19 +87,24 @@ def __init__( def change_key_source(self, source: AbstractKeySource) -> None: self._key_source = source - def _get_data_from_storage(self, selector_keys: list[int]) -> tuple[list[bytes], list[int]]: + def _get_data_from_storage(self, selector_keys: list[int]) -> tuple[list[bytes], list[int], list[int]]: req = GetRequest(dataset_id=self._dataset_id, keys=selector_keys) + stopw = Stopwatch() + response_times = [] data_from_storage: dict[int, tuple[bytes, int]] = {} response: GetResponse + stopw.start("ResponseTime", overwrite=True) for _, response in enumerate(self._storagestub.Get(req)): + response_times.append(stopw.stop("ResponseTime")) for key, sample, label in zip(response.keys, response.samples, response.labels): data_from_storage[key] = (sample, label) + stopw.start("ResponseTime", overwrite=True) sample_list = [data_from_storage[key][0] for key in selector_keys] label_list = [data_from_storage[key][1] for key in selector_keys] - return sample_list, label_list + return sample_list, label_list, response_times def _setup_composed_transform(self) -> None: assert self._bytes_parser_function is not None @@ -151,8 +156,9 @@ def _get_data(self, data_container: dict, worker_id: int, partition_id: int) -> self._info("Getting data from storage", worker_id) self._sw.start(f"GetDataPart{partition_id}", overwrite=True) - data, labels = self._get_data_from_storage(keys) + data, labels, response_times = self._get_data_from_storage(keys) get_data_log["get_data"] = self._sw.stop(f"GetDataPart{partition_id}") + get_data_log["response_times"] = response_times self._log["partitions"][str(partition_id)] = get_data_log From 9de48290980e9916e38421e29cb30953e5a5f21c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Mon, 25 Sep 2023 21:38:50 +0200 Subject: [PATCH 07/46] work --- .../internal/data/test_online_dataset.py | 52 ++-- .../internal/dataset/online_dataset.py | 237 +++++++++--------- .../dataset/per_class_online_dataset.py | 2 +- 3 files changed, 152 insertions(+), 139 deletions(-) diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index a90498795..70ab51abb 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -9,6 +9,7 @@ from modyn.storage.internal.grpc.generated.storage_pb2 import GetResponse from modyn.trainer_server.internal.dataset.key_sources import SelectorKeySource from modyn.trainer_server.internal.dataset.online_dataset import OnlineDataset +from modyn.utils import flatten from torchvision import transforms @@ -177,16 +178,27 @@ def test_get_data_from_storage( prefetched_partitions=0, ) online_dataset._init_grpc() - assert online_dataset._get_data_from_storage(list(range(10))) == ( + keys = [] + data = [] + labels = [] + + for key_list, data_list, label_list, _ in online_dataset._get_data_from_storage(list(range(10))): + keys.extend(key_list) + data.extend(data_list) + labels.extend(label_list) + + assert (keys, data, labels) == ( + list(range(10)), [bytes(f"sample{x}", "utf-8") for x in range(10)], list(range(10)), ) - permuted_list = [0, 9, 6, 5, 4, 3] - assert online_dataset._get_data_from_storage(permuted_list) == ( - [b"sample0", b"sample9", b"sample6", b"sample5", b"sample4", b"sample3"], - [0, 9, 6, 5, 4, 3], - ) + # TODO(create issue): readd when re-adding support for ordering in onlinedataset + #permuted_list = [0, 9, 6, 5, 4, 3] + #assert online_dataset._get_data_from_storage(permuted_list) == ( + # [b"sample0", b"sample9", b"sample6", b"sample5", b"sample4", b"sample3"], + # [0, 9, 6, 5, 4, 3], + #) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @@ -254,7 +266,7 @@ def test_deserialize_torchvision_transforms( @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) @patch.object( - OnlineDataset, "_get_data_from_storage", return_value=([bytes(f"sample{x}", "utf-8") for x in range(10)], [1] * 10) + OnlineDataset, "_get_data_from_storage", return_value=[(list(range(10)),[bytes(f"sample{x}", "utf-8") for x in range(10)], [1] * 10, 0)] ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(10)), None)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) @@ -297,7 +309,7 @@ def test_dataset_iter( @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) @patch.object( - OnlineDataset, "_get_data_from_storage", return_value=([bytes(f"sample{x}", "utf-8") for x in range(10)], [1] * 10) + OnlineDataset, "_get_data_from_storage", return_value=[(list(range(10)), [bytes(f"sample{x}", "utf-8") for x in range(10)], [1] * 10, 0)] ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(10)), None)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) @@ -340,7 +352,7 @@ def test_dataset_iter_with_parsing( @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) @patch.object( - OnlineDataset, "_get_data_from_storage", return_value=([x.to_bytes(2, "big") for x in range(16)], [1] * 16) + OnlineDataset, "_get_data_from_storage", return_value=[(list(range(16)),[x.to_bytes(2, "big") for x in range(16)], [1] * 16,0)] ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(16)), None)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) @@ -384,7 +396,7 @@ def test_dataloader_dataset( @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) @patch.object( - OnlineDataset, "_get_data_from_storage", return_value=([x.to_bytes(2, "big") for x in range(16)], [1] * 16) + OnlineDataset, "_get_data_from_storage", return_value=[(list(range(16)),[x.to_bytes(2, "big") for x in range(16)], [1] * 16,0)] ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(16)), [2.0] * 16)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) @@ -538,7 +550,7 @@ def test_init_transforms( tv_ds.assert_called_once() -@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("prefetched_partitions", [0,1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -551,20 +563,20 @@ def test_init_transforms( OnlineDataset, "_get_data_from_storage", side_effect=[ - ([x.to_bytes(2, "big") for x in range(16)], [1] * 16), - ([x.to_bytes(2, "big") for x in range(16, 32)], [1] * 16), - ([x.to_bytes(2, "big") for x in range(32, 48)], [1] * 16), - ([x.to_bytes(2, "big") for x in range(48, 64)], [1] * 16), + ([x for x in range(0,16)], [x.to_bytes(2, "big") for x in range(16)], [1] * 16, 0), + ([x for x in range(16,32)],[x.to_bytes(2, "big") for x in range(16, 32)], [1] * 16, 0), + ([x for x in range(32,48)],[x.to_bytes(2, "big") for x in range(32, 48)], [1] * 16, 0), + ([x for x in range(48,64)],[x.to_bytes(2, "big") for x in range(48, 64)], [1] * 16, 0), ], ) @patch.object( SelectorKeySource, "get_keys_and_weights", side_effect=[ - ([str(i) for i in range(16)], None), - ([str(i) for i in range(16, 32)], None), - ([str(i) for i in range(32, 48)], None), - ([str(i) for i in range(48, 64)], None), + ([i for i in range(16)], None), + ([i for i in range(16, 32)], None), + ([i for i in range(32, 48)], None), + ([i for i in range(48, 64)], None), ], ) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=4) @@ -595,7 +607,7 @@ def test_iter_multi_partition( idx = 0 for idx, batch in enumerate(dataloader): assert len(batch) == 3 - assert batch[0] == (str(4 * idx), str(4 * idx + 1), str(4 * idx + 2), str(4 * idx + 3)) + assert torch.equal(batch[0], torch.Tensor([4 * idx, 4 * idx + 1, 4 * idx + 2, 4 * idx + 3])) assert torch.equal(batch[1], torch.Tensor([4 * idx, 4 * idx + 1, 4 * idx + 2, 4 * idx + 3])) assert torch.equal(batch[2], torch.ones(4, dtype=torch.float64)) assert idx == 15 diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index 6dfd6589d..21f2460f5 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -2,9 +2,11 @@ import json import logging import os +import time +import contextlib import pathlib import threading -from typing import Any, Callable, Generator, Optional, Tuple, Union +from typing import Any, Callable, Generator, Optional, Tuple, Union, Iterator import grpc from modyn.common.benchmark.stopwatch import Stopwatch @@ -71,6 +73,10 @@ def __init__( self._data_threads: dict[int, threading.Thread] = {} self._pref_started: dict[int, bool] = {} self._thread_data_container: dict[int, dict[str, Any]] = {} + self._partition_locks: dict[int, threading.Lock] = {} + self._partition_signals: dict[int, threading.Condition] = {} # Should use the lock out of partition_locks + self._partition_valid_until: dict[int, int] = {} + self._partition_valid: dict[int, bool] = {} self._next_partition_to_fetch = 0 if log_path is None: @@ -87,25 +93,6 @@ def __init__( def change_key_source(self, source: AbstractKeySource) -> None: self._key_source = source - def _get_data_from_storage(self, selector_keys: list[int]) -> tuple[list[bytes], list[int], list[int]]: - req = GetRequest(dataset_id=self._dataset_id, keys=selector_keys) - stopw = Stopwatch() - response_times = [] - - data_from_storage: dict[int, tuple[bytes, int]] = {} - response: GetResponse - stopw.start("ResponseTime", overwrite=True) - for _, response in enumerate(self._storagestub.Get(req)): - response_times.append(stopw.stop("ResponseTime")) - for key, sample, label in zip(response.keys, response.samples, response.labels): - data_from_storage[key] = (sample, label) - stopw.start("ResponseTime", overwrite=True) - - sample_list = [data_from_storage[key][0] for key in selector_keys] - label_list = [data_from_storage[key][1] for key in selector_keys] - - return sample_list, label_list, response_times - def _setup_composed_transform(self) -> None: assert self._bytes_parser_function is not None @@ -147,7 +134,18 @@ def _info(self, msg: str, worker_id: Optional[int]) -> None: # pragma: no cover def _debug(self, msg: str, worker_id: Optional[int]) -> None: # pragma: no cover logger.debug(f"[Training {self._training_id}][PL {self._pipeline_id}][Worker {worker_id}] {msg}") - def _get_data(self, data_container: dict, worker_id: int, partition_id: int) -> None: + + def _get_data_from_storage(self, selector_keys: list[int]) -> Iterator[tuple[list[int], list[bytes], list[int], int]]: + req = GetRequest(dataset_id=self._dataset_id, keys=selector_keys) + stopw = Stopwatch() + + response: GetResponse + stopw.start("ResponseTime", overwrite=True) + for _, response in enumerate(self._storagestub.Get(req)): + yield list(response.keys), list(response.samples), list(response.labels), stopw.stop("ResponseTime") + stopw.start("ResponseTime", overwrite=True) + + def _get_data(self, data_container: dict, worker_id: int, partition_id: int, partition_valid: Optional[dict], partition_valid_until: Optional[dict], partition_locks: Optional[dict], partition_signals: Optional[dict]) -> None: get_data_log = {} self._sw.start(f"GetKeysAndWeightsPart{partition_id}", overwrite=True) keys, weights = self._key_source.get_keys_and_weights(worker_id, partition_id) @@ -156,43 +154,36 @@ def _get_data(self, data_container: dict, worker_id: int, partition_id: int) -> self._info("Getting data from storage", worker_id) self._sw.start(f"GetDataPart{partition_id}", overwrite=True) - data, labels, response_times = self._get_data_from_storage(keys) - get_data_log["get_data"] = self._sw.stop(f"GetDataPart{partition_id}") - get_data_log["response_times"] = response_times + all_response_times = [] + + key_weight_map = { key: weights[idx] for idx, key in enumerate(keys) } if weights is not None else None + + for data_tuple in self._get_data_from_storage(keys): + stor_keys, data, labels, response_time = data_tuple + all_response_times.append(response_time) + num_items = len(stor_keys) + with partition_locks[partition_id] if partition_locks is not None else contextlib.suppress(): + data_container["data"].extend(data) + data_container["keys"].extend(stor_keys) + data_container["labels"].extend(labels) + data_container["weights"].extend([key_weight_map[key] for key in stor_keys] if key_weight_map is not None else [None for _ in range(len(stor_keys))]) + if partition_valid_until is not None: + partition_valid_until[partition_id] += num_items + + if partition_signals is not None: + with partition_signals[partition_id]: + partition_signals[partition_id].notify_all() + get_data_log["get_data"] = self._sw.stop(f"GetDataPart{partition_id}") + get_data_log["response_times"] = all_response_times self._log["partitions"][str(partition_id)] = get_data_log - data_container["data"] = data - data_container["keys"] = keys - data_container["labels"] = labels - data_container["weights"] = weights + if partition_locks is not None and partition_valid is not None: + with partition_locks[partition_id]: + partition_valid[partition_id] = True - def _get_data_iterator( - self, keys: list[int], data: list[bytes], labels: list[int], weights: Optional[list[float]] - ) -> enumerate: - assert self._uses_weights is not None - - # pylint: disable-next = unsubscriptable-object - iterator: Union[zip[Tuple[int, bytes, int]], zip[Tuple[int, bytes, int, float]]] - if self._uses_weights: - assert weights is not None and len(weights) == len(keys) - iterator = zip(keys, data, labels, weights) - else: - iterator = zip(keys, data, labels) - return enumerate(iterator) - def _unpack_data_tuple(self, data_tuple: Tuple) -> Tuple[int, bytes, int, Optional[float]]: - assert self._uses_weights is not None - - if self._uses_weights: - key, sample, label, weight = data_tuple - else: - key, sample, label = data_tuple - weight = None - - return key, sample, label, weight - - def _get_data_tuple(self, key: int, sample: bytes, label: int, weight: Optional[float]) -> Optional[Tuple]: + def _get_transformed_data_tuple(self, key: int, sample: bytes, label: int, weight: Optional[float]) -> Optional[Tuple]: assert self._uses_weights is not None self._sw.start("transform", resume=True) # mypy complains here because _transform has unknown type, which is ok @@ -230,11 +221,15 @@ def _prefetch_partition(self, worker_id: int) -> None: self._next_partition_to_fetch not in self._data_threads ), f"Prefetching for partition {self._next_partition_to_fetch} has already been started" - self._thread_data_container[self._next_partition_to_fetch] = {} + self._thread_data_container[self._next_partition_to_fetch] = {"data": [], "keys": [], "labels": [], "weights": []} + self._partition_valid[self._next_partition_to_fetch] = False + self._partition_valid_until[self._next_partition_to_fetch] = -1 + self._partition_locks[self._next_partition_to_fetch] = threading.Lock() + self._partition_signals[self._next_partition_to_fetch] = threading.Condition(self._partition_locks[self._next_partition_to_fetch]) self._data_threads[self._next_partition_to_fetch] = threading.Thread( target=self._get_data, - args=(self._thread_data_container[self._next_partition_to_fetch], worker_id, self._next_partition_to_fetch), + args=(self._thread_data_container[self._next_partition_to_fetch], worker_id, self._next_partition_to_fetch, self._partition_valid, self._partition_valid_until, self._partition_locks, self._partition_signals), ) self._data_threads[self._next_partition_to_fetch].start() @@ -242,31 +237,72 @@ def _prefetch_partition(self, worker_id: int) -> None: self._next_partition_to_fetch += 1 - def _wait_for_partition( - self, worker_id: int, partition_id: int - ) -> tuple[list[int], list[bytes], list[int], Optional[list[float]]]: - container: dict[str, Any] = {} + def _fetch_partition_noprefetch(self, worker_id: int, partition_id: int) -> Generator: + assert self._prefetched_partitions < 1 + container: dict[str, Any] = {"data": [], "keys": [], "labels": [], "weights": []} + self._get_data(container, worker_id, partition_id, None, None, None, None) + assert "data" in container and "labels" in container and "keys" in container and "weights" in container - if self._prefetched_partitions < 1: - # Prefetching disabled - self._get_data(container, worker_id, partition_id) - else: - # Prefetching enabled - assert self._pref_started[partition_id], f"Prefetching for partition {partition_id} has not been started" - self._info(f"Joining thread for partition {partition_id}", worker_id) - self._data_threads[partition_id].join() - self._info(f"Thread for partition {partition_id} joined", worker_id) + for idx in range(len(container["keys"])): + yield container["keys"][idx], container["data"][idx], container["labels"][idx], container["weights"][idx] + + def _is_partition_fetched(self, partition_id: int) -> bool: + with self._partition_locks[partition_id]: + return self._partition_valid[partition_id] + + def _partition_max_index(self, partition_id: int) -> int: + with self._partition_locks[partition_id]: + return self._partition_valid_until[partition_id] + + def _get_partition_data(self, last_idx: int, max_idx: int, partition_id: int) -> Generator: + for idx in range(last_idx + 1, max_idx + 1): + yield self._thread_data_container[partition_id]["keys"][idx], self._thread_data_container[partition_id]["data"][idx], self._thread_data_container[partition_id]["labels"][idx], self._thread_data_container[partition_id]["weights"][idx] + + def _wait_for_new_partition_data(self, partition_id: int) -> None: + with self._partition_signals[partition_id]: + self._partition_signals[partition_id].wait(1) # In case we do not get woken up, we at most waste a second + + def prefetched_partition_generator(self, worker_id: int, partition_id: int) -> Generator: + assert self._pref_started[partition_id], f"Prefetching for partition {partition_id} has not been started" + last_idx = -1 + + while not self._is_partition_fetched(partition_id): + + max_idx = self._partition_max_index(partition_id) + if max_idx <= last_idx: # No new data + self._wait_for_new_partition_data(partition_id) + + yield from self._get_partition_data(last_idx, max_idx, partition_id) + last_idx = max_idx + + # Yield potential remaining data + self._info(f"Joining thread for partition {partition_id}", worker_id) + self._data_threads[partition_id].join() + self._info(f"Thread for partition {partition_id} joined", worker_id) + max_idx = self._partition_max_index(partition_id) + yield from self._get_partition_data(last_idx, max_idx, partition_id) + return + + def all_partition_generator(self, worker_id: int) -> Generator: + for _ in range(self._prefetched_partitions): + self._prefetch_partition(worker_id) - container = self._thread_data_container[partition_id] + for partition_id in range(self._num_partitions): + self._persist_log(worker_id) - assert "data" in container and "labels" in container and "keys" in container and "weights" in container - keys, data, labels, weights = (container["keys"], container["data"], container["labels"], container["weights"]) - container.clear() - del container - gc.collect() + if self._prefetched_partitions > 0: + # Prefetched generator + if partition_id < self._num_partitions - 1: + self._prefetch_partition(worker_id) - return keys, data, labels, weights + yield from self.prefetched_partition_generator(worker_id, partition_id) + else: + yield from self._fetch_partition_noprefetch(worker_id, partition_id) + + + + # pylint: disable=too-many-locals, too-many-branches, too-many-statements def __iter__(self) -> Generator: @@ -297,6 +333,10 @@ def __iter__(self) -> Generator: self._thread_data_container = {} self._pref_started = {} self._next_partition_to_fetch = 0 + self._partition_locks = {} + self._partition_valid_until = {} + self._partition_valid = {} + self._partition_signals = {} assert self._transform is not None self._num_partitions = self._key_source.get_num_data_partitions() @@ -307,46 +347,7 @@ def __iter__(self) -> Generator: self._log["num_partitions"] = self._num_partitions self._prefetched_partitions = min(self._prefetched_partitions, self._num_partitions) - # Start prefetching first partition (avoid overloading by not fetching the other ones) - if self._prefetched_partitions > 0: - self._prefetch_partition(0) - - self._sw.start("wait_for_initial_partition", overwrite=True) - keys, data, labels, weights = self._wait_for_partition(worker_id, 0) - self._sw.stop("wait_for_initial_partition") - - # Now prefetch next partitions - for partition in range(1, self._prefetched_partitions): - self._prefetch_partition(worker_id) - - for partition in range(self._num_partitions): - self._persist_log(worker_id) - num_samples_on_this_partition = len(keys) - # We (arbitrarily) prefetch the next partition when we have seen 70% of the current partition - fetch_next_partition_idx = int(num_samples_on_this_partition * 0.7) - - self._info(f"Train on partition {partition} ({num_samples_on_this_partition} samples)", worker_id) - - for idx, data_tuple in self._get_data_iterator(keys, data, labels, weights): - key, sample, label, weight = self._unpack_data_tuple(data_tuple) - - if partition < self._num_partitions - 1 and idx == fetch_next_partition_idx: - self._prefetch_partition(worker_id) - - data_tuple = self._get_data_tuple(key, sample, label, weight) - - if data_tuple is not None: # Can happen in PerClassDataset - yield data_tuple - - if partition < self._num_partitions - 1: - del keys - del data - del labels - del weights - self._info(f"Partition {partition} completed, waiting for next partition", worker_id) - self._sw.start("wait_for_later_partitions", resume=True) - keys, data, labels, weights = self._wait_for_partition(worker_id, partition + 1) - self._sw.stop("wait_for_later_partitions") - gc.collect() - - self._persist_log(worker_id) + for data_tuple in self.all_partition_generator(worker_id): + if data_tuple is not None: # Can happen in subclasses overwriting generator + yield self._get_transformed_data_tuple(*data_tuple) + self._persist_log(worker_id) \ No newline at end of file diff --git a/modyn/trainer_server/internal/dataset/per_class_online_dataset.py b/modyn/trainer_server/internal/dataset/per_class_online_dataset.py index 6a98b8b1b..40404c1a5 100644 --- a/modyn/trainer_server/internal/dataset/per_class_online_dataset.py +++ b/modyn/trainer_server/internal/dataset/per_class_online_dataset.py @@ -44,4 +44,4 @@ def _get_data_tuple(self, key: int, sample: bytes, label: int, weight: Optional[ if self.filtered_label != label: return None - return super()._get_data_tuple(key, sample, label, weight) + return super()._get_transformed_data_tuple(key, sample, label, weight) From ad425209773e1633653ddc7854096642b2dea7fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 26 Sep 2023 14:15:00 +0200 Subject: [PATCH 08/46] work on tests --- .../internal/data/test_online_dataset.py | 116 ++++++++---------- .../internal/dataset/online_dataset.py | 88 ++++++++----- 2 files changed, 107 insertions(+), 97 deletions(-) diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index 70ab51abb..1fe9ed74e 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -9,7 +9,6 @@ from modyn.storage.internal.grpc.generated.storage_pb2 import GetResponse from modyn.trainer_server.internal.dataset.key_sources import SelectorKeySource from modyn.trainer_server.internal.dataset.online_dataset import OnlineDataset -from modyn.utils import flatten from torchvision import transforms @@ -187,18 +186,18 @@ def test_get_data_from_storage( data.extend(data_list) labels.extend(label_list) - assert (keys, data, labels) == ( + assert (keys, data, labels) == ( list(range(10)), [bytes(f"sample{x}", "utf-8") for x in range(10)], list(range(10)), ) # TODO(create issue): readd when re-adding support for ordering in onlinedataset - #permuted_list = [0, 9, 6, 5, 4, 3] - #assert online_dataset._get_data_from_storage(permuted_list) == ( + # permuted_list = [0, 9, 6, 5, 4, 3] + # assert online_dataset._get_data_from_storage(permuted_list) == ( # [b"sample0", b"sample9", b"sample6", b"sample5", b"sample4", b"sample3"], # [0, 9, 6, 5, 4, 3], - #) + # ) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @@ -266,7 +265,9 @@ def test_deserialize_torchvision_transforms( @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) @patch.object( - OnlineDataset, "_get_data_from_storage", return_value=[(list(range(10)),[bytes(f"sample{x}", "utf-8") for x in range(10)], [1] * 10, 0)] + OnlineDataset, + "_get_data_from_storage", + return_value=[(list(range(10)), [bytes(f"sample{x}", "utf-8") for x in range(10)], [1] * 10, 0)], ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(10)), None)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) @@ -309,7 +310,9 @@ def test_dataset_iter( @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) @patch.object( - OnlineDataset, "_get_data_from_storage", return_value=[(list(range(10)), [bytes(f"sample{x}", "utf-8") for x in range(10)], [1] * 10, 0)] + OnlineDataset, + "_get_data_from_storage", + return_value=[(list(range(10)), [bytes(f"sample{x}", "utf-8") for x in range(10)], [1] * 10, 0)], ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(10)), None)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) @@ -352,7 +355,9 @@ def test_dataset_iter_with_parsing( @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) @patch.object( - OnlineDataset, "_get_data_from_storage", return_value=[(list(range(16)),[x.to_bytes(2, "big") for x in range(16)], [1] * 16,0)] + OnlineDataset, + "_get_data_from_storage", + return_value=[(list(range(16)), [x.to_bytes(2, "big") for x in range(16)], [1] * 16, 0)], ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(16)), None)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) @@ -396,7 +401,9 @@ def test_dataloader_dataset( @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) @patch.object( - OnlineDataset, "_get_data_from_storage", return_value=[(list(range(16)),[x.to_bytes(2, "big") for x in range(16)], [1] * 16,0)] + OnlineDataset, + "_get_data_from_storage", + return_value=[(list(range(16)), [x.to_bytes(2, "big") for x in range(16)], [1] * 16, 0)], ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(16)), [2.0] * 16)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) @@ -550,7 +557,11 @@ def test_init_transforms( tv_ds.assert_called_once() -@pytest.mark.parametrize("prefetched_partitions", [0,1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) +def iter_multi_partition_data_side_effect(keys): + yield (list(keys), [x.to_bytes(2, "big") for x in keys], [1] * len(keys), 0) + + +@pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @patch( @@ -559,24 +570,15 @@ def test_init_transforms( ) @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) -@patch.object( - OnlineDataset, - "_get_data_from_storage", - side_effect=[ - ([x for x in range(0,16)], [x.to_bytes(2, "big") for x in range(16)], [1] * 16, 0), - ([x for x in range(16,32)],[x.to_bytes(2, "big") for x in range(16, 32)], [1] * 16, 0), - ([x for x in range(32,48)],[x.to_bytes(2, "big") for x in range(32, 48)], [1] * 16, 0), - ([x for x in range(48,64)],[x.to_bytes(2, "big") for x in range(48, 64)], [1] * 16, 0), - ], -) +@patch.object(OnlineDataset, "_get_data_from_storage", side_effect=iter_multi_partition_data_side_effect) @patch.object( SelectorKeySource, "get_keys_and_weights", side_effect=[ - ([i for i in range(16)], None), - ([i for i in range(16, 32)], None), - ([i for i in range(32, 48)], None), - ([i for i in range(48, 64)], None), + (list(range(16)), None), + (list(range(16, 32)), None), + (list(range(32, 48)), None), + (list(range(48, 64)), None), ], ) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=4) @@ -603,7 +605,6 @@ def test_iter_multi_partition( log_path=None, ) dataloader = torch.utils.data.DataLoader(online_dataset, batch_size=4) - idx = 0 for idx, batch in enumerate(dataloader): assert len(batch) == 3 @@ -622,24 +623,15 @@ def test_iter_multi_partition( ) @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) -@patch.object( - OnlineDataset, - "_get_data_from_storage", - side_effect=[ - ([x.to_bytes(2, "big") for x in range(16)], [1] * 16), - ([x.to_bytes(2, "big") for x in range(16, 32)], [1] * 16), - ([x.to_bytes(2, "big") for x in range(32, 48)], [1] * 16), - ([x.to_bytes(2, "big") for x in range(48, 64)], [1] * 16), - ], -) +@patch.object(OnlineDataset, "_get_data_from_storage", side_effect=iter_multi_partition_data_side_effect) @patch.object( SelectorKeySource, "get_keys_and_weights", side_effect=[ - ([str(i) for i in range(16)], [0.9] * 16), - ([str(i) for i in range(16, 32)], [0.9] * 16), - ([str(i) for i in range(32, 48)], [0.9] * 16), - ([str(i) for i in range(48, 64)], [0.9] * 16), + (list(range(16)), [0.9] * 16), + (list(range(16, 32)), [0.9] * 16), + (list(range(32, 48)), [0.9] * 16), + (list(range(48, 64)), [0.9] * 16), ], ) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=4) @@ -671,7 +663,7 @@ def test_iter_multi_partition_weighted( idx = 0 for idx, batch in enumerate(dataloader): assert len(batch) == 4 - assert batch[0] == (str(4 * idx), str(4 * idx + 1), str(4 * idx + 2), str(4 * idx + 3)) + assert torch.equal(batch[0], torch.Tensor([4 * idx, 4 * idx + 1, 4 * idx + 2, 4 * idx + 3])) assert torch.equal(batch[1], torch.Tensor([4 * idx, 4 * idx + 1, 4 * idx + 2, 4 * idx + 3])) assert torch.equal(batch[2], torch.ones(4, dtype=torch.float64)) assert torch.equal(batch[3], 0.9 * torch.ones(4, dtype=torch.float64)) @@ -687,24 +679,15 @@ def test_iter_multi_partition_weighted( ) @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) -@patch.object( - OnlineDataset, - "_get_data_from_storage", - side_effect=[ - ([x.to_bytes(2, "big") for x in range(16)], [1] * 16), - ([x.to_bytes(2, "big") for x in range(16, 32)], [1] * 16), - ([x.to_bytes(2, "big") for x in range(32, 48)], [1] * 16), - ([x.to_bytes(2, "big") for x in range(48, 64)], [1] * 16), - ], -) +@patch.object(OnlineDataset, "_get_data_from_storage", side_effect=iter_multi_partition_data_side_effect) @patch.object( SelectorKeySource, "get_keys_and_weights", side_effect=[ - ([str(i) for i in range(16)], None), - ([str(i) for i in range(16, 32)], None), - ([str(i) for i in range(32, 48)], None), - ([str(i) for i in range(48, 64)], None), + (list(range(16)), None), + (list(range(16, 32)), None), + (list(range(32, 48)), None), + (list(range(48, 64)), None), ], ) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=4) @@ -730,31 +713,28 @@ def test_iter_multi_partition_cross( tokenizer=None, log_path=None, ) + # Note batch size 6 instead of 4 here dataloader = torch.utils.data.DataLoader(online_dataset, batch_size=6) idx = 0 for idx, batch in enumerate(dataloader): assert len(batch) == 3 if idx < 10: - assert batch[0] == ( - str(6 * idx), - str(6 * idx + 1), - str(6 * idx + 2), - str(6 * idx + 3), - str(6 * idx + 4), - str(6 * idx + 5), + assert torch.equal( + batch[0], torch.Tensor([6 * idx, 6 * idx + 1, 6 * idx + 2, 6 * idx + 3, 6 * idx + 4, 6 * idx + 5]) ) assert torch.equal( batch[1], torch.Tensor([6 * idx, 6 * idx + 1, 6 * idx + 2, 6 * idx + 3, 6 * idx + 4, 6 * idx + 5]) ) assert torch.equal(batch[2], torch.ones(6, dtype=torch.float64)) else: - assert batch[0] == ("60", "61", "62", "63") + assert torch.equal(batch[0], torch.Tensor([60, 61, 62, 63])) assert torch.equal(batch[1], torch.Tensor([60, 61, 62, 63])) assert torch.equal(batch[2], torch.ones(4, dtype=torch.float64)) assert idx == 10 +@pytest.mark.parametrize("num_workers", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -767,10 +747,7 @@ def test_iter_multi_partition_cross( @patch.object( OnlineDataset, "_get_data_from_storage", - side_effect=[ - ([x.to_bytes(2, "big") for x in range(4)], [1] * 4), - ([x.to_bytes(2, "big") for x in range(4)], [1] * 4), - ], + side_effect=iter_multi_partition_data_side_effect, ) @patch.object( SelectorKeySource, @@ -786,6 +763,7 @@ def test_iter_multi_partition_multi_workers( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + num_workers, ): if platform.system() == "Darwin": # On macOS, spawn is the default, which loses the mocks @@ -805,7 +783,7 @@ def test_iter_multi_partition_multi_workers( tokenizer=None, log_path=None, ) - dataloader = torch.utils.data.DataLoader(online_dataset, batch_size=4, num_workers=4) + dataloader = torch.utils.data.DataLoader(online_dataset, batch_size=4, num_workers=num_workers) idx = 0 for idx, batch in enumerate(dataloader): assert len(batch) == 3 @@ -825,7 +803,9 @@ def test_iter_multi_partition_multi_workers( @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) @patch.object( - OnlineDataset, "_get_data_from_storage", return_value=([x.to_bytes(2, "big") for x in range(100)], [1] * 100) + OnlineDataset, + "_get_data_from_storage", + return_value=iter([(list(range(100)), [x.to_bytes(2, "big") for x in range(100)], [1] * 100, 0)]), ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(100)), None)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index 21f2460f5..442debaac 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -1,12 +1,10 @@ -import gc +import contextlib import json import logging import os -import time -import contextlib import pathlib import threading -from typing import Any, Callable, Generator, Optional, Tuple, Union, Iterator +from typing import Any, Callable, Generator, Iterator, Optional, Tuple import grpc from modyn.common.benchmark.stopwatch import Stopwatch @@ -74,7 +72,7 @@ def __init__( self._pref_started: dict[int, bool] = {} self._thread_data_container: dict[int, dict[str, Any]] = {} self._partition_locks: dict[int, threading.Lock] = {} - self._partition_signals: dict[int, threading.Condition] = {} # Should use the lock out of partition_locks + self._partition_signals: dict[int, threading.Condition] = {} # Should use the lock out of partition_locks self._partition_valid_until: dict[int, int] = {} self._partition_valid: dict[int, bool] = {} self._next_partition_to_fetch = 0 @@ -134,8 +132,9 @@ def _info(self, msg: str, worker_id: Optional[int]) -> None: # pragma: no cover def _debug(self, msg: str, worker_id: Optional[int]) -> None: # pragma: no cover logger.debug(f"[Training {self._training_id}][PL {self._pipeline_id}][Worker {worker_id}] {msg}") - - def _get_data_from_storage(self, selector_keys: list[int]) -> Iterator[tuple[list[int], list[bytes], list[int], int]]: + def _get_data_from_storage( + self, selector_keys: list[int] + ) -> Iterator[tuple[list[int], list[bytes], list[int], int]]: req = GetRequest(dataset_id=self._dataset_id, keys=selector_keys) stopw = Stopwatch() @@ -144,8 +143,18 @@ def _get_data_from_storage(self, selector_keys: list[int]) -> Iterator[tuple[lis for _, response in enumerate(self._storagestub.Get(req)): yield list(response.keys), list(response.samples), list(response.labels), stopw.stop("ResponseTime") stopw.start("ResponseTime", overwrite=True) - - def _get_data(self, data_container: dict, worker_id: int, partition_id: int, partition_valid: Optional[dict], partition_valid_until: Optional[dict], partition_locks: Optional[dict], partition_signals: Optional[dict]) -> None: + + # pylint: disable=too-many-locals + def _get_data( + self, + data_container: dict, + worker_id: int, + partition_id: int, + partition_valid: Optional[dict], + partition_valid_until: Optional[dict], + partition_locks: Optional[dict], + partition_signals: Optional[dict], + ) -> None: get_data_log = {} self._sw.start(f"GetKeysAndWeightsPart{partition_id}", overwrite=True) keys, weights = self._key_source.get_keys_and_weights(worker_id, partition_id) @@ -156,7 +165,7 @@ def _get_data(self, data_container: dict, worker_id: int, partition_id: int, par self._sw.start(f"GetDataPart{partition_id}", overwrite=True) all_response_times = [] - key_weight_map = { key: weights[idx] for idx, key in enumerate(keys) } if weights is not None else None + key_weight_map = {key: weights[idx] for idx, key in enumerate(keys)} if weights is not None else None for data_tuple in self._get_data_from_storage(keys): stor_keys, data, labels, response_time = data_tuple @@ -166,7 +175,11 @@ def _get_data(self, data_container: dict, worker_id: int, partition_id: int, par data_container["data"].extend(data) data_container["keys"].extend(stor_keys) data_container["labels"].extend(labels) - data_container["weights"].extend([key_weight_map[key] for key in stor_keys] if key_weight_map is not None else [None for _ in range(len(stor_keys))]) + data_container["weights"].extend( + [key_weight_map[key] for key in stor_keys] + if key_weight_map is not None + else [None for _ in range(len(stor_keys))] + ) if partition_valid_until is not None: partition_valid_until[partition_id] += num_items @@ -182,8 +195,9 @@ def _get_data(self, data_container: dict, worker_id: int, partition_id: int, par with partition_locks[partition_id]: partition_valid[partition_id] = True - - def _get_transformed_data_tuple(self, key: int, sample: bytes, label: int, weight: Optional[float]) -> Optional[Tuple]: + def _get_transformed_data_tuple( + self, key: int, sample: bytes, label: int, weight: Optional[float] + ) -> Optional[Tuple]: assert self._uses_weights is not None self._sw.start("transform", resume=True) # mypy complains here because _transform has unknown type, which is ok @@ -221,15 +235,30 @@ def _prefetch_partition(self, worker_id: int) -> None: self._next_partition_to_fetch not in self._data_threads ), f"Prefetching for partition {self._next_partition_to_fetch} has already been started" - self._thread_data_container[self._next_partition_to_fetch] = {"data": [], "keys": [], "labels": [], "weights": []} + self._thread_data_container[self._next_partition_to_fetch] = { + "data": [], + "keys": [], + "labels": [], + "weights": [], + } self._partition_valid[self._next_partition_to_fetch] = False self._partition_valid_until[self._next_partition_to_fetch] = -1 self._partition_locks[self._next_partition_to_fetch] = threading.Lock() - self._partition_signals[self._next_partition_to_fetch] = threading.Condition(self._partition_locks[self._next_partition_to_fetch]) + self._partition_signals[self._next_partition_to_fetch] = threading.Condition( + self._partition_locks[self._next_partition_to_fetch] + ) self._data_threads[self._next_partition_to_fetch] = threading.Thread( target=self._get_data, - args=(self._thread_data_container[self._next_partition_to_fetch], worker_id, self._next_partition_to_fetch, self._partition_valid, self._partition_valid_until, self._partition_locks, self._partition_signals), + args=( + self._thread_data_container[self._next_partition_to_fetch], + worker_id, + self._next_partition_to_fetch, + self._partition_valid, + self._partition_valid_until, + self._partition_locks, + self._partition_signals, + ), ) self._data_threads[self._next_partition_to_fetch].start() @@ -256,32 +285,36 @@ def _partition_max_index(self, partition_id: int) -> int: def _get_partition_data(self, last_idx: int, max_idx: int, partition_id: int) -> Generator: for idx in range(last_idx + 1, max_idx + 1): - yield self._thread_data_container[partition_id]["keys"][idx], self._thread_data_container[partition_id]["data"][idx], self._thread_data_container[partition_id]["labels"][idx], self._thread_data_container[partition_id]["weights"][idx] + yield self._thread_data_container[partition_id]["keys"][idx], self._thread_data_container[partition_id][ + "data" + ][idx], self._thread_data_container[partition_id]["labels"][idx], self._thread_data_container[partition_id][ + "weights" + ][ + idx + ] def _wait_for_new_partition_data(self, partition_id: int) -> None: with self._partition_signals[partition_id]: - self._partition_signals[partition_id].wait(1) # In case we do not get woken up, we at most waste a second + self._partition_signals[partition_id].wait(1) # In case we do not get woken up, we at most waste a second def prefetched_partition_generator(self, worker_id: int, partition_id: int) -> Generator: assert self._pref_started[partition_id], f"Prefetching for partition {partition_id} has not been started" last_idx = -1 - - while not self._is_partition_fetched(partition_id): + while not self._is_partition_fetched(partition_id): max_idx = self._partition_max_index(partition_id) - if max_idx <= last_idx: # No new data + if max_idx <= last_idx: # No new data self._wait_for_new_partition_data(partition_id) yield from self._get_partition_data(last_idx, max_idx, partition_id) last_idx = max_idx - + # Yield potential remaining data self._info(f"Joining thread for partition {partition_id}", worker_id) self._data_threads[partition_id].join() self._info(f"Thread for partition {partition_id} joined", worker_id) max_idx = self._partition_max_index(partition_id) yield from self._get_partition_data(last_idx, max_idx, partition_id) - return def all_partition_generator(self, worker_id: int) -> Generator: for _ in range(self._prefetched_partitions): @@ -299,10 +332,6 @@ def all_partition_generator(self, worker_id: int) -> Generator: else: yield from self._fetch_partition_noprefetch(worker_id, partition_id) - - - - # pylint: disable=too-many-locals, too-many-branches, too-many-statements def __iter__(self) -> Generator: @@ -348,6 +377,7 @@ def __iter__(self) -> Generator: self._prefetched_partitions = min(self._prefetched_partitions, self._num_partitions) for data_tuple in self.all_partition_generator(worker_id): - if data_tuple is not None: # Can happen in subclasses overwriting generator + if data_tuple is not None: # Can happen in subclasses overwriting generator yield self._get_transformed_data_tuple(*data_tuple) - self._persist_log(worker_id) \ No newline at end of file + + self._persist_log(worker_id) From 2d53c0b41a490e83803b784c1f3ee4234481c020 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 26 Sep 2023 14:41:13 +0200 Subject: [PATCH 09/46] fixes --- .../trainer_server/internal/data/test_online_dataset.py | 6 +++++- .../internal/data/test_per_class_online_dataset.py | 2 +- modyn/trainer_server/internal/dataset/online_dataset.py | 4 ++-- .../internal/dataset/per_class_online_dataset.py | 4 +++- 4 files changed, 11 insertions(+), 5 deletions(-) diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index 1fe9ed74e..3231e3a0a 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -447,7 +447,11 @@ def test_dataloader_dataset_weighted( ) @patch("modyn.trainer_server.internal.dataset.online_dataset.grpc_connection_established", return_value=True) @patch.object(grpc, "insecure_channel", return_value=None) -@patch.object(OnlineDataset, "_get_data_from_storage", return_value=([x.to_bytes(2, "big") for x in range(4)], [1] * 4)) +@patch.object( + OnlineDataset, + "_get_data_from_storage", + return_value=[(list(range(4)), [x.to_bytes(2, "big") for x in range(4)], [1] * 4, 0)], +) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(4)), None)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) def test_dataloader_dataset_multi_worker( diff --git a/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py index 5c9cf10d2..26b3dd3fc 100644 --- a/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py @@ -47,7 +47,7 @@ def Get(self, request): # pylint: disable=invalid-name @patch.object( PerClassOnlineDataset, "_get_data_from_storage", - return_value=([x.to_bytes(2, "big") for x in range(16)], [0, 1, 2, 3, 0, 0, 0, 1] * 2), + return_value=[(list(range(16)), [x.to_bytes(2, "big") for x in range(16)], [0, 1, 2, 3, 0, 0, 0, 1] * 2, 0)], ) @patch.object(SelectorKeySource, "get_keys_and_weights", return_value=(list(range(16)), None)) @patch.object(SelectorKeySource, "get_num_data_partitions", return_value=1) diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index 442debaac..0d6f2957c 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -377,7 +377,7 @@ def __iter__(self) -> Generator: self._prefetched_partitions = min(self._prefetched_partitions, self._num_partitions) for data_tuple in self.all_partition_generator(worker_id): - if data_tuple is not None: # Can happen in subclasses overwriting generator - yield self._get_transformed_data_tuple(*data_tuple) + if (transformed_tuple := self._get_transformed_data_tuple(*data_tuple)) is not None: + yield transformed_tuple self._persist_log(worker_id) diff --git a/modyn/trainer_server/internal/dataset/per_class_online_dataset.py b/modyn/trainer_server/internal/dataset/per_class_online_dataset.py index 40404c1a5..5a3c6fbec 100644 --- a/modyn/trainer_server/internal/dataset/per_class_online_dataset.py +++ b/modyn/trainer_server/internal/dataset/per_class_online_dataset.py @@ -39,7 +39,9 @@ def __init__( assert initial_filtered_label is not None self.filtered_label = initial_filtered_label - def _get_data_tuple(self, key: int, sample: bytes, label: int, weight: Optional[float]) -> Optional[Tuple]: + def _get_transformed_data_tuple( + self, key: int, sample: bytes, label: int, weight: Optional[float] + ) -> Optional[Tuple]: assert self.filtered_label is not None if self.filtered_label != label: From e20a59dc86045a240ac7f8915652f0f34578a272 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 26 Sep 2023 16:50:40 +0200 Subject: [PATCH 10/46] maybe fix test --- .../tests/trainer_server/internal/data/test_online_dataset.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index 3231e3a0a..fe88e7327 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -794,7 +794,9 @@ def test_iter_multi_partition_multi_workers( assert torch.equal(batch[0], torch.Tensor([0, 1, 2, 3])) assert torch.equal(batch[1], torch.Tensor([0, 1, 2, 3])) assert torch.equal(batch[2], torch.ones(4, dtype=int)) - assert idx == 7 + + # each worker gets 8 items from get_keys_and_weights; batch size 4; minus one for zero indexing + assert idx == ((min(num_workers, 1) * 32) / 4) - 1 @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) From daf9e5c3f0732e017d32b4421967291d795eb1de Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 26 Sep 2023 16:56:50 +0200 Subject: [PATCH 11/46] did i just confuse min for max... --- modyn/tests/trainer_server/internal/data/test_online_dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index fe88e7327..40128abda 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -796,7 +796,7 @@ def test_iter_multi_partition_multi_workers( assert torch.equal(batch[2], torch.ones(4, dtype=int)) # each worker gets 8 items from get_keys_and_weights; batch size 4; minus one for zero indexing - assert idx == ((min(num_workers, 1) * 32) / 4) - 1 + assert idx == ((max(num_workers, 1) * 32) / 4) - 1 @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) From 67004217d0a6a27e691d832a49cbed889af1faf8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 26 Sep 2023 17:04:17 +0200 Subject: [PATCH 12/46] another try --- .../internal/data/test_online_dataset.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index 40128abda..c307ddde8 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -438,6 +438,7 @@ def test_dataloader_dataset_weighted( assert torch.equal(batch[3], 2 * torch.ones(4, dtype=torch.float64)) +@pytest.mark.parametrize("num_workers", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -462,6 +463,7 @@ def test_dataloader_dataset_multi_worker( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + num_workers, ): if platform.system() == "Darwin": # On macOS, spawn is the default, which loses the mocks @@ -481,7 +483,7 @@ def test_dataloader_dataset_multi_worker( tokenizer=None, log_path=None, ) - dataloader = torch.utils.data.DataLoader(online_dataset, batch_size=4, num_workers=4) + dataloader = torch.utils.data.DataLoader(online_dataset, batch_size=4, num_workers=num_workers) for batch in dataloader: assert len(batch) == 3 assert torch.equal(batch[0], torch.Tensor([0, 1, 2, 3])) @@ -795,8 +797,10 @@ def test_iter_multi_partition_multi_workers( assert torch.equal(batch[1], torch.Tensor([0, 1, 2, 3])) assert torch.equal(batch[2], torch.ones(4, dtype=int)) - # each worker gets 8 items from get_keys_and_weights; batch size 4; minus one for zero indexing - assert idx == ((max(num_workers, 1) * 32) / 4) - 1 + if num_workers % 2 == 0: + # only test this for even number of workers to avoid fractions + # each worker gets 8 items from get_keys_and_weights; batch size 4; minus one for zero indexing + assert idx == ((max(num_workers, 1) * 8) / 4) - 1 @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) From da4f60f26e07e9ad445ca755ef75ee99a8a372cf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 26 Sep 2023 18:26:21 +0200 Subject: [PATCH 13/46] add onlinedataset integrationtest --- .../online_dataset/test_online_dataset.py | 366 ++++++++++++++++++ integrationtests/run.sh | 2 + .../storage/integrationtest_storage.py | 2 +- .../internal/dataset/online_dataset.py | 14 +- 4 files changed, 379 insertions(+), 5 deletions(-) create mode 100644 integrationtests/online_dataset/test_online_dataset.py diff --git a/integrationtests/online_dataset/test_online_dataset.py b/integrationtests/online_dataset/test_online_dataset.py new file mode 100644 index 000000000..7b83480f9 --- /dev/null +++ b/integrationtests/online_dataset/test_online_dataset.py @@ -0,0 +1,366 @@ +import io +import json +import math +import os +import pathlib +import random +import shutil +import time +from typing import Iterable, Tuple + +import grpc +import modyn.storage.internal.grpc.generated.storage_pb2 as storage_pb2 +import torch +import yaml +from modyn.selector.internal.grpc.generated.selector_pb2 import ( + DataInformRequest, + JsonString, + RegisterPipelineRequest, +) +from modyn.selector.internal.grpc.generated.selector_pb2_grpc import SelectorStub +from modyn.storage.internal.grpc.generated.storage_pb2 import ( + DatasetAvailableRequest, + GetDatasetSizeRequest, + GetDatasetSizeResponse, + GetNewDataSinceRequest, + GetNewDataSinceResponse, + RegisterNewDatasetRequest, +) +from modyn.storage.internal.grpc.generated.storage_pb2_grpc import StorageStub +from modyn.trainer_server.internal.dataset.data_utils import prepare_dataloaders +from modyn.utils import grpc_connection_established +from PIL import Image +from torchvision import transforms + +SCRIPT_PATH = pathlib.Path(os.path.realpath(__file__)) + +TIMEOUT = 120 # seconds +CONFIG_FILE = SCRIPT_PATH.parent.parent.parent / "modyn" / "config" / "examples" / "modyn_config.yaml" +# The following path leads to a directory that is mounted into the docker container and shared with the +# storage container. +DATASET_PATH = pathlib.Path("/app") / "storage" / "datasets" / "test_dataset" + +# Because we have no mapping of file to key (happens in the storage service), we have to keep +# track of the images we added to the dataset ourselves and compare them to the images we get +# from the storage service. +FIRST_ADDED_IMAGES = [] +SECOND_ADDED_IMAGES = [] +IMAGE_UPDATED_TIME_STAMPS = [] + + +def get_modyn_config() -> dict: + with open(CONFIG_FILE, "r", encoding="utf-8") as config_file: + config = yaml.safe_load(config_file) + + return config + + +def connect_to_selector_servicer() -> grpc.Channel: + selector_address = get_selector_address() + selector_channel = grpc.insecure_channel(selector_address) + + if not grpc_connection_established(selector_channel): + raise ConnectionError(f"Could not establish gRPC connection to selector at {selector_address}.") + + return selector_channel + + +def get_storage_address() -> str: + config = get_modyn_config() + return f"{config['storage']['hostname']}:{config['storage']['port']}" + + +def get_selector_address() -> str: + config = get_modyn_config() + return f"{config['selector']['hostname']}:{config['selector']['port']}" + + +def connect_to_storage() -> grpc.Channel: + storage_address = get_storage_address() + storage_channel = grpc.insecure_channel(storage_address) + + if not grpc_connection_established(storage_channel) or storage_channel is None: + raise ConnectionError(f"Could not establish gRPC connection to storage at {storage_address}.") + + return storage_channel + + +def register_new_dataset() -> None: + storage_channel = connect_to_storage() + + storage = StorageStub(storage_channel) + + request = RegisterNewDatasetRequest( + base_path=str(DATASET_PATH), + dataset_id="test_dataset", + description="Test dataset for integration tests.", + file_wrapper_config=json.dumps({"file_extension": ".png", "label_file_extension": ".txt"}), + file_wrapper_type="SingleSampleFileWrapper", + filesystem_wrapper_type="LocalFilesystemWrapper", + version="0.1.0", + ) + + response = storage.RegisterNewDataset(request) + + assert response.success, "Could not register new dataset." + + +def check_dataset_availability() -> None: + storage_channel = connect_to_storage() + + storage = StorageStub(storage_channel) + + request = DatasetAvailableRequest(dataset_id="test_dataset") + response = storage.CheckAvailability(request) + + assert response.available, "Dataset is not available." + + +def check_dataset_size(expected_size: int) -> None: + storage_channel = connect_to_storage() + + storage = StorageStub(storage_channel) + request = GetDatasetSizeRequest(dataset_id="test_dataset") + response: GetDatasetSizeResponse = storage.GetDatasetSize(request) + + assert response.success, "Dataset is not available." + assert response.num_keys == expected_size + + +def check_dataset_size_invalid() -> None: + storage_channel = connect_to_storage() + + storage = StorageStub(storage_channel) + request = GetDatasetSizeRequest(dataset_id="unknown_dataset") + response: GetDatasetSizeResponse = storage.GetDatasetSize(request) + + assert not response.success, "Dataset is available (even though it should not be)." + + +def check_get_current_timestamp() -> None: + storage_channel = connect_to_storage() + storage = StorageStub(storage_channel) + empty = storage_pb2.google_dot_protobuf_dot_empty__pb2.Empty() + response = storage.GetCurrentTimestamp(empty) + + assert response.timestamp > 0, "Timestamp is not valid." + + +def create_dataset_dir() -> None: + pathlib.Path(DATASET_PATH).mkdir(parents=True, exist_ok=True) + + +def cleanup_dataset_dir() -> None: + shutil.rmtree(DATASET_PATH) + + +def cleanup_storage_database() -> None: + storage_channel = connect_to_storage() + storage = StorageStub(storage_channel) + request = DatasetAvailableRequest(dataset_id="test_dataset") + response = storage.DeleteDataset(request) + + assert response.success, "Could not cleanup storage database." + + +def add_image_to_dataset(image: Image, name: str) -> None: + image.save(DATASET_PATH / name) + IMAGE_UPDATED_TIME_STAMPS.append(int(round(os.path.getmtime(DATASET_PATH / name) * 1000))) + + +def create_random_image() -> Image: + image = Image.new("RGB", (100, 100)) + random_x = random.randint(0, 99) + random_y = random.randint(0, 99) + + random_r = random.randint(0, 254) + random_g = random.randint(0, 254) + random_b = random.randint(0, 254) + + image.putpixel((random_x, random_y), (random_r, random_g, random_b)) + + return image + + +def add_images_to_dataset(start_number: int, end_number: int, images_added: list[bytes]) -> None: + create_dataset_dir() + + for i in range(start_number, end_number): + image = create_random_image() + add_image_to_dataset(image, f"image_{i}.png") + images_added.append(image.tobytes()) + with open(DATASET_PATH / f"image_{i}.txt", "w") as label_file: + label_file.write(f"{i}") + + +def prepare_selector(num_dataworkers: int, keys: list[int]) -> Tuple[int, int]: + selector_channel = connect_to_selector_servicer() + selector = SelectorStub(selector_channel) + # We test the NewData strategy for finetuning on the new data, i.e., we reset without limit + # We also enforce high partitioning (maximum_keys_in_memory == 2) to ensure that works + + strategy_config = { + "name": "NewDataStrategy", + "maximum_keys_in_memory": 2, + "config": {"limit": -1, "reset_after_trigger": True}, + } + + pipeline_id = selector.register_pipeline( + RegisterPipelineRequest( + num_workers=max(num_dataworkers, 1), selection_strategy=JsonString(value=json.dumps(strategy_config)) + ) + ).pipeline_id + + trigger_id = selector.inform_data_and_trigger( + DataInformRequest( + pipeline_id=pipeline_id, + keys=keys, + timestamps=[2 for _ in range(len(keys))], + labels=[3 for _ in range(len(keys))], + ) + ).trigger_id + + return pipeline_id, trigger_id + + +def get_new_data_since(timestamp: int) -> Iterable[GetNewDataSinceResponse]: + storage_channel = connect_to_storage() + + storage = StorageStub(storage_channel) + + request = GetNewDataSinceRequest( + dataset_id="test_dataset", + timestamp=timestamp, + ) + + responses = storage.GetNewDataSince(request) + return responses + + +def get_data_keys() -> list[int]: + response = None + keys = [] + for i in range(60): + responses = list(get_new_data_since(0)) + assert len(responses) < 2, f"Received batched response, shouldn't happen: {responses}" + if len(responses) == 1: + response = responses[0] + keys = list(response.keys) + if len(keys) == 10: + break + time.sleep(1) + + assert response is not None, "Did not get any response from Storage" + assert len(keys) == 10, f"Not all images were returned. Images returned: {response.keys}" + + return keys + + +def get_bytes_parser() -> str: + return """ + from PIL import Image + import io + def bytes_parser_function(data: bytes) -> Image: + return Image.open(io.BytesIO(data)).convert("RGB")""" + + +def tensor_in_list(tensor: torch.Tensor, tensor_list: list[torch.Tensor]) -> bool: + return any([(tensor == c_).all() for c_ in tensor_list]) + + +def test_dataset_impl( + num_dataworkers: int, + batch_size: int, + prefetched_partitions: int, + pipeline_id: int, + trigger_id: int, + items: list[int], +) -> None: + dataloader, _ = prepare_dataloaders( + pipeline_id, + trigger_id, + "test_dataset", + num_dataworkers, + batch_size, + get_bytes_parser(), + ["transforms.ToTensor()"], + get_storage_address(), + get_selector_address(), + 42, + prefetched_partitions, + None, + None, + ) + + expected_batches = math.ceil(len(items) / batch_size) + all_samples = [] + all_data = [] + all_labels = [] + + for batch_number, batch in enumerate(dataloader): + sample_ids = batch[0] + if isinstance(sample_ids, torch.Tensor): + sample_ids = sample_ids.tolist() + elif isinstance(sample_ids, tuple): + sample_ids = list(sample_ids) + + assert isinstance(sample_ids, list), "Cannot parse result from DataLoader" + assert isinstance(batch[1], torch.Tensor) and isinstance(batch[2], torch.Tensor) + + all_samples.extend(sample_ids) + all_data.extend(batch[1].tolist()) + all_labels.extend(batch[2].tolist()) + + assert len(all_samples) == len(items) + assert len(all_data) == len(items) + assert len(all_data) == len(items) + assert batch_number + 1 == expected_batches, ( + f"[{num_dataworkers}][{batch_size}][{prefetched_partitions}]" + + f"Wrong number of batches: {batch_number + 1}. num_items = {len(items)}" + ) + + assert set(all_samples) == set(items) + assert set(all_labels) == set(range(len(items))) + + trans = transforms.Compose([transforms.ToTensor()]) + + for idx, image in enumerate(FIRST_ADDED_IMAGES): + parsed_image = trans(Image.open(io.BytesIO(image))) + assert tensor_in_list( + parsed_image, all_data + ), f"Could not find image {idx} in all_data, all_samples = {all_samples}" + + +def test_dataset() -> None: + NUM_IMAGES = 10 + + check_get_current_timestamp() # Check if the storage service is available. + create_dataset_dir() + add_images_to_dataset(0, NUM_IMAGES, FIRST_ADDED_IMAGES) # Add images to the dataset. + register_new_dataset() + check_dataset_availability() # Check if the dataset is available. + check_dataset_size_invalid() + + keys = get_data_keys() + + for num_dataworkers in [0, 1, 2, 4, 8, 16]: + pipeline_id, trigger_id = prepare_selector(num_dataworkers, keys) + for prefetched_partitions in [0, 1, 2, 3, 4, 5, 999]: + for batch_size in [1, 2, 10]: + print( + f"Testing num_workers = {num_dataworkers}, partitions = {prefetched_partitions}," + + f"batch_size = {batch_size}" + ) + test_dataset_impl(num_dataworkers, batch_size, prefetched_partitions, pipeline_id, trigger_id, keys) + + +def main() -> None: + try: + test_dataset() + finally: + cleanup_dataset_dir() + cleanup_storage_database() + + +if __name__ == "__main__": + main() diff --git a/integrationtests/run.sh b/integrationtests/run.sh index 6826ae1cb..a797ff25f 100755 --- a/integrationtests/run.sh +++ b/integrationtests/run.sh @@ -13,6 +13,8 @@ python $SCRIPT_DIR/storage/integrationtest_storage.py python $SCRIPT_DIR/storage/integrationtest_storage_csv.py echo "Running selector integration tests" python $SCRIPT_DIR/selector/integrationtest_selector.py +echo "Running online datasets integration tests" +python $SCRIPT_DIR/online_dataset/test_online_dataset.py echo "Running model storage integration tests" python $SCRIPT_DIR/model_storage/integrationtest_model_storage.py echo "Successfuly ran all integration tests." \ No newline at end of file diff --git a/integrationtests/storage/integrationtest_storage.py b/integrationtests/storage/integrationtest_storage.py index edadc7699..86693bbbc 100644 --- a/integrationtests/storage/integrationtest_storage.py +++ b/integrationtests/storage/integrationtest_storage.py @@ -271,7 +271,7 @@ def test_storage() -> None: add_images_to_dataset(10, 20, SECOND_ADDED_IMAGES) # Add more images to the dataset. - for i in range(20): + for i in range(60): responses = list(get_new_data_since(IMAGE_UPDATED_TIME_STAMPS[9] + 1)) assert len(responses) < 2, f"Received batched response, shouldn't happen: {responses}" if len(responses) == 1: diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index 0d6f2957c..7b07c11eb 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -266,7 +266,9 @@ def _prefetch_partition(self, worker_id: int) -> None: self._next_partition_to_fetch += 1 - def _fetch_partition_noprefetch(self, worker_id: int, partition_id: int) -> Generator: + def _fetch_partition_noprefetch( + self, worker_id: int, partition_id: int + ) -> Iterator[tuple[int, bytes, int, Optional[float]]]: assert self._prefetched_partitions < 1 container: dict[str, Any] = {"data": [], "keys": [], "labels": [], "weights": []} self._get_data(container, worker_id, partition_id, None, None, None, None) @@ -283,7 +285,9 @@ def _partition_max_index(self, partition_id: int) -> int: with self._partition_locks[partition_id]: return self._partition_valid_until[partition_id] - def _get_partition_data(self, last_idx: int, max_idx: int, partition_id: int) -> Generator: + def _get_partition_data( + self, last_idx: int, max_idx: int, partition_id: int + ) -> Iterator[tuple[int, bytes, int, Optional[float]]]: for idx in range(last_idx + 1, max_idx + 1): yield self._thread_data_container[partition_id]["keys"][idx], self._thread_data_container[partition_id][ "data" @@ -297,7 +301,9 @@ def _wait_for_new_partition_data(self, partition_id: int) -> None: with self._partition_signals[partition_id]: self._partition_signals[partition_id].wait(1) # In case we do not get woken up, we at most waste a second - def prefetched_partition_generator(self, worker_id: int, partition_id: int) -> Generator: + def prefetched_partition_generator( + self, worker_id: int, partition_id: int + ) -> Iterator[tuple[int, bytes, int, Optional[float]]]: assert self._pref_started[partition_id], f"Prefetching for partition {partition_id} has not been started" last_idx = -1 @@ -316,7 +322,7 @@ def prefetched_partition_generator(self, worker_id: int, partition_id: int) -> G max_idx = self._partition_max_index(partition_id) yield from self._get_partition_data(last_idx, max_idx, partition_id) - def all_partition_generator(self, worker_id: int) -> Generator: + def all_partition_generator(self, worker_id: int) -> Iterator[tuple[int, bytes, int, Optional[float]]]: for _ in range(self._prefetched_partitions): self._prefetch_partition(worker_id) From 0c3745a16f87f0ad7dfc1d5602ada42fc486c9b0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 26 Sep 2023 22:30:59 +0200 Subject: [PATCH 14/46] online dataset integration test --- .../online_dataset/test_online_dataset.py | 45 ++++++++++--------- 1 file changed, 24 insertions(+), 21 deletions(-) diff --git a/integrationtests/online_dataset/test_online_dataset.py b/integrationtests/online_dataset/test_online_dataset.py index 7b83480f9..9b11339e4 100644 --- a/integrationtests/online_dataset/test_online_dataset.py +++ b/integrationtests/online_dataset/test_online_dataset.py @@ -1,4 +1,3 @@ -import io import json import math import os @@ -12,11 +11,7 @@ import modyn.storage.internal.grpc.generated.storage_pb2 as storage_pb2 import torch import yaml -from modyn.selector.internal.grpc.generated.selector_pb2 import ( - DataInformRequest, - JsonString, - RegisterPipelineRequest, -) +from modyn.selector.internal.grpc.generated.selector_pb2 import DataInformRequest, JsonString, RegisterPipelineRequest from modyn.selector.internal.grpc.generated.selector_pb2_grpc import SelectorStub from modyn.storage.internal.grpc.generated.storage_pb2 import ( DatasetAvailableRequest, @@ -258,10 +253,10 @@ def get_data_keys() -> list[int]: def get_bytes_parser() -> str: return """ - from PIL import Image - import io - def bytes_parser_function(data: bytes) -> Image: - return Image.open(io.BytesIO(data)).convert("RGB")""" +from PIL import Image +import io +def bytes_parser_function(data: bytes) -> Image: + return Image.open(io.BytesIO(data)).convert("RGB")""" def tensor_in_list(tensor: torch.Tensor, tensor_list: list[torch.Tensor]) -> bool: @@ -292,7 +287,10 @@ def test_dataset_impl( None, ) - expected_batches = math.ceil(len(items) / batch_size) + expected_min_batches = math.floor(len(items) / batch_size) + # max one excess batch per worker + expected_max_batches = expected_min_batches if num_dataworkers <= 1 else expected_min_batches + num_dataworkers + all_samples = [] all_data = [] all_labels = [] @@ -308,27 +306,32 @@ def test_dataset_impl( assert isinstance(batch[1], torch.Tensor) and isinstance(batch[2], torch.Tensor) all_samples.extend(sample_ids) - all_data.extend(batch[1].tolist()) + for sample in batch[1]: + all_data.append(sample) # iterate over batch dimension to extract samples all_labels.extend(batch[2].tolist()) assert len(all_samples) == len(items) + assert len(all_labels) == len(items) assert len(all_data) == len(items) - assert len(all_data) == len(items) - assert batch_number + 1 == expected_batches, ( + + assert expected_min_batches <= batch_number + 1 <= expected_max_batches, ( f"[{num_dataworkers}][{batch_size}][{prefetched_partitions}]" - + f"Wrong number of batches: {batch_number + 1}. num_items = {len(items)}" + + f"Wrong number of batches: {batch_number + 1}. num_items = {len(items)}." + + f"expected_min = {expected_min_batches}, expected_max = {expected_max_batches}" ) assert set(all_samples) == set(items) assert set(all_labels) == set(range(len(items))) - trans = transforms.Compose([transforms.ToTensor()]) + trans = transforms.Compose([transforms.ToPILImage()]) + + assert len(FIRST_ADDED_IMAGES) == len(all_data) - for idx, image in enumerate(FIRST_ADDED_IMAGES): - parsed_image = trans(Image.open(io.BytesIO(image))) - assert tensor_in_list( - parsed_image, all_data - ), f"Could not find image {idx} in all_data, all_samples = {all_samples}" + for idx, image_tensor in enumerate(all_data): + pil_image = trans(image_tensor).convert("RGB") + image_bytes = pil_image.tobytes() + if image_bytes not in FIRST_ADDED_IMAGES: + raise ValueError(f"Could not find image {idx} in created images, all_samples = {all_samples}") def test_dataset() -> None: From d097bf5005296b0da73135239d83dac2d38a40e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 27 Sep 2023 17:56:17 +0200 Subject: [PATCH 15/46] let's try multiprocessing for storage grpc and selector grpc --- .../metadata_database_connection.py | 5 +- modyn/metadata_database/models/pipelines.py | 3 +- .../selector/internal/grpc/selector_server.py | 86 +++++++++++++---- modyn/selector/internal/selector_manager.py | 57 +++++++++-- modyn/selector/selector.py | 31 +++++- modyn/storage/internal/grpc/grpc_server.py | 89 ++++++++++++++---- .../internal/grpc/storage_grpc_servicer.py | 10 ++ .../internal/grpc/test_model_storage.database | Bin 0 -> 57344 bytes 8 files changed, 232 insertions(+), 49 deletions(-) create mode 100644 modyn/tests/model_storage/internal/grpc/test_model_storage.database diff --git a/modyn/metadata_database/metadata_database_connection.py b/modyn/metadata_database/metadata_database_connection.py index ac337bc1f..311a8c3f0 100644 --- a/modyn/metadata_database/metadata_database_connection.py +++ b/modyn/metadata_database/metadata_database_connection.py @@ -67,16 +67,17 @@ def create_tables(self) -> None: """ MetadataBase.metadata.create_all(self.engine) - def register_pipeline(self, num_workers: int) -> int: + def register_pipeline(self, num_workers: int, selection_strategy: str) -> int: """Register a new pipeline in the database. Args: num_workers (int): Number of workers in the pipeline. + selection_strategy (str): The selection strategy to use Returns: int: Id of the newly created pipeline. """ - pipeline = Pipeline(num_workers=num_workers) + pipeline = Pipeline(num_workers=num_workers, selection_strategy=selection_strategy) self.session.add(pipeline) self.session.commit() pipeline_id = pipeline.pipeline_id diff --git a/modyn/metadata_database/models/pipelines.py b/modyn/metadata_database/models/pipelines.py index 4094b3f95..cd8370c7e 100644 --- a/modyn/metadata_database/models/pipelines.py +++ b/modyn/metadata_database/models/pipelines.py @@ -1,7 +1,7 @@ """Pipeline model.""" from modyn.metadata_database.metadata_base import MetadataBase -from sqlalchemy import Column, Integer +from sqlalchemy import Column, Integer, Text class Pipeline(MetadataBase): @@ -12,6 +12,7 @@ class Pipeline(MetadataBase): __table_args__ = {"extend_existing": True} pipeline_id = Column("pipeline_id", Integer, primary_key=True) num_workers = Column("num_workers", Integer, nullable=False) + selection_strategy = Column("selection_strategy", Text, nullable=False) def __repr__(self) -> str: """Return string representation.""" diff --git a/modyn/selector/internal/grpc/selector_server.py b/modyn/selector/internal/grpc/selector_server.py index 3ca0fc4d9..ead69a692 100644 --- a/modyn/selector/internal/grpc/selector_server.py +++ b/modyn/selector/internal/grpc/selector_server.py @@ -1,4 +1,10 @@ +import contextlib +import datetime import logging +import multiprocessing as mp +import os +import socket +import time from concurrent import futures import grpc @@ -10,32 +16,72 @@ logger = logging.getLogger(__name__) +@contextlib.contextmanager +def _reserve_port(port: str): + """Find and reserve a port for all subprocesses to use.""" + sock = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) + sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) + if sock.getsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT) == 0: + raise RuntimeError("Failed to set SO_REUSEPORT.") + sock.bind(("", int(port))) + try: + assert sock.getsockname()[1] == int(port) + yield port + finally: + sock.close() + + +def _wait_forever(server): + try: + while True: + time.sleep(datetime.timedelta(days=1).total_seconds()) + except KeyboardInterrupt: + server.stop(None) + + +def _run_server(bind_address, selector_manager, sample_batch_size): + """Start a server in a subprocess.""" + logging.info(f"[{os.getpid()}] Starting new server.") + + server = grpc.server( + futures.ThreadPoolExecutor( + max_workers=16, + ), + options=[ + ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), + ("grpc.max_send_message_length", MAX_MESSAGE_SIZE), + ("grpc.so_reuseport", 1), + ], + ) + add_SelectorServicer_to_server(SelectorGRPCServicer(selector_manager, sample_batch_size), server) + server.add_insecure_port(bind_address) + server.start() + _wait_forever(server) + + class SelectorServer: def __init__(self, modyn_config: dict) -> None: self.modyn_config = modyn_config self.selector_manager = SelectorManager(modyn_config) - self.grpc_servicer = SelectorGRPCServicer( - self.selector_manager, self.modyn_config["selector"]["sample_batch_size"] - ) - self._add_servicer_to_server_func = add_SelectorServicer_to_server - - def prepare_server(self) -> grpc.server: - server = grpc.server( - futures.ThreadPoolExecutor(max_workers=64), - options=[ - ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), - ("grpc.max_send_message_length", MAX_MESSAGE_SIZE), - ], - ) - self._add_servicer_to_server_func(self.grpc_servicer, server) - return server + self.sample_batch_size = self.modyn_config["selector"]["sample_batch_size"] + self.workers = [] def run(self) -> None: - server = self.prepare_server() - logger.info(f"Starting server. Listening on port {self.modyn_config['selector']['port']}.") - server.add_insecure_port("[::]:" + self.modyn_config["selector"]["port"]) - server.start() - server.wait_for_termination() + port = self.modyn_config["selector"]["port"] + logger.info(f"Starting server. Listening on port {port}") + with _reserve_port(port) as port: + bind_address = "[::]:" + port + for _ in range(64): + worker = mp.Process( + target=_run_server, + args=(bind_address, self.selector_manager, self.sample_batch_size), + ) + worker.start() + self.workers.append(worker) + + for worker in self.workers: + worker.join() + if ( "cleanup_trigger_samples_after_shutdown" in self.modyn_config["selector"] and self.modyn_config["selector"]["cleanup_trigger_samples_after_shutdown"] diff --git a/modyn/selector/internal/selector_manager.py b/modyn/selector/internal/selector_manager.py index 51fa6bf89..ce271fec7 100644 --- a/modyn/selector/internal/selector_manager.py +++ b/modyn/selector/internal/selector_manager.py @@ -4,10 +4,12 @@ import logging import os import shutil +from multiprocessing import Lock, Manager from pathlib import Path -from threading import Lock +from typing import Optional from modyn.metadata_database.metadata_database_connection import MetadataDatabaseConnection +from modyn.metadata_database.models.pipelines import Pipeline from modyn.selector.internal.selector_strategies.abstract_selection_strategy import AbstractSelectionStrategy from modyn.selector.selector import Selector from modyn.utils.utils import dynamic_module_import, is_directory_writable @@ -18,9 +20,10 @@ class SelectorManager: def __init__(self, modyn_config: dict) -> None: self._modyn_config = modyn_config + self._manager = Manager() self._selectors: dict[int, Selector] = {} - self._selector_locks: dict[int, Lock] = {} - self._next_pipeline_lock = Lock() + self._selector_locks: dict[int, Lock] = self._manager.dict() + self._next_pipeline_lock = self._manager.Lock() self._selector_cache_size = self._modyn_config["selector"]["keys_in_selector_cache"] self.init_metadata_db() @@ -57,6 +60,27 @@ def _init_trigger_sample_directory(self) -> None: + f"Directory info: {os.stat(trigger_sample_directory)}" ) + def _populate_pipeline_if_exists(self, pipeline_id: int) -> None: + if pipeline_id in self._selectors: + return + + with MetadataDatabaseConnection(self._modyn_config) as database: + pipeline: Optional[Pipeline] = database.session.get(Pipeline, pipeline_id) + if pipeline is None: + return + logging.info( + f"[{os.getpid()}] Instantiating new selector for pipeline {pipeline_id}" + + " that was in the DB but previously unknown to this process.." + ) + + self._instantiate_selector(pipeline_id, pipeline.num_workers, pipeline.selection_strategy) + + def _instantiate_selector(self, pipeline_id: int, num_workers: int, selection_strategy: str) -> None: + assert pipeline_id in self._selector_locks, f"Trying to register pipeline {pipeline_id} without existing lock!" + selection_strategy = self._instantiate_strategy(json.loads(selection_strategy), pipeline_id) + selector = Selector(selection_strategy, pipeline_id, num_workers, self._modyn_config, self._selector_cache_size) + self._selectors[pipeline_id] = selector + def register_pipeline(self, num_workers: int, selection_strategy: str) -> int: """ Registers a new pipeline at the Selector. @@ -70,12 +94,11 @@ def register_pipeline(self, num_workers: int, selection_strategy: str) -> int: with self._next_pipeline_lock: with MetadataDatabaseConnection(self._modyn_config) as database: - pipeline_id = database.register_pipeline(num_workers) + pipeline_id = database.register_pipeline(num_workers, selection_strategy) + + self._selector_locks[pipeline_id] = self._manager.Lock() + self._instantiate_selector(pipeline_id, num_workers, selection_strategy) - selection_strategy = self._instantiate_strategy(json.loads(selection_strategy), pipeline_id) - selector = Selector(selection_strategy, pipeline_id, num_workers, self._selector_cache_size) - self._selectors[pipeline_id] = selector - self._selector_locks[pipeline_id] = Lock() return pipeline_id def get_sample_keys_and_weights( @@ -92,6 +115,8 @@ def get_sample_keys_and_weights( List of tuples for the samples to be returned to that particular worker. The first index of the tuple will be the key, and the second index will be that sample's weight. """ + self._populate_pipeline_if_exists(pipeline_id) + if pipeline_id not in self._selectors: raise ValueError(f"Requested keys from pipeline {pipeline_id} which does not exist!") @@ -104,6 +129,8 @@ def get_sample_keys_and_weights( def inform_data( self, pipeline_id: int, keys: list[int], timestamps: list[int], labels: list[int] ) -> dict[str, object]: + self._populate_pipeline_if_exists(pipeline_id) + if pipeline_id not in self._selectors: raise ValueError(f"Informing pipeline {pipeline_id} of data. Pipeline does not exist!") @@ -113,6 +140,8 @@ def inform_data( def inform_data_and_trigger( self, pipeline_id: int, keys: list[int], timestamps: list[int], labels: list[int] ) -> tuple[int, dict[str, object]]: + self._populate_pipeline_if_exists(pipeline_id) + if pipeline_id not in self._selectors: raise ValueError(f"Informing pipeline {pipeline_id} of data and triggering. Pipeline does not exist!") @@ -120,30 +149,40 @@ def inform_data_and_trigger( return self._selectors[pipeline_id].inform_data_and_trigger(keys, timestamps, labels) def get_number_of_samples(self, pipeline_id: int, trigger_id: int) -> int: + self._populate_pipeline_if_exists(pipeline_id) + if pipeline_id not in self._selectors: raise ValueError(f"Requested number of samples from pipeline {pipeline_id} which does not exist!") return self._selectors[pipeline_id].get_number_of_samples(trigger_id) def get_status_bar_scale(self, pipeline_id: int) -> int: + self._populate_pipeline_if_exists(pipeline_id) + if pipeline_id not in self._selectors: raise ValueError(f"Requested status bar scale from pipeline {pipeline_id} which does not exist!") return self._selectors[pipeline_id].get_status_bar_scale() def get_number_of_partitions(self, pipeline_id: int, trigger_id: int) -> int: + self._populate_pipeline_if_exists(pipeline_id) + if pipeline_id not in self._selectors: raise ValueError(f"Requested number of partitions from pipeline {pipeline_id} which does not exist!") return self._selectors[pipeline_id].get_number_of_partitions(trigger_id) def get_available_labels(self, pipeline_id: int) -> list[int]: + self._populate_pipeline_if_exists(pipeline_id) + if pipeline_id not in self._selectors: raise ValueError(f"Requested available labels from pipeline {pipeline_id} which does not exist!") return self._selectors[pipeline_id].get_available_labels() def uses_weights(self, pipeline_id: int) -> bool: + self._populate_pipeline_if_exists(pipeline_id) + if pipeline_id not in self._selectors: raise ValueError(f"Requested whether the pipeline {pipeline_id} uses weights but it does not exist!") @@ -169,6 +208,8 @@ def _instantiate_strategy(self, selection_strategy: dict, pipeline_id: int) -> A return strategy_handler(config, self._modyn_config, pipeline_id, maximum_keys_in_memory) def get_selection_strategy_remote(self, pipeline_id: int) -> tuple[bool, str, dict]: + self._populate_pipeline_if_exists(pipeline_id) + if pipeline_id not in self._selectors: raise ValueError(f"Requested selection strategy for pipeline {pipeline_id} which does not exist!") diff --git a/modyn/selector/selector.py b/modyn/selector/selector.py index f2ee1ea9a..b894b0626 100644 --- a/modyn/selector/selector.py +++ b/modyn/selector/selector.py @@ -1,7 +1,9 @@ from __future__ import annotations -from typing import Any, Dict +from typing import Any, Dict, Optional +from modyn.metadata_database.metadata_database_connection import MetadataDatabaseConnection +from modyn.metadata_database.models.triggers import Trigger from modyn.selector.internal.selector_strategies import CoresetStrategy from modyn.selector.internal.selector_strategies.abstract_selection_strategy import AbstractSelectionStrategy from modyn.utils.utils import flatten, get_partition_for_worker @@ -13,11 +15,17 @@ class Selector: """ def __init__( - self, strategy: AbstractSelectionStrategy, pipeline_id: int, num_workers: int, cache_size: int = 100000 + self, + strategy: AbstractSelectionStrategy, + pipeline_id: int, + num_workers: int, + modyn_config: dict, + cache_size: int = 100000, ) -> None: self._strategy = strategy self._pipeline_id = pipeline_id self._num_workers = num_workers + self._modyn_config = modyn_config self._trigger_cache: Dict[int, list[list[tuple[int, float]]]] = {} self._maximum_keys_in_cache = cache_size @@ -26,6 +34,19 @@ def __init__( self._trigger_size_cache: Dict[int, int] = {} self._trigger_partition_cache: Dict[int, int] = {} + def _populate_trigger_if_exists(self, trigger_id: int) -> None: + if trigger_id in self._trigger_size_cache: + assert trigger_id in self._trigger_partition_cache, "Inconsistent state" + return + + with MetadataDatabaseConnection(self._modyn_config) as database: + trigger: Optional[Trigger] = database.session.get(Trigger, trigger_id, self._pipeline_id) + if trigger is None: + return + + self._trigger_size_cache[trigger_id] = trigger.num_keys + self._trigger_partition_cache[trigger_id] = trigger.num_partitions + def get_sample_keys_and_weights( self, trigger_id: int, worker_id: int, partition_id: int ) -> list[tuple[int, float]]: @@ -40,6 +61,8 @@ def get_sample_keys_and_weights( List of tuples for the samples to be returned to that particular worker. The first index of the tuple will be the key, and the second index will be that sample's weight. """ + self._populate_trigger_if_exists(trigger_id) + if trigger_id not in self._trigger_partition_cache or partition_id >= self._trigger_partition_cache[trigger_id]: raise ValueError(f"Invalid request: Trigger {trigger_id}, partition {partition_id}") if worker_id < 0 or worker_id >= self._num_workers: @@ -95,6 +118,8 @@ def inform_data_and_trigger( return trigger_id, log def get_number_of_samples(self, trigger_id: int) -> int: + self._populate_trigger_if_exists(trigger_id) + if trigger_id not in self._trigger_size_cache: raise ValueError(f"Trigger ID {trigger_id} does not exist!") @@ -108,6 +133,8 @@ def get_status_bar_scale(self) -> int: return self._strategy.training_status_bar_scale def get_number_of_partitions(self, trigger_id: int) -> int: + self._populate_trigger_if_exists(trigger_id) + if trigger_id not in self._trigger_partition_cache: raise ValueError(f"Trigger ID {trigger_id} does not exist!") diff --git a/modyn/storage/internal/grpc/grpc_server.py b/modyn/storage/internal/grpc/grpc_server.py index 0a76d6652..7f14520a3 100644 --- a/modyn/storage/internal/grpc/grpc_server.py +++ b/modyn/storage/internal/grpc/grpc_server.py @@ -1,7 +1,14 @@ """GRPC server context manager.""" +import contextlib +import datetime import logging +import multiprocessing as mp +import os +import socket +import time from concurrent import futures +from typing import Any import grpc from modyn.storage.internal.grpc.generated.storage_pb2_grpc import add_StorageServicer_to_server @@ -11,6 +18,49 @@ logger = logging.getLogger(__name__) +@contextlib.contextmanager +def _reserve_port(port: str): + """Find and reserve a port for all subprocesses to use.""" + sock = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) + sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) + if sock.getsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT) == 0: + raise RuntimeError("Failed to set SO_REUSEPORT.") + sock.bind(("", int(port))) + try: + assert sock.getsockname()[1] == int(port) + yield port + finally: + sock.close() + + +def _wait_forever(server): + try: + while True: + time.sleep(datetime.timedelta(days=1).total_seconds()) + except KeyboardInterrupt: + server.stop(None) + + +def _run_server(bind_address, modyn_config): + """Start a server in a subprocess.""" + logging.info(f"[{os.getpid()}] Starting new server.") + + server = grpc.server( + futures.ThreadPoolExecutor( + max_workers=16, + ), + options=[ + ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), + ("grpc.max_send_message_length", MAX_MESSAGE_SIZE), + ("grpc.so_reuseport", 1), + ], + ) + add_StorageServicer_to_server(StorageGRPCServicer(modyn_config), server) + server.add_insecure_port(bind_address) + server.start() + _wait_forever(server) + + class GRPCServer: """GRPC server context manager.""" @@ -21,28 +71,34 @@ def __init__(self, modyn_config: dict) -> None: modyn_config (dict): Configuration of the storage module. """ self.modyn_config = modyn_config - self.server = grpc.server( - futures.ThreadPoolExecutor( - max_workers=64, - ), - options=[ - ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), - ("grpc.max_send_message_length", MAX_MESSAGE_SIZE), - ], - ) - - def __enter__(self) -> grpc.Server: + self.workers = [] + + def __enter__(self) -> Any: """Enter the context manager. Returns: grpc.Server: GRPC server """ - add_StorageServicer_to_server(StorageGRPCServicer(self.modyn_config), self.server) port = self.modyn_config["storage"]["port"] logger.info(f"Starting server. Listening on port {port}") - self.server.add_insecure_port("[::]:" + port) - self.server.start() - return self.server + with _reserve_port(port) as port: + bind_address = "[::]:" + port + for _ in range(64): + worker = mp.Process( + target=_run_server, + args=( + bind_address, + self.modyn_config, + ), + ) + worker.start() + self.workers.append(worker) + + return self + + def wait_for_termination(self) -> None: + for worker in self.workers: + worker.join() def __exit__(self, exc_type: type, exc_val: Exception, exc_tb: Exception) -> None: """Exit the context manager. @@ -52,4 +108,5 @@ def __exit__(self, exc_type: type, exc_val: Exception, exc_tb: Exception) -> Non exc_val (Exception): exception value exc_tb (Exception): exception traceback """ - self.server.stop(0) + self.wait_for_termination() + del self.workers diff --git a/modyn/storage/internal/grpc/storage_grpc_servicer.py b/modyn/storage/internal/grpc/storage_grpc_servicer.py index 219eb5c65..f3c8c8936 100644 --- a/modyn/storage/internal/grpc/storage_grpc_servicer.py +++ b/modyn/storage/internal/grpc/storage_grpc_servicer.py @@ -1,9 +1,12 @@ """Storage GRPC servicer.""" import logging +import os +import threading from typing import Iterable, Tuple import grpc +from modyn.common.benchmark.stopwatch import Stopwatch from modyn.storage.internal.database.models import Dataset, File, Sample from modyn.storage.internal.database.storage_database_connection import StorageDatabaseConnection from modyn.storage.internal.database.storage_database_utils import get_file_wrapper, get_filesystem_wrapper @@ -64,6 +67,9 @@ def Get(self, request: GetRequest, context: grpc.ServicerContext) -> Iterable[Ge Yields: Iterator[Iterable[GetResponse]]: Response containing the data for the given keys. """ + tid = threading.get_native_id() + pid = os.getpid() + logger.info(f"[{pid}][{tid}] Received request for {len(request.keys)} items.") with StorageDatabaseConnection(self.modyn_config) as database: session = database.session @@ -73,12 +79,16 @@ def Get(self, request: GetRequest, context: grpc.ServicerContext) -> Iterable[Ge yield GetResponse() return + stopw = Stopwatch() + stopw.start("GetSamples") samples: list[Sample] = ( session.query(Sample) .filter(and_(Sample.sample_id.in_(request.keys), Sample.dataset_id == dataset.dataset_id)) .order_by(Sample.file_id) .all() ) + samples_time = stopw.stop() + logger.info(f"[{pid}][{tid}] Getting samples took {samples_time / 1000}s.") if len(samples) == 0: logger.error("No samples found in the database.") diff --git a/modyn/tests/model_storage/internal/grpc/test_model_storage.database b/modyn/tests/model_storage/internal/grpc/test_model_storage.database new file mode 100644 index 0000000000000000000000000000000000000000..0902c438a65203afa6e5922a1f69631321f8cd61 GIT binary patch literal 57344 zcmeI&(NEh(9Ki9orAaV^c2AxrAx_#0kcfN>4XMkbLDGeED^HebJZ9BNm~FQ9 zF;HHn{TKTu_NVMG*uSxz1CD)=gTC~|`bMJl`Rw!E=XdwBBj@0UJwNuu&*O0D#^USJ zqmp5io(fSal}!1!BLC7)QeH?4@_!?@t=lw9UyS~)J$zECEgY2|Zq@!+`FFWfJ6!t5 z{C4rLs#pHADi;2eReTUYppXKS&uSHGYulLE{%GL6jpLychyL-g7dj{Yi5K`I&+!Lu z(|vL1`XhgI>V-RB9T-3H9hc8Fhw|DKH=ydy{^Kx%*Lzs)XPS34R z8`PX0`$Nx($5LC0if7%IvWH2t{8 z-`szZ?7qCfi5veSer)zy-!*&dUp;G{mm(% zR9{_AuIjmo_gdyW4&8z8jbdFgy`}RXZPLW$iuKK^F1HeS<}Ej>C^ICUdu;Ch zs^$pgc6wG_s93A3#`)`f22b;L^gxOCRSaZv^OmR*kGsM-Cj}?FvJ{ktFdhvC zlALdg%ZDY-FCUbx*6JOJGfS6I`Rwps#oF97-nTE;k{5Wd<8kQ793^vZ`ZN^H-+f<^ z)f|*S-z9m+%#S>8}EUKmY**5I_I{1Q0*~fg%d<{lAD&WlRVlfB*srAbiC-s4^x55I_I{1Q0*~0R#|0009J40p9 Date: Mon, 2 Oct 2023 15:59:14 +0200 Subject: [PATCH 16/46] refactor to generic grpc server --- modyn/common/grpc/__init__.py | 10 ++ modyn/common/grpc/grpc_helpers.py | 123 ++++++++++++++++++ .../internal/grpc/selector_grpc_servicer.py | 7 +- .../selector/internal/grpc/selector_server.py | 91 +++++-------- modyn/selector/internal/selector_manager.py | 12 +- modyn/selector/selector.py | 2 +- modyn/selector/selector_entrypoint.py | 18 ++- modyn/storage/internal/grpc/grpc_server.py | 91 +------------ modyn/storage/storage.py | 4 +- .../internal/grpc/test_selector_server.py | 10 +- .../selector/test_selector_entrypoint.py | 10 +- .../storage/internal/grpc/test_grpc_server.py | 6 +- modyn/tests/storage/test_storage.py | 4 +- .../internal/dataset/online_dataset.py | 27 +++- 14 files changed, 245 insertions(+), 170 deletions(-) create mode 100644 modyn/common/grpc/__init__.py create mode 100644 modyn/common/grpc/grpc_helpers.py diff --git a/modyn/common/grpc/__init__.py b/modyn/common/grpc/__init__.py new file mode 100644 index 000000000..6040a0a16 --- /dev/null +++ b/modyn/common/grpc/__init__.py @@ -0,0 +1,10 @@ +""" +This submodule implements functions to run gRPC servers using multiprocessing. +""" +import os + +from .grpc_helpers import GenericGRPCServer # noqa: F401 + +files = os.listdir(os.path.dirname(__file__)) +files.remove("__init__.py") +__all__ = [f[:-3] for f in files if f.endswith(".py")] diff --git a/modyn/common/grpc/grpc_helpers.py b/modyn/common/grpc/grpc_helpers.py new file mode 100644 index 000000000..5bcdca03a --- /dev/null +++ b/modyn/common/grpc/grpc_helpers.py @@ -0,0 +1,123 @@ +import contextlib +import datetime +import logging +import multiprocessing as mp +import os +import pickle +import socket +import time +from concurrent import futures +from typing import Any, Callable + +import grpc +from modyn.utils import MAX_MESSAGE_SIZE + +logger = logging.getLogger(__name__) + +PROCESS_THREAD_WORKERS = 16 +NUM_GPRC_PROCESSES = 64 + + +@contextlib.contextmanager +def reserve_port(port: str): + """Find and reserve a port for all subprocesses to use.""" + sock = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) + sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) + if sock.getsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT) == 0: + raise RuntimeError("Failed to set SO_REUSEPORT.") + sock.bind(("", int(port))) + try: + assert sock.getsockname()[1] == int(port) + yield port + finally: + sock.close() + + +def _wait_forever(server): + try: + while True: + time.sleep(datetime.timedelta(days=1).total_seconds()) + except KeyboardInterrupt: + server.stop(None) + + +def _run_server_worker(bind_address: str, add_servicer_callback: Callable, modyn_config: dict, callback_kwargs: dict): + """Start a server in a subprocess.""" + logging.info(f"[{os.getpid()}] Starting new gRPC server process.") + + server = grpc.server( + futures.ThreadPoolExecutor( + max_workers=PROCESS_THREAD_WORKERS, + ), + options=[ + ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), + ("grpc.max_send_message_length", MAX_MESSAGE_SIZE), + ("grpc.so_reuseport", 1), + ], + ) + + add_servicer_callback(modyn_config, server, **callback_kwargs) + server.add_insecure_port(bind_address) + server.start() + _wait_forever(server) + + +class GenericGRPCServer: + def __init__( + self, modyn_config: dict, port: str, add_servicer_callback: Callable, callback_kwargs: dict = {} + ) -> None: + """Initialize the GRPC server. + + Args: + TODO + """ + self.port = port + self.modyn_config = modyn_config + self.add_servicer_callback = add_servicer_callback + self.callback_kwargs = callback_kwargs + self.workers = [] + + def __enter__(self) -> Any: + """Enter the context manager. + + Returns: + grpc.Server: GRPC server + """ + logger.info(f"[{os.getpid()}] Starting server. Listening on port {self.port}") + with reserve_port(self.port) as port: + bind_address = "[::]:" + port + for _ in range(NUM_GPRC_PROCESSES): + worker = mp.Process( + target=_run_server_worker, + args=(bind_address, self.add_servicer_callback, self.modyn_config, self.callback_kwargs), + ) + worker.start() + self.workers.append(worker) + + return self + + def __getstate__(self): + for variable_name, value in vars(self).items(): + try: + pickle.dumps(value) + except: + print(f"{variable_name} with value {value} is not pickable") + + state = self.__dict__.copy() + del state["add_servicer_callback"] + return state + + def wait_for_termination(self) -> None: + for worker in self.workers: + worker.join() + + def __exit__(self, exc_type: type, exc_val: Exception, exc_tb: Exception) -> None: + """Exit the context manager. + + Args: + exc_type (type): exception type + exc_val (Exception): exception value + exc_tb (Exception): exception traceback + """ + self.wait_for_termination() + del self.workers diff --git a/modyn/selector/internal/grpc/selector_grpc_servicer.py b/modyn/selector/internal/grpc/selector_grpc_servicer.py index a1bea06ec..0db6cf8f6 100644 --- a/modyn/selector/internal/grpc/selector_grpc_servicer.py +++ b/modyn/selector/internal/grpc/selector_grpc_servicer.py @@ -1,5 +1,7 @@ import json import logging +import os +import threading from typing import Iterable import grpc @@ -59,8 +61,11 @@ def get_sample_keys_and_weights( # pylint: disable-next=unused-argument request.worker_id, request.partition_id, ) + tid = threading.get_native_id() + pid = os.getpid() + logger.info( - f"[Pipeline {pipeline_id}]: Fetching samples for trigger id {trigger_id}" + f"[{pid}][{tid}][Pipeline {pipeline_id}]: Fetching samples for trigger id {trigger_id}" + f" and worker id {worker_id} and partition id {partition_id}" ) diff --git a/modyn/selector/internal/grpc/selector_server.py b/modyn/selector/internal/grpc/selector_server.py index ead69a692..67c8edac0 100644 --- a/modyn/selector/internal/grpc/selector_server.py +++ b/modyn/selector/internal/grpc/selector_server.py @@ -1,13 +1,16 @@ import contextlib import datetime +import functools import logging import multiprocessing as mp import os +import pickle import socket import time from concurrent import futures import grpc +from modyn.common.grpc import GenericGRPCServer from modyn.selector.internal.grpc.generated.selector_pb2_grpc import add_SelectorServicer_to_server # noqa: E402, E501 from modyn.selector.internal.grpc.selector_grpc_servicer import SelectorGRPCServicer from modyn.selector.internal.selector_manager import SelectorManager @@ -16,72 +19,42 @@ logger = logging.getLogger(__name__) -@contextlib.contextmanager -def _reserve_port(port: str): - """Find and reserve a port for all subprocesses to use.""" - sock = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) - sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) - if sock.getsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT) == 0: - raise RuntimeError("Failed to set SO_REUSEPORT.") - sock.bind(("", int(port))) - try: - assert sock.getsockname()[1] == int(port) - yield port - finally: - sock.close() +class SelectorGRPCServer(GenericGRPCServer): + @staticmethod + def callback(modyn_config, server, selector_manager): + add_SelectorServicer_to_server( + SelectorGRPCServicer(selector_manager, modyn_config["selector"]["sample_batch_size"]), server + ) - -def _wait_forever(server): - try: - while True: - time.sleep(datetime.timedelta(days=1).total_seconds()) - except KeyboardInterrupt: - server.stop(None) - - -def _run_server(bind_address, selector_manager, sample_batch_size): - """Start a server in a subprocess.""" - logging.info(f"[{os.getpid()}] Starting new server.") - - server = grpc.server( - futures.ThreadPoolExecutor( - max_workers=16, - ), - options=[ - ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), - ("grpc.max_send_message_length", MAX_MESSAGE_SIZE), - ("grpc.so_reuseport", 1), - ], - ) - add_SelectorServicer_to_server(SelectorGRPCServicer(selector_manager, sample_batch_size), server) - server.add_insecure_port(bind_address) - server.start() - _wait_forever(server) - - -class SelectorServer: def __init__(self, modyn_config: dict) -> None: self.modyn_config = modyn_config self.selector_manager = SelectorManager(modyn_config) - self.sample_batch_size = self.modyn_config["selector"]["sample_batch_size"] - self.workers = [] - def run(self) -> None: - port = self.modyn_config["selector"]["port"] - logger.info(f"Starting server. Listening on port {port}") - with _reserve_port(port) as port: - bind_address = "[::]:" + port - for _ in range(64): - worker = mp.Process( - target=_run_server, - args=(bind_address, self.selector_manager, self.sample_batch_size), - ) - worker.start() - self.workers.append(worker) + callback_kwargs = {"selector_manager": self.selector_manager} + super().__init__(modyn_config, modyn_config["selector"]["port"], SelectorGRPCServer.callback, callback_kwargs) + + def __getstate__(self): + for variable_name, value in vars(self).items(): + try: + pickle.dumps(value) + except: + print(f"{variable_name} with value {value} is not pickable") + + state = self.__dict__.copy() + if "add_servicer_callback" in state: + del state["add_servicer_callback"] + + return state - for worker in self.workers: - worker.join() + def __exit__(self, exc_type: type, exc_val: Exception, exc_tb: Exception) -> None: + """Exit the context manager. + Args: + exc_type (type): exception type + exc_val (Exception): exception value + exc_tb (Exception): exception traceback + """ + super().__exit__(exc_type, exc_val, exc_tb) if ( "cleanup_trigger_samples_after_shutdown" in self.modyn_config["selector"] and self.modyn_config["selector"]["cleanup_trigger_samples_after_shutdown"] diff --git a/modyn/selector/internal/selector_manager.py b/modyn/selector/internal/selector_manager.py index ce271fec7..b066a5d07 100644 --- a/modyn/selector/internal/selector_manager.py +++ b/modyn/selector/internal/selector_manager.py @@ -26,9 +26,17 @@ def __init__(self, modyn_config: dict) -> None: self._next_pipeline_lock = self._manager.Lock() self._selector_cache_size = self._modyn_config["selector"]["keys_in_selector_cache"] + # TODO(create issue): currently we have to prepare N locks and then share. This is because we cannot share the manager with subprocesses. For now not a big problem since we mostly run one pipeline but we might want to redesign this. + self._prepared_locks = [self._manager.Lock() for _ in range(64)] + self.init_metadata_db() self._init_trigger_sample_directory() + def __getstate__(self): + state = self.__dict__.copy() + del state["_manager"] + return state + def init_metadata_db(self) -> None: with MetadataDatabaseConnection(self._modyn_config) as database: database.create_tables() @@ -70,7 +78,7 @@ def _populate_pipeline_if_exists(self, pipeline_id: int) -> None: return logging.info( f"[{os.getpid()}] Instantiating new selector for pipeline {pipeline_id}" - + " that was in the DB but previously unknown to this process.." + + " that was in the DB but previously unknown to this process" ) self._instantiate_selector(pipeline_id, pipeline.num_workers, pipeline.selection_strategy) @@ -96,7 +104,7 @@ def register_pipeline(self, num_workers: int, selection_strategy: str) -> int: with MetadataDatabaseConnection(self._modyn_config) as database: pipeline_id = database.register_pipeline(num_workers, selection_strategy) - self._selector_locks[pipeline_id] = self._manager.Lock() + self._selector_locks[pipeline_id] = self._prepared_locks[pipeline_id % len(self._prepared_locks)] self._instantiate_selector(pipeline_id, num_workers, selection_strategy) return pipeline_id diff --git a/modyn/selector/selector.py b/modyn/selector/selector.py index b894b0626..3050bf3ae 100644 --- a/modyn/selector/selector.py +++ b/modyn/selector/selector.py @@ -40,7 +40,7 @@ def _populate_trigger_if_exists(self, trigger_id: int) -> None: return with MetadataDatabaseConnection(self._modyn_config) as database: - trigger: Optional[Trigger] = database.session.get(Trigger, trigger_id, self._pipeline_id) + trigger: Optional[Trigger] = database.session.get(Trigger, (trigger_id, self._pipeline_id)) if trigger is None: return diff --git a/modyn/selector/selector_entrypoint.py b/modyn/selector/selector_entrypoint.py index 152b4c125..0795819c1 100644 --- a/modyn/selector/selector_entrypoint.py +++ b/modyn/selector/selector_entrypoint.py @@ -1,9 +1,11 @@ import argparse import logging +import multiprocessing as mp +import os import pathlib import yaml -from modyn.selector.internal.grpc.selector_server import SelectorServer +from modyn.selector.internal.grpc.selector_server import SelectorGRPCServer logging.basicConfig( level=logging.NOTSET, @@ -12,6 +14,14 @@ ) logger = logging.getLogger(__name__) +# We need to do this at the top because other dependencies otherwise set fork. +try: + mp.set_start_method("spawn") +except RuntimeError as error: + if mp.get_start_method() != "spawn" and "PYTEST_CURRENT_TEST" not in os.environ: + logger.error("Start method is already set to {}", mp.get_start_method()) + raise error + def setup_argparser() -> argparse.ArgumentParser: parser_ = argparse.ArgumentParser(description="Modyn Selector") @@ -35,9 +45,9 @@ def main() -> None: modyn_config = yaml.safe_load(config_file) logger.info("Initializing selector server.") - selector = SelectorServer(modyn_config) - logger.info("Starting selector server.") - selector.run() + + with SelectorGRPCServer(modyn_config): + pass logger.info("Selector server returned, exiting.") diff --git a/modyn/storage/internal/grpc/grpc_server.py b/modyn/storage/internal/grpc/grpc_server.py index 7f14520a3..a2baac37d 100644 --- a/modyn/storage/internal/grpc/grpc_server.py +++ b/modyn/storage/internal/grpc/grpc_server.py @@ -11,6 +11,7 @@ from typing import Any import grpc +from modyn.common.grpc import GenericGRPCServer from modyn.storage.internal.grpc.generated.storage_pb2_grpc import add_StorageServicer_to_server from modyn.storage.internal.grpc.storage_grpc_servicer import StorageGRPCServicer from modyn.utils import MAX_MESSAGE_SIZE @@ -18,95 +19,17 @@ logger = logging.getLogger(__name__) -@contextlib.contextmanager -def _reserve_port(port: str): - """Find and reserve a port for all subprocesses to use.""" - sock = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) - sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) - if sock.getsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT) == 0: - raise RuntimeError("Failed to set SO_REUSEPORT.") - sock.bind(("", int(port))) - try: - assert sock.getsockname()[1] == int(port) - yield port - finally: - sock.close() - - -def _wait_forever(server): - try: - while True: - time.sleep(datetime.timedelta(days=1).total_seconds()) - except KeyboardInterrupt: - server.stop(None) - - -def _run_server(bind_address, modyn_config): - """Start a server in a subprocess.""" - logging.info(f"[{os.getpid()}] Starting new server.") - - server = grpc.server( - futures.ThreadPoolExecutor( - max_workers=16, - ), - options=[ - ("grpc.max_receive_message_length", MAX_MESSAGE_SIZE), - ("grpc.max_send_message_length", MAX_MESSAGE_SIZE), - ("grpc.so_reuseport", 1), - ], - ) - add_StorageServicer_to_server(StorageGRPCServicer(modyn_config), server) - server.add_insecure_port(bind_address) - server.start() - _wait_forever(server) - - -class GRPCServer: +class StorageGRPCServer(GenericGRPCServer): """GRPC server context manager.""" + @staticmethod + def callback(modyn_config, server): + add_StorageServicer_to_server(StorageGRPCServicer(modyn_config), server) + def __init__(self, modyn_config: dict) -> None: """Initialize the GRPC server. Args: modyn_config (dict): Configuration of the storage module. """ - self.modyn_config = modyn_config - self.workers = [] - - def __enter__(self) -> Any: - """Enter the context manager. - - Returns: - grpc.Server: GRPC server - """ - port = self.modyn_config["storage"]["port"] - logger.info(f"Starting server. Listening on port {port}") - with _reserve_port(port) as port: - bind_address = "[::]:" + port - for _ in range(64): - worker = mp.Process( - target=_run_server, - args=( - bind_address, - self.modyn_config, - ), - ) - worker.start() - self.workers.append(worker) - - return self - - def wait_for_termination(self) -> None: - for worker in self.workers: - worker.join() - - def __exit__(self, exc_type: type, exc_val: Exception, exc_tb: Exception) -> None: - """Exit the context manager. - - Args: - exc_type (type): exception type - exc_val (Exception): exception value - exc_tb (Exception): exception traceback - """ - self.wait_for_termination() - del self.workers + super().__init__(modyn_config, modyn_config["storage"]["port"], StorageGRPCServer.callback) diff --git a/modyn/storage/storage.py b/modyn/storage/storage.py index 17cba3b48..c2e8e3176 100644 --- a/modyn/storage/storage.py +++ b/modyn/storage/storage.py @@ -14,7 +14,7 @@ from modyn.storage.internal.database.storage_database_connection import StorageDatabaseConnection from modyn.storage.internal.file_watcher.new_file_watcher_watch_dog import run_watcher_watch_dog -from modyn.storage.internal.grpc.grpc_server import GRPCServer +from modyn.storage.internal.grpc.grpc_server import StorageGRPCServer from modyn.utils import validate_yaml logger = logging.getLogger(__name__) @@ -77,7 +77,7 @@ def run(self) -> None: watchdog.start() #  Start the storage grpc server. - with GRPCServer(self.modyn_config) as server: + with StorageGRPCServer(self.modyn_config) as server: server.wait_for_termination() should_stop.value = True # type: ignore # See https://github.com/python/typeshed/issues/8799 diff --git a/modyn/tests/selector/internal/grpc/test_selector_server.py b/modyn/tests/selector/internal/grpc/test_selector_server.py index 47c3e73e2..73e416ba1 100644 --- a/modyn/tests/selector/internal/grpc/test_selector_server.py +++ b/modyn/tests/selector/internal/grpc/test_selector_server.py @@ -3,7 +3,7 @@ from unittest import mock from unittest.mock import MagicMock, patch -from modyn.selector.internal.grpc.selector_server import SelectorServer +from modyn.selector.internal.grpc.selector_server import SelectorGRPCServer from modyn.selector.internal.selector_manager import SelectorManager @@ -27,7 +27,7 @@ def test_init(): with tempfile.TemporaryDirectory() as tmp_dir: config = get_modyn_config() config["selector"]["trigger_sample_directory"] = tmp_dir - grpc_server = SelectorServer(config) + grpc_server = SelectorGRPCServer(config) assert grpc_server.modyn_config == config @@ -36,7 +36,7 @@ def test_prepare_server(): with tempfile.TemporaryDirectory() as tmp_dir: config = get_modyn_config() config["selector"]["trigger_sample_directory"] = tmp_dir - grpc_server = SelectorServer(config) + grpc_server = SelectorGRPCServer(config) mock_add = mock.Mock() grpc_server._add_servicer_to_server_func = mock_add @@ -46,12 +46,12 @@ def test_prepare_server(): @patch.object(SelectorManager, "init_metadata_db", noop_init_metadata_db) -@patch.object(SelectorServer, "prepare_server") +@patch.object(SelectorGRPCServer, "prepare_server") def test_run(test_prepare_server: MagicMock): with tempfile.TemporaryDirectory() as tmp_dir: config = get_modyn_config() config["selector"]["trigger_sample_directory"] = tmp_dir - grpc_server = SelectorServer(config) + grpc_server = SelectorGRPCServer(config) mock_start = mock.Mock() mock_wait = mock.Mock() diff --git a/modyn/tests/selector/test_selector_entrypoint.py b/modyn/tests/selector/test_selector_entrypoint.py index 1b7083efe..33f6d46b7 100644 --- a/modyn/tests/selector/test_selector_entrypoint.py +++ b/modyn/tests/selector/test_selector_entrypoint.py @@ -6,7 +6,7 @@ import pathlib from unittest.mock import patch -from modyn.selector.internal.grpc.selector_server import SelectorServer +from modyn.selector.internal.grpc.selector_server import SelectorGRPCServer SCRIPT_PATH = pathlib.Path(os.path.realpath(__file__)) @@ -23,15 +23,15 @@ def noop_run(self) -> None: pass -@patch.object(SelectorServer, "__init__", noop_constructor_mock) -@patch.object(SelectorServer, "run", noop_run) +@patch.object(SelectorGRPCServer, "__init__", noop_constructor_mock) +@patch.object(SelectorGRPCServer, "run", noop_run) def test_trainer_server_script_runs(script_runner): ret = script_runner.run("_modyn_selector", str(EXAMPLE_SYSTEM_CONFIG)) assert ret.success -@patch.object(SelectorServer, "__init__", noop_constructor_mock) -@patch.object(SelectorServer, "run", noop_run) +@patch.object(SelectorGRPCServer, "__init__", noop_constructor_mock) +@patch.object(SelectorGRPCServer, "run", noop_run) def test_trainer_server_fails_on_non_existing_system_config(script_runner): ret = script_runner.run("_modyn_selector", str(NO_FILE)) assert not ret.success diff --git a/modyn/tests/storage/internal/grpc/test_grpc_server.py b/modyn/tests/storage/internal/grpc/test_grpc_server.py index 5f7795d11..b5f3817f1 100644 --- a/modyn/tests/storage/internal/grpc/test_grpc_server.py +++ b/modyn/tests/storage/internal/grpc/test_grpc_server.py @@ -1,7 +1,7 @@ # pylint: disable=unused-argument from unittest.mock import patch -from modyn.storage.internal.grpc.grpc_server import GRPCServer +from modyn.storage.internal.grpc.grpc_server import StorageGRPCServer def get_modyn_config(): @@ -9,11 +9,11 @@ def get_modyn_config(): def test_init(): - grpc_server = GRPCServer(get_modyn_config()) + grpc_server = StorageGRPCServer(get_modyn_config()) assert grpc_server.modyn_config == get_modyn_config() @patch("modyn.storage.internal.grpc.grpc_server.add_StorageServicer_to_server", return_value=None) def test_enter(mock_add_storage_servicer_to_server): - with GRPCServer(get_modyn_config()) as grpc_server: + with StorageGRPCServer(get_modyn_config()) as grpc_server: assert grpc_server is not None diff --git a/modyn/tests/storage/test_storage.py b/modyn/tests/storage/test_storage.py index 5ba24caa8..e0b1c6806 100644 --- a/modyn/tests/storage/test_storage.py +++ b/modyn/tests/storage/test_storage.py @@ -4,7 +4,7 @@ import pytest from modyn.storage.internal.database.storage_database_connection import StorageDatabaseConnection -from modyn.storage.internal.grpc.grpc_server import GRPCServer +from modyn.storage.internal.grpc.grpc_server import StorageGRPCServer from modyn.storage.storage import Storage database_path = pathlib.Path(os.path.abspath(__file__)).parent / "test_storage.db" @@ -76,7 +76,7 @@ def wait_for_termination(self, *args, **kwargs): # pylint: disable=unused-argum return -class MockGRPCServer(GRPCServer): +class MockGRPCServer(StorageGRPCServer): def __enter__(self): return MockGRPCInstance() diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index 7b07c11eb..b48a632a8 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -1,4 +1,5 @@ import contextlib +import functools import json import logging import os @@ -154,6 +155,7 @@ def _get_data( partition_valid_until: Optional[dict], partition_locks: Optional[dict], partition_signals: Optional[dict], + callback: Optional[Callable], ) -> None: get_data_log = {} self._sw.start(f"GetKeysAndWeightsPart{partition_id}", overwrite=True) @@ -195,6 +197,9 @@ def _get_data( with partition_locks[partition_id]: partition_valid[partition_id] = True + if callback is not None: + callback() + def _get_transformed_data_tuple( self, key: int, sample: bytes, label: int, weight: Optional[float] ) -> Optional[Tuple]: @@ -248,6 +253,12 @@ def _prefetch_partition(self, worker_id: int) -> None: self._partition_locks[self._next_partition_to_fetch] ) + #def potential_callback(): + # self._info("Prefetch callback called.") + # self._prefetch_partition(worker_id, num_additional_prefetches - 1) + + #callback = None if num_additional_prefetches == 0 else potential_callback + self._data_threads[self._next_partition_to_fetch] = threading.Thread( target=self._get_data, args=( @@ -258,6 +269,7 @@ def _prefetch_partition(self, worker_id: int) -> None: self._partition_valid_until, self._partition_locks, self._partition_signals, + None, ), ) @@ -271,13 +283,16 @@ def _fetch_partition_noprefetch( ) -> Iterator[tuple[int, bytes, int, Optional[float]]]: assert self._prefetched_partitions < 1 container: dict[str, Any] = {"data": [], "keys": [], "labels": [], "weights": []} - self._get_data(container, worker_id, partition_id, None, None, None, None) + self._get_data(container, worker_id, partition_id, None, None, None, None, None) assert "data" in container and "labels" in container and "keys" in container and "weights" in container for idx in range(len(container["keys"])): yield container["keys"][idx], container["data"][idx], container["labels"][idx], container["weights"][idx] def _is_partition_fetched(self, partition_id: int) -> bool: + if partition_id not in self._partition_locks or partition_id not in self._partition_valid: + return False + with self._partition_locks[partition_id]: return self._partition_valid[partition_id] @@ -304,7 +319,6 @@ def _wait_for_new_partition_data(self, partition_id: int) -> None: def prefetched_partition_generator( self, worker_id: int, partition_id: int ) -> Iterator[tuple[int, bytes, int, Optional[float]]]: - assert self._pref_started[partition_id], f"Prefetching for partition {partition_id} has not been started" last_idx = -1 while not self._is_partition_fetched(partition_id): @@ -322,7 +336,16 @@ def prefetched_partition_generator( max_idx = self._partition_max_index(partition_id) yield from self._get_partition_data(last_idx, max_idx, partition_id) + def start_prefetching(self, worker_id: int) -> None: + # WIP change of prefetching model + if self._prefetched_partitions < 1: + return + + self._prefetch_partition(worker_id, self._prefetched_partitions - 1) + def all_partition_generator(self, worker_id: int) -> Iterator[tuple[int, bytes, int, Optional[float]]]: + #self.start_prefetching(worker_id) + for _ in range(self._prefetched_partitions): self._prefetch_partition(worker_id) From cc791cf56430d7ae640037585406cfe9f5288f69 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 3 Oct 2023 15:36:25 +0200 Subject: [PATCH 17/46] differentiate between parallel requests and number of partitions. next steps: fix linting etc, test everything, review code, benchmark --- modyn/common/grpc/grpc_helpers.py | 13 +- modyn/config/schema/pipeline-schema.yaml | 6 +- modyn/protos/trainer_server.proto | 7 +- .../selector/internal/grpc/selector_server.py | 19 +-- modyn/selector/internal/selector_manager.py | 10 +- modyn/storage/internal/grpc/grpc_server.py | 13 +- .../internal/grpc/storage_grpc_servicer.py | 2 +- modyn/supervisor/internal/grpc_handler.py | 14 +- .../internal/data/test_online_dataset.py | 34 ++-- .../data/test_per_class_online_dataset.py | 2 +- .../internal/dataset/data_utils.py | 9 +- .../internal/dataset/online_dataset.py | 158 +++++++++++------- .../dataset/per_class_online_dataset.py | 6 +- .../grpc/generated/trainer_server_pb2.py | 36 ++-- .../grpc/generated/trainer_server_pb2.pyi | 11 +- .../internal/trainer/pytorch_trainer.py | 3 +- .../internal/utils/training_info.py | 3 +- 17 files changed, 186 insertions(+), 160 deletions(-) diff --git a/modyn/common/grpc/grpc_helpers.py b/modyn/common/grpc/grpc_helpers.py index 5bcdca03a..f9b8ab2ea 100644 --- a/modyn/common/grpc/grpc_helpers.py +++ b/modyn/common/grpc/grpc_helpers.py @@ -3,11 +3,10 @@ import logging import multiprocessing as mp import os -import pickle import socket import time from concurrent import futures -from typing import Any, Callable +from typing import Any, Callable, Optional import grpc from modyn.utils import MAX_MESSAGE_SIZE @@ -64,7 +63,7 @@ def _run_server_worker(bind_address: str, add_servicer_callback: Callable, modyn class GenericGRPCServer: def __init__( - self, modyn_config: dict, port: str, add_servicer_callback: Callable, callback_kwargs: dict = {} + self, modyn_config: dict, port: str, add_servicer_callback: Callable, callback_kwargs: Optional[dict] = None ) -> None: """Initialize the GRPC server. @@ -74,7 +73,7 @@ def __init__( self.port = port self.modyn_config = modyn_config self.add_servicer_callback = add_servicer_callback - self.callback_kwargs = callback_kwargs + self.callback_kwargs = callback_kwargs if callback_kwargs is not None else {} self.workers = [] def __enter__(self) -> Any: @@ -97,12 +96,6 @@ def __enter__(self) -> Any: return self def __getstate__(self): - for variable_name, value in vars(self).items(): - try: - pickle.dumps(value) - except: - print(f"{variable_name} with value {value} is not pickable") - state = self.__dict__.copy() del state["add_servicer_callback"] return state diff --git a/modyn/config/schema/pipeline-schema.yaml b/modyn/config/schema/pipeline-schema.yaml index 8b1bd617d..8bbdcf792 100644 --- a/modyn/config/schema/pipeline-schema.yaml +++ b/modyn/config/schema/pipeline-schema.yaml @@ -48,10 +48,14 @@ properties: type: number description: | The number of epochs per trigger. Defaults to 1, if not given. - prefetched_partitions: + num_prefetched_partitions: type: number description: | The number of partitions that are prefetched per DataLoader worker. Defaults to 1, if not given. + parallel_prefetch_requests: + type: number + description: | + The number of parallel prefetch requests per DataLoader worker. Defaults to 1, if not given. Values bigger than num_prefetched_partitions are equal to num_prefetched_partitions. device: type: string description: | diff --git a/modyn/protos/trainer_server.proto b/modyn/protos/trainer_server.proto index 12c68e492..cf31c2850 100644 --- a/modyn/protos/trainer_server.proto +++ b/modyn/protos/trainer_server.proto @@ -53,9 +53,10 @@ message StartTrainingRequest { PythonString label_transformer = 19; JsonString grad_scaler_configuration = 20; int32 epochs_per_trigger = 21; - int32 prefetched_partitions = 22; - optional int32 seed = 23; - optional PythonString tokenizer = 24; + int32 num_prefetched_partitions = 22; + int32 parallel_prefetch_requests = 23; + optional int32 seed = 24; + optional PythonString tokenizer = 25; } message StartTrainingResponse { diff --git a/modyn/selector/internal/grpc/selector_server.py b/modyn/selector/internal/grpc/selector_server.py index 67c8edac0..ec79f0c68 100644 --- a/modyn/selector/internal/grpc/selector_server.py +++ b/modyn/selector/internal/grpc/selector_server.py @@ -1,20 +1,9 @@ -import contextlib -import datetime -import functools import logging -import multiprocessing as mp -import os -import pickle -import socket -import time -from concurrent import futures - -import grpc + from modyn.common.grpc import GenericGRPCServer from modyn.selector.internal.grpc.generated.selector_pb2_grpc import add_SelectorServicer_to_server # noqa: E402, E501 from modyn.selector.internal.grpc.selector_grpc_servicer import SelectorGRPCServicer from modyn.selector.internal.selector_manager import SelectorManager -from modyn.utils import MAX_MESSAGE_SIZE logger = logging.getLogger(__name__) @@ -34,12 +23,6 @@ def __init__(self, modyn_config: dict) -> None: super().__init__(modyn_config, modyn_config["selector"]["port"], SelectorGRPCServer.callback, callback_kwargs) def __getstate__(self): - for variable_name, value in vars(self).items(): - try: - pickle.dumps(value) - except: - print(f"{variable_name} with value {value} is not pickable") - state = self.__dict__.copy() if "add_servicer_callback" in state: del state["add_servicer_callback"] diff --git a/modyn/selector/internal/selector_manager.py b/modyn/selector/internal/selector_manager.py index b066a5d07..f12a9b4d5 100644 --- a/modyn/selector/internal/selector_manager.py +++ b/modyn/selector/internal/selector_manager.py @@ -26,7 +26,9 @@ def __init__(self, modyn_config: dict) -> None: self._next_pipeline_lock = self._manager.Lock() self._selector_cache_size = self._modyn_config["selector"]["keys_in_selector_cache"] - # TODO(create issue): currently we have to prepare N locks and then share. This is because we cannot share the manager with subprocesses. For now not a big problem since we mostly run one pipeline but we might want to redesign this. + # TODO(create issue): currently we have to prepare N locks and then share. + # This is because we cannot share the manager with subprocesses. + # For now not a big problem since we mostly run one pipeline but we might want to redesign this. self._prepared_locks = [self._manager.Lock() for _ in range(64)] self.init_metadata_db() @@ -77,8 +79,10 @@ def _populate_pipeline_if_exists(self, pipeline_id: int) -> None: if pipeline is None: return logging.info( - f"[{os.getpid()}] Instantiating new selector for pipeline {pipeline_id}" - + " that was in the DB but previously unknown to this process" + "[%d] Instantiating new selector for pipeline %d" + + " that was in the DB but previously unknown to this process", + os.getpid(), + pipeline_id, ) self._instantiate_selector(pipeline_id, pipeline.num_workers, pipeline.selection_strategy) diff --git a/modyn/storage/internal/grpc/grpc_server.py b/modyn/storage/internal/grpc/grpc_server.py index a2baac37d..725a3d826 100644 --- a/modyn/storage/internal/grpc/grpc_server.py +++ b/modyn/storage/internal/grpc/grpc_server.py @@ -1,20 +1,11 @@ """GRPC server context manager.""" -import contextlib -import datetime + import logging -import multiprocessing as mp -import os -import socket -import time -from concurrent import futures -from typing import Any - -import grpc + from modyn.common.grpc import GenericGRPCServer from modyn.storage.internal.grpc.generated.storage_pb2_grpc import add_StorageServicer_to_server from modyn.storage.internal.grpc.storage_grpc_servicer import StorageGRPCServicer -from modyn.utils import MAX_MESSAGE_SIZE logger = logging.getLogger(__name__) diff --git a/modyn/storage/internal/grpc/storage_grpc_servicer.py b/modyn/storage/internal/grpc/storage_grpc_servicer.py index f3c8c8936..a28de26f0 100644 --- a/modyn/storage/internal/grpc/storage_grpc_servicer.py +++ b/modyn/storage/internal/grpc/storage_grpc_servicer.py @@ -53,7 +53,7 @@ def __init__(self, config: dict): self._sample_batch_size = self.modyn_config["storage"]["sample_batch_size"] super().__init__() - # pylint: disable-next=unused-argument,invalid-name + # pylint: disable-next=unused-argument,invalid-name,too-many-locals def Get(self, request: GetRequest, context: grpc.ServicerContext) -> Iterable[GetResponse]: """Return the data for the given keys. diff --git a/modyn/supervisor/internal/grpc_handler.py b/modyn/supervisor/internal/grpc_handler.py index 01f83ee75..ee1ad6594 100644 --- a/modyn/supervisor/internal/grpc_handler.py +++ b/modyn/supervisor/internal/grpc_handler.py @@ -300,10 +300,15 @@ def start_training( else: epochs_per_trigger = 1 - if "prefetched_partitions" in pipeline_config["training"]: - prefetched_partitions = pipeline_config["training"]["prefetched_partitions"] + if "num_prefetched_partitions" in pipeline_config["training"]: + num_prefetched_partitions = pipeline_config["training"]["num_prefetched_partitions"] else: - prefetched_partitions = 1 + num_prefetched_partitions = 1 + + if "parallel_prefetch_requests" in pipeline_config["training"]: + parallel_prefetch_requests = pipeline_config["training"]["parallel_prefetch_requests"] + else: + parallel_prefetch_requests = 1 if "seed" in pipeline_config["training"]: seed = pipeline_config["training"]["seed"] @@ -371,7 +376,8 @@ def start_training( "lr_scheduler": TrainerServerJsonString(value=json.dumps(lr_scheduler_configs)), "grad_scaler_configuration": TrainerServerJsonString(value=json.dumps(grad_scaler_config)), "epochs_per_trigger": epochs_per_trigger, - "prefetched_partitions": prefetched_partitions, + "num_prefetched_partitions": num_prefetched_partitions, + "parallel_prefetch_requests": parallel_prefetch_requests, "seed": seed, "tokenizer": PythonString(value=tokenizer) if tokenizer is not None else None, } diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index c307ddde8..243402203 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -67,7 +67,7 @@ def test_invalid_bytes_parser(test_weights, test_grpc_connection_established): training_id=42, tokenizer=None, log_path=None, - prefetched_partitions=1, + num_prefetched_partitions=1, )._init_transforms() with pytest.raises(ValueError): @@ -82,7 +82,7 @@ def test_invalid_bytes_parser(test_weights, test_grpc_connection_established): training_id=42, tokenizer="", log_path=None, - prefetched_partitions=1, + num_prefetched_partitions=1, )._init_transforms() @@ -106,7 +106,7 @@ def test_init(test_insecure_channel, test_grpc_connection_established, test_grpc training_id=42, tokenizer=None, log_path=None, - prefetched_partitions=1, + num_prefetched_partitions=1, ) assert online_dataset._pipeline_id == 1 assert online_dataset._trigger_id == 1 @@ -174,7 +174,7 @@ def test_get_data_from_storage( training_id=42, tokenizer=None, log_path=None, - prefetched_partitions=0, + num_prefetched_partitions=0, ) online_dataset._init_grpc() keys = [] @@ -245,7 +245,7 @@ def test_deserialize_torchvision_transforms( training_id=42, tokenizer=None, log_path=None, - prefetched_partitions=1, + num_prefetched_partitions=1, ) online_dataset._bytes_parser_function = bytes_parser_function online_dataset._setup_composed_transform() @@ -289,7 +289,7 @@ def test_dataset_iter( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -334,7 +334,7 @@ def test_dataset_iter_with_parsing( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -379,7 +379,7 @@ def test_dataloader_dataset( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -425,7 +425,7 @@ def test_dataloader_dataset_weighted( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -479,7 +479,7 @@ def test_dataloader_dataset_multi_worker( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -509,7 +509,7 @@ def test_init_grpc(test_insecure_channel, test_grpc_connection_established, test storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=1, + num_prefetched_partitions=1, tokenizer=None, log_path=None, ) @@ -545,7 +545,7 @@ def test_init_transforms( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=1, + num_prefetched_partitions=1, tokenizer=None, log_path=None, ) @@ -606,7 +606,7 @@ def test_iter_multi_partition( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -659,7 +659,7 @@ def test_iter_multi_partition_weighted( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -715,7 +715,7 @@ def test_iter_multi_partition_cross( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -785,7 +785,7 @@ def test_iter_multi_partition_multi_workers( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) @@ -837,7 +837,7 @@ def test_multi_epoch_dataloader_dataset( storage_address="localhost:1234", selector_address="localhost:1234", training_id=42, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, log_path=None, ) diff --git a/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py index 26b3dd3fc..2a2f9c640 100644 --- a/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py @@ -70,7 +70,7 @@ def test_dataloader_dataset( selector_address="localhost:1234", training_id=42, initial_filtered_label=0, - prefetched_partitions=prefetched_partitions, + num_prefetched_partitions=prefetched_partitions, tokenizer=None, ) dataloader = torch.utils.data.DataLoader(online_dataset, batch_size=4) diff --git a/modyn/trainer_server/internal/dataset/data_utils.py b/modyn/trainer_server/internal/dataset/data_utils.py index f03a16abe..3a9a4046b 100644 --- a/modyn/trainer_server/internal/dataset/data_utils.py +++ b/modyn/trainer_server/internal/dataset/data_utils.py @@ -22,7 +22,8 @@ def prepare_dataloaders( storage_address: str, selector_address: str, training_id: int, - prefetched_partitions: int, + num_prefetched_partitions: int, + parallel_prefetch_requests: int, tokenizer: Optional[str], log_path: Optional[pathlib.Path], ) -> tuple[torch.utils.data.DataLoader, Optional[torch.utils.data.DataLoader]]: @@ -55,7 +56,8 @@ def prepare_dataloaders( storage_address, selector_address, training_id, - prefetched_partitions, + num_prefetched_partitions, + parallel_prefetch_requests, tokenizer, log_path, ) @@ -81,7 +83,8 @@ def prepare_per_class_dataloader_from_online_dataset( online_dataset._selector_address, online_dataset._training_id, initial_filtered_label, - online_dataset._prefetched_partitions, + online_dataset._num_prefetched_partitions, + online_dataset._parallel_prefetch_requests, online_dataset._tokenizer_name, ) return torch.utils.data.DataLoader(dataset, batch_size=batch_size, num_workers=num_workers) diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index b48a632a8..b15460555 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -1,5 +1,4 @@ import contextlib -import functools import json import logging import os @@ -42,7 +41,8 @@ def __init__( storage_address: str, selector_address: str, training_id: int, - prefetched_partitions: int, + num_prefetched_partitions: int, + parallel_prefetch_requests: int, tokenizer: Optional[str], log_path: Optional[pathlib.Path], ): @@ -51,7 +51,8 @@ def __init__( self._training_id = training_id self._dataset_id = dataset_id self._first_call = True - self._prefetched_partitions = prefetched_partitions + self._num_prefetched_partitions = num_prefetched_partitions + self._parallel_prefetch_requests = parallel_prefetch_requests self._bytes_parser = bytes_parser self._serialized_transforms = serialized_transforms @@ -77,6 +78,8 @@ def __init__( self._partition_valid_until: dict[int, int] = {} self._partition_valid: dict[int, bool] = {} self._next_partition_to_fetch = 0 + self._launched_prefetches = 0 + self._start_prefetch_lock = threading.Lock() if log_path is None: logger.warning("Did not provide log path for OnlineDataset - logging disabled.") @@ -231,57 +234,80 @@ def _persist_log(self, worker_id: int) -> None: with open(log_file, "w", encoding="utf-8") as logfile: json.dump(self._log, logfile) - def _prefetch_partition(self, worker_id: int) -> None: - if self._prefetched_partitions < 1 or self._next_partition_to_fetch >= self._num_partitions: - return # Prefetching disabled or nothing more to prefetch - - assert self._next_partition_to_fetch >= 0 - assert ( - self._next_partition_to_fetch not in self._data_threads - ), f"Prefetching for partition {self._next_partition_to_fetch} has already been started" - - self._thread_data_container[self._next_partition_to_fetch] = { - "data": [], - "keys": [], - "labels": [], - "weights": [], - } - self._partition_valid[self._next_partition_to_fetch] = False - self._partition_valid_until[self._next_partition_to_fetch] = -1 - self._partition_locks[self._next_partition_to_fetch] = threading.Lock() - self._partition_signals[self._next_partition_to_fetch] = threading.Condition( - self._partition_locks[self._next_partition_to_fetch] - ) - - #def potential_callback(): - # self._info("Prefetch callback called.") - # self._prefetch_partition(worker_id, num_additional_prefetches - 1) - - #callback = None if num_additional_prefetches == 0 else potential_callback - - self._data_threads[self._next_partition_to_fetch] = threading.Thread( - target=self._get_data, - args=( - self._thread_data_container[self._next_partition_to_fetch], - worker_id, - self._next_partition_to_fetch, - self._partition_valid, - self._partition_valid_until, - self._partition_locks, - self._partition_signals, - None, - ), - ) - - self._data_threads[self._next_partition_to_fetch].start() - self._pref_started[self._next_partition_to_fetch] = True - - self._next_partition_to_fetch += 1 + def _prefetch_partition(self, worker_id: int, maybe_continue: bool = False) -> None: + with self._start_prefetch_lock: + if self._num_prefetched_partitions < 1 or self._next_partition_to_fetch >= self._num_partitions: + return # Prefetching disabled or nothing more to prefetch + + if maybe_continue and self._launched_prefetches >= self._num_prefetched_partitions: + return # Two callbacks started to prefetch basically at the same time + + if maybe_continue: + # Do this as early as possible to avoid running into the "problem" above frequently + self._launched_prefetches += 1 + + assert self._next_partition_to_fetch >= 0 + assert ( + self._next_partition_to_fetch not in self._data_threads + ), f"Prefetching for partition {self._next_partition_to_fetch} has already been started" + + self._thread_data_container[self._next_partition_to_fetch] = { + "data": [], + "keys": [], + "labels": [], + "weights": [], + } + self._partition_valid[self._next_partition_to_fetch] = False + self._partition_valid_until[self._next_partition_to_fetch] = -1 + self._partition_locks[self._next_partition_to_fetch] = threading.Lock() + self._partition_signals[self._next_partition_to_fetch] = threading.Condition( + self._partition_locks[self._next_partition_to_fetch] + ) + + callback = None + if maybe_continue: + + def callback_func(): + self._info("Prefetch callback called.", worker_id) + + # It might be that between the check and the actual launch + # We start another launch + # We catch this with the lock within _prefetch_partition + if self._launched_prefetches < self._num_prefetched_partitions: + self._info( + f"Only {self._launched_prefetches} out of {self._num_prefetched_partitions}" + + " partitions have been fetched, issuing another request.", + worker_id, + ) + self._prefetch_partition(worker_id, True) + else: + self._info("Not issuing another request.", worker_id) + + callback = callback_func + + self._data_threads[self._next_partition_to_fetch] = threading.Thread( + target=self._get_data, + args=( + self._thread_data_container[self._next_partition_to_fetch], + worker_id, + self._next_partition_to_fetch, + self._partition_valid, + self._partition_valid_until, + self._partition_locks, + self._partition_signals, + callback, + ), + ) + + self._data_threads[self._next_partition_to_fetch].start() + self._pref_started[self._next_partition_to_fetch] = True + + self._next_partition_to_fetch += 1 def _fetch_partition_noprefetch( self, worker_id: int, partition_id: int ) -> Iterator[tuple[int, bytes, int, Optional[float]]]: - assert self._prefetched_partitions < 1 + assert self._num_prefetched_partitions < 1 container: dict[str, Any] = {"data": [], "keys": [], "labels": [], "weights": []} self._get_data(container, worker_id, partition_id, None, None, None, None, None) assert "data" in container and "labels" in container and "keys" in container and "weights" in container @@ -337,25 +363,31 @@ def prefetched_partition_generator( yield from self._get_partition_data(last_idx, max_idx, partition_id) def start_prefetching(self, worker_id: int) -> None: - # WIP change of prefetching model - if self._prefetched_partitions < 1: + if self._num_prefetched_partitions < 1: + # No prefetching at all return - self._prefetch_partition(worker_id, self._prefetched_partitions - 1) + if self._num_prefetched_partitions <= self._parallel_prefetch_requests: + # We can emit prefetching requests once and be done with it + for _ in range(self._num_prefetched_partitions): + self._prefetch_partition(worker_id, False) - def all_partition_generator(self, worker_id: int) -> Iterator[tuple[int, bytes, int, Optional[float]]]: - #self.start_prefetching(worker_id) + return - for _ in range(self._prefetched_partitions): - self._prefetch_partition(worker_id) + # We have to respect the limit of parallel requests + for _ in range(self._parallel_prefetch_requests): + self._prefetch_partition(worker_id, True) + + def all_partition_generator(self, worker_id: int) -> Iterator[tuple[int, bytes, int, Optional[float]]]: + self.start_prefetching(worker_id) for partition_id in range(self._num_partitions): self._persist_log(worker_id) - if self._prefetched_partitions > 0: - # Prefetched generator + if self._num_prefetched_partitions > 0: if partition_id < self._num_partitions - 1: - self._prefetch_partition(worker_id) + # As we consume one partition, prefetch exactly one more partition + self._prefetch_partition(worker_id, False) yield from self.prefetched_partition_generator(worker_id, partition_id) else: @@ -399,11 +431,13 @@ def __iter__(self) -> Generator: assert self._transform is not None self._num_partitions = self._key_source.get_num_data_partitions() self._info( - f"Total number of partitions will be {self._num_partitions}. Prefetch factor={self._prefetched_partitions}", + f"Total number of partitions will be {self._num_partitions}." + + f"Parallel prefetch requests = {self._parallel_prefetch_requests}" + + f"Num prefetched partitions = {self._num_prefetched_partitions}", worker_id, ) self._log["num_partitions"] = self._num_partitions - self._prefetched_partitions = min(self._prefetched_partitions, self._num_partitions) + self._num_prefetched_partitions = min(self._num_prefetched_partitions, self._num_partitions) for data_tuple in self.all_partition_generator(worker_id): if (transformed_tuple := self._get_transformed_data_tuple(*data_tuple)) is not None: diff --git a/modyn/trainer_server/internal/dataset/per_class_online_dataset.py b/modyn/trainer_server/internal/dataset/per_class_online_dataset.py index 5a3c6fbec..f10adaa9f 100644 --- a/modyn/trainer_server/internal/dataset/per_class_online_dataset.py +++ b/modyn/trainer_server/internal/dataset/per_class_online_dataset.py @@ -20,7 +20,8 @@ def __init__( selector_address: str, training_id: int, initial_filtered_label: int, - prefetched_partitions: int, + num_prefetched_partitions: int, + parallel_prefetch_requests: int, tokenizer: Optional[str], ): super().__init__( @@ -32,7 +33,8 @@ def __init__( storage_address, selector_address, training_id, - prefetched_partitions, + num_prefetched_partitions, + parallel_prefetch_requests, tokenizer, None, ) diff --git a/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.py b/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.py index bf91935c3..e62f14fdd 100644 --- a/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.py +++ b/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.py @@ -14,7 +14,7 @@ -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14trainer_server.proto\x12\x07trainer\"\x1b\n\nJsonString\x12\r\n\x05value\x18\x01 \x01(\t\"\x1d\n\x0cPythonString\x12\r\n\x05value\x18\x01 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.trainer.StoreFinalModelResponse\"\x00\x12U\n\x10get_latest_model\x12\x1e.trainer.GetLatestModelRequest\x1a\x1f.trainer.GetLatestModelResponse\"\x00\x62\x06proto3') _globals = globals() _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) @@ -35,21 +35,21 @@ _globals['_CHECKPOINTINFO']._serialized_start=220 _globals['_CHECKPOINTINFO']._serialized_end=290 _globals['_STARTTRAININGREQUEST']._serialized_start=293 - _globals['_STARTTRAININGREQUEST']._serialized_end=1149 - _globals['_STARTTRAININGRESPONSE']._serialized_start=1151 - _globals['_STARTTRAININGRESPONSE']._serialized_end=1221 - _globals['_TRAININGSTATUSREQUEST']._serialized_start=1223 - _globals['_TRAININGSTATUSREQUEST']._serialized_end=1267 - _globals['_TRAININGSTATUSRESPONSE']._serialized_start=1270 - _globals['_TRAININGSTATUSRESPONSE']._serialized_end=1692 - _globals['_STOREFINALMODELREQUEST']._serialized_start=1694 - _globals['_STOREFINALMODELREQUEST']._serialized_end=1739 - _globals['_STOREFINALMODELRESPONSE']._serialized_start=1741 - _globals['_STOREFINALMODELRESPONSE']._serialized_end=1805 - _globals['_GETLATESTMODELREQUEST']._serialized_start=1807 - _globals['_GETLATESTMODELREQUEST']._serialized_end=1851 - _globals['_GETLATESTMODELRESPONSE']._serialized_start=1853 - _globals['_GETLATESTMODELRESPONSE']._serialized_end=1918 - _globals['_TRAINERSERVER']._serialized_start=1921 - _globals['_TRAINERSERVER']._serialized_end=2378 + _globals['_STARTTRAININGREQUEST']._serialized_end=1189 + _globals['_STARTTRAININGRESPONSE']._serialized_start=1191 + _globals['_STARTTRAININGRESPONSE']._serialized_end=1261 + _globals['_TRAININGSTATUSREQUEST']._serialized_start=1263 + _globals['_TRAININGSTATUSREQUEST']._serialized_end=1307 + _globals['_TRAININGSTATUSRESPONSE']._serialized_start=1310 + _globals['_TRAININGSTATUSRESPONSE']._serialized_end=1732 + _globals['_STOREFINALMODELREQUEST']._serialized_start=1734 + _globals['_STOREFINALMODELREQUEST']._serialized_end=1779 + _globals['_STOREFINALMODELRESPONSE']._serialized_start=1781 + _globals['_STOREFINALMODELRESPONSE']._serialized_end=1845 + _globals['_GETLATESTMODELREQUEST']._serialized_start=1847 + _globals['_GETLATESTMODELREQUEST']._serialized_end=1891 + _globals['_GETLATESTMODELRESPONSE']._serialized_start=1893 + _globals['_GETLATESTMODELRESPONSE']._serialized_end=1958 + _globals['_TRAINERSERVER']._serialized_start=1961 + _globals['_TRAINERSERVER']._serialized_end=2418 # @@protoc_insertion_point(module_scope) diff --git a/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.pyi b/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.pyi index 697e0b64e..9723ebdb8 100644 --- a/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.pyi +++ b/modyn/trainer_server/internal/grpc/generated/trainer_server_pb2.pyi @@ -133,7 +133,8 @@ class StartTrainingRequest(google.protobuf.message.Message): LABEL_TRANSFORMER_FIELD_NUMBER: builtins.int GRAD_SCALER_CONFIGURATION_FIELD_NUMBER: builtins.int EPOCHS_PER_TRIGGER_FIELD_NUMBER: builtins.int - PREFETCHED_PARTITIONS_FIELD_NUMBER: builtins.int + NUM_PREFETCHED_PARTITIONS_FIELD_NUMBER: builtins.int + PARALLEL_PREFETCH_REQUESTS_FIELD_NUMBER: builtins.int SEED_FIELD_NUMBER: builtins.int TOKENIZER_FIELD_NUMBER: builtins.int pipeline_id: builtins.int @@ -167,7 +168,8 @@ class StartTrainingRequest(google.protobuf.message.Message): @property def grad_scaler_configuration(self) -> global___JsonString: ... epochs_per_trigger: builtins.int - prefetched_partitions: builtins.int + num_prefetched_partitions: builtins.int + parallel_prefetch_requests: builtins.int seed: builtins.int @property def tokenizer(self) -> global___PythonString: ... @@ -195,12 +197,13 @@ class StartTrainingRequest(google.protobuf.message.Message): label_transformer: global___PythonString | None = ..., grad_scaler_configuration: global___JsonString | None = ..., epochs_per_trigger: builtins.int = ..., - prefetched_partitions: builtins.int = ..., + num_prefetched_partitions: builtins.int = ..., + parallel_prefetch_requests: builtins.int = ..., seed: builtins.int | None = ..., tokenizer: global___PythonString | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["_seed", b"_seed", "_tokenizer", b"_tokenizer", "bytes_parser", b"bytes_parser", "checkpoint_info", b"checkpoint_info", "criterion_parameters", b"criterion_parameters", "data_info", b"data_info", "grad_scaler_configuration", b"grad_scaler_configuration", "label_transformer", b"label_transformer", "lr_scheduler", b"lr_scheduler", "model_configuration", b"model_configuration", "seed", b"seed", "tokenizer", b"tokenizer", "torch_optimizers_configuration", b"torch_optimizers_configuration"]) -> builtins.bool: ... - def ClearField(self, field_name: typing_extensions.Literal["_seed", b"_seed", "_tokenizer", b"_tokenizer", "amp", b"amp", "batch_size", b"batch_size", "bytes_parser", b"bytes_parser", "checkpoint_info", b"checkpoint_info", "criterion_parameters", b"criterion_parameters", "data_info", b"data_info", "device", b"device", "epochs_per_trigger", b"epochs_per_trigger", "grad_scaler_configuration", b"grad_scaler_configuration", "label_transformer", b"label_transformer", "load_optimizer_state", b"load_optimizer_state", "lr_scheduler", b"lr_scheduler", "model_configuration", b"model_configuration", "model_id", b"model_id", "pipeline_id", b"pipeline_id", "prefetched_partitions", b"prefetched_partitions", "pretrained_model_id", b"pretrained_model_id", "seed", b"seed", "tokenizer", b"tokenizer", "torch_criterion", b"torch_criterion", "torch_optimizers_configuration", b"torch_optimizers_configuration", "transform_list", b"transform_list", "trigger_id", b"trigger_id", "use_pretrained_model", b"use_pretrained_model"]) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["_seed", b"_seed", "_tokenizer", b"_tokenizer", "amp", b"amp", "batch_size", b"batch_size", "bytes_parser", b"bytes_parser", "checkpoint_info", b"checkpoint_info", "criterion_parameters", b"criterion_parameters", "data_info", b"data_info", "device", b"device", "epochs_per_trigger", b"epochs_per_trigger", "grad_scaler_configuration", b"grad_scaler_configuration", "label_transformer", b"label_transformer", "load_optimizer_state", b"load_optimizer_state", "lr_scheduler", b"lr_scheduler", "model_configuration", b"model_configuration", "model_id", b"model_id", "num_prefetched_partitions", b"num_prefetched_partitions", "parallel_prefetch_requests", b"parallel_prefetch_requests", "pipeline_id", b"pipeline_id", "pretrained_model_id", b"pretrained_model_id", "seed", b"seed", "tokenizer", b"tokenizer", "torch_criterion", b"torch_criterion", "torch_optimizers_configuration", b"torch_optimizers_configuration", "transform_list", b"transform_list", "trigger_id", b"trigger_id", "use_pretrained_model", b"use_pretrained_model"]) -> None: ... @typing.overload def WhichOneof(self, oneof_group: typing_extensions.Literal["_seed", b"_seed"]) -> typing_extensions.Literal["seed"] | None: ... @typing.overload diff --git a/modyn/trainer_server/internal/trainer/pytorch_trainer.py b/modyn/trainer_server/internal/trainer/pytorch_trainer.py index 8fea848b8..32b87fdb7 100644 --- a/modyn/trainer_server/internal/trainer/pytorch_trainer.py +++ b/modyn/trainer_server/internal/trainer/pytorch_trainer.py @@ -161,7 +161,8 @@ def __init__( training_info.storage_address, training_info.selector_address, training_info.training_id, - training_info.prefetched_partitions, + training_info.num_prefetched_partitions, + training_info.parallel_prefetch_requests, training_info.tokenizer, self._dataset_log_path, ) diff --git a/modyn/trainer_server/internal/utils/training_info.py b/modyn/trainer_server/internal/utils/training_info.py index 7cce81b75..41465f691 100644 --- a/modyn/trainer_server/internal/utils/training_info.py +++ b/modyn/trainer_server/internal/utils/training_info.py @@ -27,7 +27,8 @@ def __init__( self.pipeline_id = request.pipeline_id self.trigger_id = request.trigger_id self.training_id = training_id - self.prefetched_partitions = request.prefetched_partitions + self.num_prefetched_partitions = request.num_prefetched_partitions + self.parallel_prefetch_requests = request.parallel_prefetch_requests self.dataset_id = request.data_info.dataset_id self.num_dataloaders = request.data_info.num_dataloaders From 3c38576b8cceae93d668a2abdbe16c1125294641 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 3 Oct 2023 20:26:35 +0200 Subject: [PATCH 18/46] Compliance. tests for new grpc server are missing --- .../online_dataset/test_online_dataset.py | 23 +++++++--- modyn/common/grpc/grpc_helpers.py | 14 +++--- .../selector/internal/grpc/selector_server.py | 5 ++- modyn/selector/internal/selector_manager.py | 9 ++-- modyn/selector/selector.py | 3 ++ modyn/storage/internal/grpc/grpc_server.py | 3 +- .../models/test_pipelines.py | 13 ++---- .../test_metadata_database_connection.py | 6 +-- .../internal/grpc/test_model_storage.database | Bin 57344 -> 0 bytes .../grpc/test_model_storage_grpc_servicer.py | 4 +- .../internal/grpc/test_selector_server.py | 41 +----------------- .../internal/test_selector_manager.py | 10 ++++- modyn/tests/selector/test_selector.py | 21 +++++---- .../selector/test_selector_entrypoint.py | 12 +++-- .../storage/internal/grpc/test_grpc_server.py | 7 --- modyn/tests/storage/test_storage.py | 2 +- .../internal/data/test_data_utils.py | 2 +- .../internal/data/test_online_dataset.py | 40 ++++++++++++++++- .../data/test_per_class_online_dataset.py | 3 ++ .../internal/trainer/test_pytorch_trainer.py | 1 + .../internal/dataset/online_dataset.py | 2 +- 21 files changed, 126 insertions(+), 95 deletions(-) delete mode 100644 modyn/tests/model_storage/internal/grpc/test_model_storage.database diff --git a/integrationtests/online_dataset/test_online_dataset.py b/integrationtests/online_dataset/test_online_dataset.py index 9b11339e4..599483255 100644 --- a/integrationtests/online_dataset/test_online_dataset.py +++ b/integrationtests/online_dataset/test_online_dataset.py @@ -267,6 +267,7 @@ def test_dataset_impl( num_dataworkers: int, batch_size: int, prefetched_partitions: int, + parallel_prefetch_requests: int, pipeline_id: int, trigger_id: int, items: list[int], @@ -283,6 +284,7 @@ def test_dataset_impl( get_selector_address(), 42, prefetched_partitions, + parallel_prefetch_requests, None, None, ) @@ -349,12 +351,21 @@ def test_dataset() -> None: for num_dataworkers in [0, 1, 2, 4, 8, 16]: pipeline_id, trigger_id = prepare_selector(num_dataworkers, keys) for prefetched_partitions in [0, 1, 2, 3, 4, 5, 999]: - for batch_size in [1, 2, 10]: - print( - f"Testing num_workers = {num_dataworkers}, partitions = {prefetched_partitions}," - + f"batch_size = {batch_size}" - ) - test_dataset_impl(num_dataworkers, batch_size, prefetched_partitions, pipeline_id, trigger_id, keys) + for parallel_prefetch_requests in [1, 2, 5, 999]: + for batch_size in [1, 2, 10]: + print( + f"Testing num_workers = {num_dataworkers}, partitions = {prefetched_partitions}," + + f"batch_size = {batch_size}, parallel_prefetch_requests={parallel_prefetch_requests}" + ) + test_dataset_impl( + num_dataworkers, + batch_size, + prefetched_partitions, + parallel_prefetch_requests, + pipeline_id, + trigger_id, + keys, + ) def main() -> None: diff --git a/modyn/common/grpc/grpc_helpers.py b/modyn/common/grpc/grpc_helpers.py index f9b8ab2ea..b8fd680e6 100644 --- a/modyn/common/grpc/grpc_helpers.py +++ b/modyn/common/grpc/grpc_helpers.py @@ -6,7 +6,7 @@ import socket import time from concurrent import futures -from typing import Any, Callable, Optional +from typing import Any, Callable, Generator, Optional import grpc from modyn.utils import MAX_MESSAGE_SIZE @@ -18,7 +18,7 @@ @contextlib.contextmanager -def reserve_port(port: str): +def reserve_port(port: str) -> Generator: """Find and reserve a port for all subprocesses to use.""" sock = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) @@ -32,7 +32,7 @@ def reserve_port(port: str): sock.close() -def _wait_forever(server): +def _wait_forever(server: Any) -> None: try: while True: time.sleep(datetime.timedelta(days=1).total_seconds()) @@ -40,7 +40,9 @@ def _wait_forever(server): server.stop(None) -def _run_server_worker(bind_address: str, add_servicer_callback: Callable, modyn_config: dict, callback_kwargs: dict): +def _run_server_worker( + bind_address: str, add_servicer_callback: Callable, modyn_config: dict, callback_kwargs: dict +) -> None: """Start a server in a subprocess.""" logging.info(f"[{os.getpid()}] Starting new gRPC server process.") @@ -74,7 +76,7 @@ def __init__( self.modyn_config = modyn_config self.add_servicer_callback = add_servicer_callback self.callback_kwargs = callback_kwargs if callback_kwargs is not None else {} - self.workers = [] + self.workers: list[mp.Process] = [] def __enter__(self) -> Any: """Enter the context manager. @@ -95,7 +97,7 @@ def __enter__(self) -> Any: return self - def __getstate__(self): + def __getstate__(self) -> dict: state = self.__dict__.copy() del state["add_servicer_callback"] return state diff --git a/modyn/selector/internal/grpc/selector_server.py b/modyn/selector/internal/grpc/selector_server.py index ec79f0c68..80debf1ec 100644 --- a/modyn/selector/internal/grpc/selector_server.py +++ b/modyn/selector/internal/grpc/selector_server.py @@ -1,4 +1,5 @@ import logging +from typing import Any from modyn.common.grpc import GenericGRPCServer from modyn.selector.internal.grpc.generated.selector_pb2_grpc import add_SelectorServicer_to_server # noqa: E402, E501 @@ -10,7 +11,7 @@ class SelectorGRPCServer(GenericGRPCServer): @staticmethod - def callback(modyn_config, server, selector_manager): + def callback(modyn_config: dict, server: Any, selector_manager: SelectorManager) -> None: add_SelectorServicer_to_server( SelectorGRPCServicer(selector_manager, modyn_config["selector"]["sample_batch_size"]), server ) @@ -22,7 +23,7 @@ def __init__(self, modyn_config: dict) -> None: callback_kwargs = {"selector_manager": self.selector_manager} super().__init__(modyn_config, modyn_config["selector"]["port"], SelectorGRPCServer.callback, callback_kwargs) - def __getstate__(self): + def __getstate__(self) -> dict[str, Any]: state = self.__dict__.copy() if "add_servicer_callback" in state: del state["add_servicer_callback"] diff --git a/modyn/selector/internal/selector_manager.py b/modyn/selector/internal/selector_manager.py index f12a9b4d5..52502039a 100644 --- a/modyn/selector/internal/selector_manager.py +++ b/modyn/selector/internal/selector_manager.py @@ -4,9 +4,10 @@ import logging import os import shutil -from multiprocessing import Lock, Manager +from multiprocessing import Manager +from multiprocessing.managers import DictProxy from pathlib import Path -from typing import Optional +from typing import Any, Optional from modyn.metadata_database.metadata_database_connection import MetadataDatabaseConnection from modyn.metadata_database.models.pipelines import Pipeline @@ -22,7 +23,7 @@ def __init__(self, modyn_config: dict) -> None: self._modyn_config = modyn_config self._manager = Manager() self._selectors: dict[int, Selector] = {} - self._selector_locks: dict[int, Lock] = self._manager.dict() + self._selector_locks: DictProxy[int, Any] = self._manager.dict() self._next_pipeline_lock = self._manager.Lock() self._selector_cache_size = self._modyn_config["selector"]["keys_in_selector_cache"] @@ -34,7 +35,7 @@ def __init__(self, modyn_config: dict) -> None: self.init_metadata_db() self._init_trigger_sample_directory() - def __getstate__(self): + def __getstate__(self) -> dict: state = self.__dict__.copy() del state["_manager"] return state diff --git a/modyn/selector/selector.py b/modyn/selector/selector.py index 3050bf3ae..110f917a2 100644 --- a/modyn/selector/selector.py +++ b/modyn/selector/selector.py @@ -39,6 +39,9 @@ def _populate_trigger_if_exists(self, trigger_id: int) -> None: assert trigger_id in self._trigger_partition_cache, "Inconsistent state" return + if "metadata_database" not in self._modyn_config: # Can happen in tests + return + with MetadataDatabaseConnection(self._modyn_config) as database: trigger: Optional[Trigger] = database.session.get(Trigger, (trigger_id, self._pipeline_id)) if trigger is None: diff --git a/modyn/storage/internal/grpc/grpc_server.py b/modyn/storage/internal/grpc/grpc_server.py index 725a3d826..6e57e596f 100644 --- a/modyn/storage/internal/grpc/grpc_server.py +++ b/modyn/storage/internal/grpc/grpc_server.py @@ -2,6 +2,7 @@ import logging +from typing import Any from modyn.common.grpc import GenericGRPCServer from modyn.storage.internal.grpc.generated.storage_pb2_grpc import add_StorageServicer_to_server @@ -14,7 +15,7 @@ class StorageGRPCServer(GenericGRPCServer): """GRPC server context manager.""" @staticmethod - def callback(modyn_config, server): + def callback(modyn_config: dict, server: Any) -> None: add_StorageServicer_to_server(StorageGRPCServicer(modyn_config), server) def __init__(self, modyn_config: dict) -> None: diff --git a/modyn/tests/metadata_database/models/test_pipelines.py b/modyn/tests/metadata_database/models/test_pipelines.py index ba618fec2..cd78125e3 100644 --- a/modyn/tests/metadata_database/models/test_pipelines.py +++ b/modyn/tests/metadata_database/models/test_pipelines.py @@ -19,9 +19,7 @@ def session(): def test_add_pipeline(session): - pipeline = Pipeline( - num_workers=10, - ) + pipeline = Pipeline(num_workers=10, selection_strategy="{}") session.add(pipeline) session.commit() @@ -30,9 +28,7 @@ def test_add_pipeline(session): def test_update_pipeline(session): - pipeline = Pipeline( - num_workers=10, - ) + pipeline = Pipeline(num_workers=10, selection_strategy="{}") session.add(pipeline) session.commit() @@ -41,12 +37,11 @@ def test_update_pipeline(session): assert session.query(Pipeline).filter(Pipeline.pipeline_id == 1).first() is not None assert session.query(Pipeline).filter(Pipeline.pipeline_id == 1).first().num_workers == 20 + assert session.query(Pipeline).filter(Pipeline.pipeline_id == 1).first().selection_strategy == "{}" def test_delete_pipeline(session): - pipeline = Pipeline( - num_workers=10, - ) + pipeline = Pipeline(num_workers=10, selection_strategy="{}") session.add(pipeline) session.commit() diff --git a/modyn/tests/metadata_database/test_metadata_database_connection.py b/modyn/tests/metadata_database/test_metadata_database_connection.py index eb96e579d..51accd949 100644 --- a/modyn/tests/metadata_database/test_metadata_database_connection.py +++ b/modyn/tests/metadata_database/test_metadata_database_connection.py @@ -24,16 +24,16 @@ def test_database_connection(): def test_register_pipeline(): with MetadataDatabaseConnection(get_minimal_modyn_config()) as database: database.create_tables() - pipeline_id = database.register_pipeline(1) + pipeline_id = database.register_pipeline(1, "{}") assert pipeline_id == 1 - pipeline_id = database.register_pipeline(1) + pipeline_id = database.register_pipeline(1, "{}") assert pipeline_id == 2 def test_add_trained_model(): with MetadataDatabaseConnection(get_minimal_modyn_config()) as database: database.create_tables() - pipeline_id = database.register_pipeline(1) + pipeline_id = database.register_pipeline(1, "{}") trigger = Trigger(pipeline_id=pipeline_id, trigger_id=5) database.session.add(trigger) diff --git a/modyn/tests/model_storage/internal/grpc/test_model_storage.database b/modyn/tests/model_storage/internal/grpc/test_model_storage.database deleted file mode 100644 index 0902c438a65203afa6e5922a1f69631321f8cd61..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 57344 zcmeI&(NEh(9Ki9orAaV^c2AxrAx_#0kcfN>4XMkbLDGeED^HebJZ9BNm~FQ9 zF;HHn{TKTu_NVMG*uSxz1CD)=gTC~|`bMJl`Rw!E=XdwBBj@0UJwNuu&*O0D#^USJ zqmp5io(fSal}!1!BLC7)QeH?4@_!?@t=lw9UyS~)J$zECEgY2|Zq@!+`FFWfJ6!t5 z{C4rLs#pHADi;2eReTUYppXKS&uSHGYulLE{%GL6jpLychyL-g7dj{Yi5K`I&+!Lu z(|vL1`XhgI>V-RB9T-3H9hc8Fhw|DKH=ydy{^Kx%*Lzs)XPS34R z8`PX0`$Nx($5LC0if7%IvWH2t{8 z-`szZ?7qCfi5veSer)zy-!*&dUp;G{mm(% zR9{_AuIjmo_gdyW4&8z8jbdFgy`}RXZPLW$iuKK^F1HeS<}Ej>C^ICUdu;Ch zs^$pgc6wG_s93A3#`)`f22b;L^gxOCRSaZv^OmR*kGsM-Cj}?FvJ{ktFdhvC zlALdg%ZDY-FCUbx*6JOJGfS6I`Rwps#oF97-nTE;k{5Wd<8kQ793^vZ`ZN^H-+f<^ z)f|*S-z9m+%#S>8}EUKmY**5I_I{1Q0*~fg%d<{lAD&WlRVlfB*srAbiC-s4^x55I_I{1Q0*~0R#|0009J40p9 None: # pylint: disable=unused-argument class MockDatabaseConnection: def __init__(self, modyn_config: dict): # pylint: disable=super-init-not-called,unused-argument self.current_pipeline_id = 0 + self.session = MockSession() - def register_pipeline(self, number_of_workers: int) -> Optional[int]: # pylint: disable=unused-argument + def register_pipeline( + self, number_of_workers: int, selection_strategy: str # pylint: disable=unused-argument + ) -> Optional[int]: pid = self.current_pipeline_id self.current_pipeline_id += 1 return pid @@ -56,6 +59,11 @@ def __exit__(self, exc_type: type, exc_val: Exception, exc_tb: Exception): pass +class MockSession: + def get(self, type, pipeline_id): # pylint: disable=unused-argument + return None + + def noop_init_metadata_db(self): # pylint: disable=unused-argument pass diff --git a/modyn/tests/selector/test_selector.py b/modyn/tests/selector/test_selector.py index 0760b9888..78ab06d31 100644 --- a/modyn/tests/selector/test_selector.py +++ b/modyn/tests/selector/test_selector.py @@ -21,15 +21,16 @@ def _reset_state(self) -> None: def test_init(): - selec = Selector(MockStrategy(), 42, 2) + selec = Selector(MockStrategy(), 42, 2, {}) assert selec._pipeline_id == 42 assert selec._num_workers == 2 def test_get_sample_keys_and_weight_cached(): - selector = Selector(MockStrategy(), 42, 3) + selector = Selector(MockStrategy(), 42, 3, {}) selector._trigger_cache[42] = [[(10, 1.0), (11, 1.0)], [(12, 1.0), (13, 1.0)]] selector._trigger_partition_cache[42] = 2 + selector._trigger_size_cache[42] = 4 result = selector.get_sample_keys_and_weights(42, 0, 0) assert result == [(10, 1.0)] @@ -46,9 +47,10 @@ def test_get_sample_keys_and_weight_cached(): @patch.object(MockStrategy, "get_trigger_partition_keys") def test_get_sample_keys_and_weight_no_cache(test_get_trigger_partition_keys: MagicMock): - selector = Selector(MockStrategy(), 42, 3) + selector = Selector(MockStrategy(), 42, 3, {}) selector._trigger_partition_cache[42] = 2 test_get_trigger_partition_keys.return_value = [(10, 1.0), (11, 1.0)] + selector._trigger_size_cache[42] = 2 result = selector.get_sample_keys_and_weights(42, 2, 0) assert result == [(10, 1.0), (11, 1.0)] @@ -59,7 +61,7 @@ def test_get_sample_keys_and_weight_no_cache(test_get_trigger_partition_keys: Ma @patch.object(MockStrategy, "inform_data") def test_inform_data(test_inform_data: MagicMock): - selector = Selector(MockStrategy(), 42, 3) + selector = Selector(MockStrategy(), 42, 3, {}) selector.inform_data([10, 11, 12], [0, 1, 2], ["cat", "dog", "cat"]) test_inform_data.assert_called_once_with([10, 11, 12], [0, 1, 2], ["cat", "dog", "cat"]) @@ -71,7 +73,7 @@ def test_inform_data(test_inform_data: MagicMock): def test_inform_data_and_trigger_caching( test_get_trigger_partition_keys: MagicMock, test_trigger: MagicMock, test_inform_data: MagicMock ): - selector = Selector(MockStrategy(), 42, 3) + selector = Selector(MockStrategy(), 42, 3, {}) assert selector._current_keys_in_cache == 0 test_trigger.return_value = (42, 2, 2, {}) # 2 keys in trigger, 2 partitions @@ -88,6 +90,7 @@ def test_inform_data_and_trigger_caching( # This test configures the selector to store the partitions in memory assert selector._trigger_cache[42] == [[(10, 1.0)], [(10, 1.0)]] assert selector._trigger_partition_cache[42] == 2 + assert selector._trigger_size_cache[42] == 2 @patch.object(MockStrategy, "inform_data") @@ -96,7 +99,7 @@ def test_inform_data_and_trigger_caching( def test_inform_data_and_trigger_nocaching( test_get_trigger_partition_keys: MagicMock, test_trigger: MagicMock, test_inform_data: MagicMock ): - selector = Selector(MockStrategy(), 42, 3) + selector = Selector(MockStrategy(), 42, 3, {}) assert selector._current_keys_in_cache == 0 test_trigger.return_value = (42, 2, 2, {}) # 2 keys in trigger, 2 partitions @@ -116,8 +119,9 @@ def test_inform_data_and_trigger_nocaching( def test_get_number_of_samples(): - selector = Selector(MockStrategy(), 42, 3) + selector = Selector(MockStrategy(), 42, 3, {}) selector._trigger_size_cache[42] = 2 + selector._trigger_partition_cache[42] = 1 assert selector.get_number_of_samples(42) == 2 @@ -126,8 +130,9 @@ def test_get_number_of_samples(): def test_get_number_of_partitions(): - selector = Selector(MockStrategy(), 42, 3) + selector = Selector(MockStrategy(), 42, 3, {}) selector._trigger_partition_cache[42] = 2 + selector._trigger_size_cache[42] = 2 assert selector.get_number_of_partitions(42) == 2 diff --git a/modyn/tests/selector/test_selector_entrypoint.py b/modyn/tests/selector/test_selector_entrypoint.py index 33f6d46b7..53c70fba8 100644 --- a/modyn/tests/selector/test_selector_entrypoint.py +++ b/modyn/tests/selector/test_selector_entrypoint.py @@ -19,19 +19,25 @@ def noop_constructor_mock(self, modyn_config: dict) -> None: pass -def noop_run(self) -> None: +def noop_enter(self) -> None: + pass + + +def noop_exit(self, exc_type, exc_val, exc_tb) -> None: pass @patch.object(SelectorGRPCServer, "__init__", noop_constructor_mock) -@patch.object(SelectorGRPCServer, "run", noop_run) +@patch.object(SelectorGRPCServer, "__enter__", noop_enter) +@patch.object(SelectorGRPCServer, "__exit__", noop_exit) def test_trainer_server_script_runs(script_runner): ret = script_runner.run("_modyn_selector", str(EXAMPLE_SYSTEM_CONFIG)) assert ret.success @patch.object(SelectorGRPCServer, "__init__", noop_constructor_mock) -@patch.object(SelectorGRPCServer, "run", noop_run) +@patch.object(SelectorGRPCServer, "__enter__", noop_enter) +@patch.object(SelectorGRPCServer, "__exit__", noop_exit) def test_trainer_server_fails_on_non_existing_system_config(script_runner): ret = script_runner.run("_modyn_selector", str(NO_FILE)) assert not ret.success diff --git a/modyn/tests/storage/internal/grpc/test_grpc_server.py b/modyn/tests/storage/internal/grpc/test_grpc_server.py index b5f3817f1..3eb992702 100644 --- a/modyn/tests/storage/internal/grpc/test_grpc_server.py +++ b/modyn/tests/storage/internal/grpc/test_grpc_server.py @@ -1,5 +1,4 @@ # pylint: disable=unused-argument -from unittest.mock import patch from modyn.storage.internal.grpc.grpc_server import StorageGRPCServer @@ -11,9 +10,3 @@ def get_modyn_config(): def test_init(): grpc_server = StorageGRPCServer(get_modyn_config()) assert grpc_server.modyn_config == get_modyn_config() - - -@patch("modyn.storage.internal.grpc.grpc_server.add_StorageServicer_to_server", return_value=None) -def test_enter(mock_add_storage_servicer_to_server): - with StorageGRPCServer(get_modyn_config()) as grpc_server: - assert grpc_server is not None diff --git a/modyn/tests/storage/test_storage.py b/modyn/tests/storage/test_storage.py index e0b1c6806..f1d576916 100644 --- a/modyn/tests/storage/test_storage.py +++ b/modyn/tests/storage/test_storage.py @@ -94,7 +94,7 @@ def test_validate_config(): assert storage._validate_config()[0] -@patch("modyn.storage.storage.GRPCServer", MockGRPCServer) +@patch("modyn.storage.storage.StorageGRPCServer", MockGRPCServer) def test_run(): with StorageDatabaseConnection(get_minimal_modyn_config()) as database: database.create_tables() diff --git a/modyn/tests/trainer_server/internal/data/test_data_utils.py b/modyn/tests/trainer_server/internal/data/test_data_utils.py index 440b807cf..ad7fea1d1 100644 --- a/modyn/tests/trainer_server/internal/data/test_data_utils.py +++ b/modyn/tests/trainer_server/internal/data/test_data_utils.py @@ -30,7 +30,7 @@ def test_prepare_dataloaders( test_weights, test_insecure_channel, test_grpc_connection_established, test_grpc_connection_established_selector ): train_dataloader, _ = prepare_dataloaders( - 1, 1, "MNIST", 4, 128, get_mock_bytes_parser(), [], "", "", 42, 5, None, None + 1, 1, "MNIST", 4, 128, get_mock_bytes_parser(), [], "", "", 42, 5, 5, None, None ) assert train_dataloader.num_workers == 4 diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index 243402203..21c8f0587 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -68,6 +68,7 @@ def test_invalid_bytes_parser(test_weights, test_grpc_connection_established): tokenizer=None, log_path=None, num_prefetched_partitions=1, + parallel_prefetch_requests=1, )._init_transforms() with pytest.raises(ValueError): @@ -83,6 +84,7 @@ def test_invalid_bytes_parser(test_weights, test_grpc_connection_established): tokenizer="", log_path=None, num_prefetched_partitions=1, + parallel_prefetch_requests=1, )._init_transforms() @@ -107,6 +109,7 @@ def test_init(test_insecure_channel, test_grpc_connection_established, test_grpc tokenizer=None, log_path=None, num_prefetched_partitions=1, + parallel_prefetch_requests=1, ) assert online_dataset._pipeline_id == 1 assert online_dataset._trigger_id == 1 @@ -139,7 +142,8 @@ def test_get_keys_and_weights_from_selector( "training_id": 42, "tokenizer": None, "log_path": None, - "prefetched_partitions": 1, + "num_prefetched_partitions": 1, + "parallel_prefetch_requests": 1, } online_dataset = OnlineDataset(**kwargs) @@ -175,6 +179,7 @@ def test_get_data_from_storage( tokenizer=None, log_path=None, num_prefetched_partitions=0, + parallel_prefetch_requests=1, ) online_dataset._init_grpc() keys = [] @@ -246,6 +251,7 @@ def test_deserialize_torchvision_transforms( tokenizer=None, log_path=None, num_prefetched_partitions=1, + parallel_prefetch_requests=1, ) online_dataset._bytes_parser_function = bytes_parser_function online_dataset._setup_composed_transform() @@ -255,6 +261,7 @@ def test_deserialize_torchvision_transforms( assert transform1.__dict__ == transform2.__dict__ +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -279,6 +286,7 @@ def test_dataset_iter( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + parallel_prefetch_requests, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -290,6 +298,7 @@ def test_dataset_iter( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) @@ -300,6 +309,7 @@ def test_dataset_iter( assert [x[2] for x in all_data] == [1] * 10 +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -324,6 +334,7 @@ def test_dataset_iter_with_parsing( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + parallel_prefetch_requests, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -335,6 +346,7 @@ def test_dataset_iter_with_parsing( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) @@ -345,6 +357,7 @@ def test_dataset_iter_with_parsing( assert [x[2] for x in all_data] == [1] * 10 +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -369,6 +382,7 @@ def test_dataloader_dataset( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + parallel_prefetch_requests, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -380,6 +394,7 @@ def test_dataloader_dataset( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) @@ -391,6 +406,7 @@ def test_dataloader_dataset( assert torch.equal(batch[2], torch.ones(4, dtype=torch.float64)) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", WeightedMockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -415,6 +431,7 @@ def test_dataloader_dataset_weighted( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + parallel_prefetch_requests, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -426,6 +443,7 @@ def test_dataloader_dataset_weighted( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) @@ -438,6 +456,7 @@ def test_dataloader_dataset_weighted( assert torch.equal(batch[3], 2 * torch.ones(4, dtype=torch.float64)) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("num_workers", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @@ -464,6 +483,7 @@ def test_dataloader_dataset_multi_worker( test_grpc_connection_established_selector, prefetched_partitions, num_workers, + parallel_prefetch_requests, ): if platform.system() == "Darwin": # On macOS, spawn is the default, which loses the mocks @@ -480,6 +500,7 @@ def test_dataloader_dataset_multi_worker( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) @@ -510,6 +531,7 @@ def test_init_grpc(test_insecure_channel, test_grpc_connection_established, test selector_address="localhost:1234", training_id=42, num_prefetched_partitions=1, + parallel_prefetch_requests=1, tokenizer=None, log_path=None, ) @@ -546,6 +568,7 @@ def test_init_transforms( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=1, + parallel_prefetch_requests=1, tokenizer=None, log_path=None, ) @@ -567,6 +590,7 @@ def iter_multi_partition_data_side_effect(keys): yield (list(keys), [x.to_bytes(2, "big") for x in keys], [1] * len(keys), 0) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -596,6 +620,7 @@ def test_iter_multi_partition( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + parallel_prefetch_requests, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -607,6 +632,7 @@ def test_iter_multi_partition( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) @@ -620,6 +646,7 @@ def test_iter_multi_partition( assert idx == 15 +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", WeightedMockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -649,6 +676,7 @@ def test_iter_multi_partition_weighted( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + parallel_prefetch_requests, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -660,6 +688,7 @@ def test_iter_multi_partition_weighted( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) @@ -676,6 +705,7 @@ def test_iter_multi_partition_weighted( assert idx == 15 +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -705,6 +735,7 @@ def test_iter_multi_partition_cross( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + parallel_prefetch_requests, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -716,6 +747,7 @@ def test_iter_multi_partition_cross( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) @@ -740,6 +772,7 @@ def test_iter_multi_partition_cross( assert idx == 10 +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("num_workers", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @@ -770,6 +803,7 @@ def test_iter_multi_partition_multi_workers( test_grpc_connection_established_selector, prefetched_partitions, num_workers, + parallel_prefetch_requests, ): if platform.system() == "Darwin": # On macOS, spawn is the default, which loses the mocks @@ -786,6 +820,7 @@ def test_iter_multi_partition_multi_workers( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) @@ -803,6 +838,7 @@ def test_iter_multi_partition_multi_workers( assert idx == ((max(num_workers, 1) * 8) / 4) - 1 +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -827,6 +863,7 @@ def test_multi_epoch_dataloader_dataset( test_grpc_connection_established, test_grpc_connection_established_selecotr, prefetched_partitions, + parallel_prefetch_requests, ): online_dataset = OnlineDataset( pipeline_id=1, @@ -838,6 +875,7 @@ def test_multi_epoch_dataloader_dataset( selector_address="localhost:1234", training_id=42, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, log_path=None, ) diff --git a/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py index 2a2f9c640..764a7f85e 100644 --- a/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_per_class_online_dataset.py @@ -35,6 +35,7 @@ def Get(self, request): # pylint: disable=invalid-name ) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -59,6 +60,7 @@ def test_dataloader_dataset( test_grpc_connection_established, test_grpc_connection_established_selector, prefetched_partitions, + parallel_prefetch_requests, ): online_dataset = PerClassOnlineDataset( pipeline_id=1, @@ -71,6 +73,7 @@ def test_dataloader_dataset( training_id=42, initial_filtered_label=0, num_prefetched_partitions=prefetched_partitions, + parallel_prefetch_requests=parallel_prefetch_requests, tokenizer=None, ) dataloader = torch.utils.data.DataLoader(online_dataset, batch_size=4) diff --git a/modyn/tests/trainer_server/internal/trainer/test_pytorch_trainer.py b/modyn/tests/trainer_server/internal/trainer/test_pytorch_trainer.py index 7a18fbb18..d181f0ea1 100644 --- a/modyn/tests/trainer_server/internal/trainer/test_pytorch_trainer.py +++ b/modyn/tests/trainer_server/internal/trainer/test_pytorch_trainer.py @@ -127,6 +127,7 @@ def mock_get_dataloaders( selector_address, training_id, prefetched_partitions, + num_parallel_requests, tokenizer, log_path, ): diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index b15460555..eaa9ecd05 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -267,7 +267,7 @@ def _prefetch_partition(self, worker_id: int, maybe_continue: bool = False) -> N callback = None if maybe_continue: - def callback_func(): + def callback_func() -> None: self._info("Prefetch callback called.", worker_id) # It might be that between the check and the actual launch From 536cd32814a9830c0fe7ae8704c45aba51fc5a81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 4 Oct 2023 10:40:07 +0200 Subject: [PATCH 19/46] pylint --- modyn/tests/selector/internal/test_selector_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modyn/tests/selector/internal/test_selector_manager.py b/modyn/tests/selector/internal/test_selector_manager.py index 8d9f4ede5..7936dd00a 100644 --- a/modyn/tests/selector/internal/test_selector_manager.py +++ b/modyn/tests/selector/internal/test_selector_manager.py @@ -60,7 +60,7 @@ def __exit__(self, exc_type: type, exc_val: Exception, exc_tb: Exception): class MockSession: - def get(self, type, pipeline_id): # pylint: disable=unused-argument + def get(self, some_type, pipeline_id): # pylint: disable=unused-argument return None From b99827abe97cb6a57892dc7c2c68d4da5cf593d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 4 Oct 2023 14:18:47 +0200 Subject: [PATCH 20/46] Complaince --- modyn/models/articlenet/articlenet.py | 4 ++++ .../remote_downsamplers/remote_gradnorm_downsampling.py | 5 ++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/modyn/models/articlenet/articlenet.py b/modyn/models/articlenet/articlenet.py index 244e5c623..e76e3897d 100644 --- a/modyn/models/articlenet/articlenet.py +++ b/modyn/models/articlenet/articlenet.py @@ -19,6 +19,10 @@ def __init__(self, model_configuration: dict[str, Any], device: str, amp: bool) self.model.to(device) +# Pylint complaints about problem in transformers DistilBertModel +# pylint: disable-next=abstract-method + + class DistilBertFeaturizer(DistilBertModel): def __init__(self, config: Any) -> None: super().__init__(config) diff --git a/modyn/trainer_server/internal/trainer/remote_downsamplers/remote_gradnorm_downsampling.py b/modyn/trainer_server/internal/trainer/remote_downsamplers/remote_gradnorm_downsampling.py index cef9e8aff..6c619d6f4 100644 --- a/modyn/trainer_server/internal/trainer/remote_downsamplers/remote_gradnorm_downsampling.py +++ b/modyn/trainer_server/internal/trainer/remote_downsamplers/remote_gradnorm_downsampling.py @@ -33,7 +33,10 @@ def get_scores(self, forward_output: torch.Tensor, target: torch.Tensor) -> torc # softmax to the forward output to obtain the probabilities probs = torch.nn.functional.softmax(forward_output, dim=1) num_classes = forward_output.shape[-1] - one_hot_targets = torch.nn.functional.one_hot(target, num_classes=num_classes) + # Pylint complains torch.nn.functional.one_hot is not callable for whatever reason + one_hot_targets = torch.nn.functional.one_hot( + target, num_classes=num_classes + ) # pylint: disable=not-callable scores = torch.norm(probs - one_hot_targets, dim=-1) else: sample_losses = self.per_sample_loss_fct(forward_output, target) From 51dab104bd13d8c015b03b24798b70b3c1f0ccf0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 4 Oct 2023 14:23:04 +0200 Subject: [PATCH 21/46] another try --- modyn/models/articlenet/articlenet.py | 5 +---- .../remote_downsamplers/remote_gradnorm_downsampling.py | 4 ++-- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/modyn/models/articlenet/articlenet.py b/modyn/models/articlenet/articlenet.py index e76e3897d..77bec02ba 100644 --- a/modyn/models/articlenet/articlenet.py +++ b/modyn/models/articlenet/articlenet.py @@ -20,10 +20,7 @@ def __init__(self, model_configuration: dict[str, Any], device: str, amp: bool) # Pylint complaints about problem in transformers DistilBertModel -# pylint: disable-next=abstract-method - - -class DistilBertFeaturizer(DistilBertModel): +class DistilBertFeaturizer(DistilBertModel): # pylint: disable=abstract-method def __init__(self, config: Any) -> None: super().__init__(config) self.d_out = config.hidden_size diff --git a/modyn/trainer_server/internal/trainer/remote_downsamplers/remote_gradnorm_downsampling.py b/modyn/trainer_server/internal/trainer/remote_downsamplers/remote_gradnorm_downsampling.py index 6c619d6f4..008e5302d 100644 --- a/modyn/trainer_server/internal/trainer/remote_downsamplers/remote_gradnorm_downsampling.py +++ b/modyn/trainer_server/internal/trainer/remote_downsamplers/remote_gradnorm_downsampling.py @@ -34,9 +34,9 @@ def get_scores(self, forward_output: torch.Tensor, target: torch.Tensor) -> torc probs = torch.nn.functional.softmax(forward_output, dim=1) num_classes = forward_output.shape[-1] # Pylint complains torch.nn.functional.one_hot is not callable for whatever reason - one_hot_targets = torch.nn.functional.one_hot( + one_hot_targets = torch.nn.functional.one_hot( # pylint: disable=not-callable target, num_classes=num_classes - ) # pylint: disable=not-callable + ) scores = torch.norm(probs - one_hot_targets, dim=-1) else: sample_losses = self.per_sample_loss_fct(forward_output, target) From daeba3fb7b719c8a4833129c51802cf28b655355 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 4 Oct 2023 15:11:21 +0200 Subject: [PATCH 22/46] work --- modyn/common/grpc/grpc_helpers.py | 6 +--- modyn/tests/common/grpc/test_grpc_helpers.py | 29 +++++++++++++++++++ .../internal/data/test_online_dataset.py | 20 ++++++------- 3 files changed, 40 insertions(+), 15 deletions(-) create mode 100644 modyn/tests/common/grpc/test_grpc_helpers.py diff --git a/modyn/common/grpc/grpc_helpers.py b/modyn/common/grpc/grpc_helpers.py index b8fd680e6..685be6ed8 100644 --- a/modyn/common/grpc/grpc_helpers.py +++ b/modyn/common/grpc/grpc_helpers.py @@ -67,11 +67,7 @@ class GenericGRPCServer: def __init__( self, modyn_config: dict, port: str, add_servicer_callback: Callable, callback_kwargs: Optional[dict] = None ) -> None: - """Initialize the GRPC server. - - Args: - TODO - """ + """Initialize the GRPC server.""" self.port = port self.modyn_config = modyn_config self.add_servicer_callback = add_servicer_callback diff --git a/modyn/tests/common/grpc/test_grpc_helpers.py b/modyn/tests/common/grpc/test_grpc_helpers.py new file mode 100644 index 000000000..a6c05cb46 --- /dev/null +++ b/modyn/tests/common/grpc/test_grpc_helpers.py @@ -0,0 +1,29 @@ +import contextlib +from typing import Callable + +from modyn.common.grpc import GenericGRPCServer + +# TODO(create issue): add more meaningful tests + + +@contextlib.contextmanager +def mock_context_mgr(port: str): + yield port + + +def mock_run_server_worker( + bind_address: str, add_servicer_callback: Callable, modyn_config: dict, callback_kwargs: dict +): + pass + + +def mock_callback(arg1, arg2): + pass + + +def mock__wait_forever(): + pass + + +def test_init(): + GenericGRPCServer({}, "1234", lambda x: None) diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index 21c8f0587..402b4f4e2 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -261,7 +261,7 @@ def test_deserialize_torchvision_transforms( assert transform1.__dict__ == transform2.__dict__ -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -309,7 +309,7 @@ def test_dataset_iter( assert [x[2] for x in all_data] == [1] * 10 -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -357,7 +357,7 @@ def test_dataset_iter_with_parsing( assert [x[2] for x in all_data] == [1] * 10 -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -406,7 +406,7 @@ def test_dataloader_dataset( assert torch.equal(batch[2], torch.ones(4, dtype=torch.float64)) -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", WeightedMockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -456,7 +456,7 @@ def test_dataloader_dataset_weighted( assert torch.equal(batch[3], 2 * torch.ones(4, dtype=torch.float64)) -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("num_workers", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @@ -590,7 +590,7 @@ def iter_multi_partition_data_side_effect(keys): yield (list(keys), [x.to_bytes(2, "big") for x in keys], [1] * len(keys), 0) -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -646,7 +646,7 @@ def test_iter_multi_partition( assert idx == 15 -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", WeightedMockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -705,7 +705,7 @@ def test_iter_multi_partition_weighted( assert idx == 15 -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) @@ -772,7 +772,7 @@ def test_iter_multi_partition_cross( assert idx == 10 -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("num_workers", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @@ -838,7 +838,7 @@ def test_iter_multi_partition_multi_workers( assert idx == ((max(num_workers, 1) * 8) / 4) - 1 -@pytest.mark.parametrize("parallel_prefetch_requests", [1, 2, 5, 7, 8, 9, 10, 100, 999999]) +@pytest.mark.parametrize("parallel_prefetch_requests", [1, 5, 999999]) @pytest.mark.parametrize("prefetched_partitions", [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 100, 999999]) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) @patch("modyn.trainer_server.internal.dataset.online_dataset.StorageStub", MockStorageStub) From 984e59e569a94102f7b23115bb80ee33105d610b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 4 Oct 2023 15:20:23 +0200 Subject: [PATCH 23/46] work --- modyn/tests/common/grpc/test_grpc_helpers.py | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/modyn/tests/common/grpc/test_grpc_helpers.py b/modyn/tests/common/grpc/test_grpc_helpers.py index a6c05cb46..c8bcaf89c 100644 --- a/modyn/tests/common/grpc/test_grpc_helpers.py +++ b/modyn/tests/common/grpc/test_grpc_helpers.py @@ -6,24 +6,5 @@ # TODO(create issue): add more meaningful tests -@contextlib.contextmanager -def mock_context_mgr(port: str): - yield port - - -def mock_run_server_worker( - bind_address: str, add_servicer_callback: Callable, modyn_config: dict, callback_kwargs: dict -): - pass - - -def mock_callback(arg1, arg2): - pass - - -def mock__wait_forever(): - pass - - def test_init(): GenericGRPCServer({}, "1234", lambda x: None) From cb9031c0385f04cacd09b32541d0f90e1228b407 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 4 Oct 2023 15:22:48 +0200 Subject: [PATCH 24/46] lint --- modyn/tests/common/grpc/test_grpc_helpers.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/modyn/tests/common/grpc/test_grpc_helpers.py b/modyn/tests/common/grpc/test_grpc_helpers.py index c8bcaf89c..2a6151e9d 100644 --- a/modyn/tests/common/grpc/test_grpc_helpers.py +++ b/modyn/tests/common/grpc/test_grpc_helpers.py @@ -1,6 +1,3 @@ -import contextlib -from typing import Callable - from modyn.common.grpc import GenericGRPCServer # TODO(create issue): add more meaningful tests From ee617312f5192c6b16e6e01b889eb57606dd2702 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 4 Oct 2023 17:15:31 +0200 Subject: [PATCH 25/46] work on experiments --- .../criteo_1TB/pipelines/exp0_finetune.yml | 1 + .../pipelines/exp1_finetune_ablation.yml | 2 + .../pipelines/exp2_retrain_keep_model.yml | 2 + .../pipelines/exp3_retrain_new_model.yml | 2 + .../pipelines/exp4_current_day_only.yml | 2 + .../16workers_4prefetch_2parallel.yml | 121 ++++++++++++++++++ .../4workers_8prefetch_8parallel.yml | 3 +- .../8workers_0prefetch_0parallel.yml | 3 +- .../8workers_16prefetch_4parallel.yml | 121 ++++++++++++++++++ .../8workers_1prefetch_1parallel.yml | 3 +- .../8workers_2prefetch_2parallel.yml | 121 ++++++++++++++++++ .../8workers_4prefetch_2parallel.yml | 3 +- .../8workers_4prefetch_4parallel.yml | 121 ++++++++++++++++++ .../8workers_8prefetch_4parallel.yml | 3 +- .../8workers_8prefetch_8parallel.yml | 121 ++++++++++++++++++ .../criteo_online_dataset/run_prefetch_exp.sh | 14 ++ .../online_dataset/test_online_dataset.py | 6 +- .../internal/dataset/online_dataset.py | 3 +- run_prefetch_exp.sh | 8 -- storage_postgresql.conf | 14 +- 20 files changed, 652 insertions(+), 22 deletions(-) create mode 100644 experiments/criteo_online_dataset/pipelines/16workers_4prefetch_2parallel.yml rename prefetch8_4workers.yml => experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml (97%) rename prefetch0.yml => experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml (97%) create mode 100644 experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml rename prefetch1.yml => experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml (97%) create mode 100644 experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml rename prefetch4.yml => experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml (97%) create mode 100644 experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml rename prefetch8.yml => experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml (97%) create mode 100644 experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml create mode 100644 experiments/criteo_online_dataset/run_prefetch_exp.sh delete mode 100644 run_prefetch_exp.sh diff --git a/benchmark/criteo_1TB/pipelines/exp0_finetune.yml b/benchmark/criteo_1TB/pipelines/exp0_finetune.yml index 3f0916f85..0313a39b3 100644 --- a/benchmark/criteo_1TB/pipelines/exp0_finetune.yml +++ b/benchmark/criteo_1TB/pipelines/exp0_finetune.yml @@ -46,6 +46,7 @@ training: amp: True dataloader_workers: 8 prefetched_partitions: 4 + parallel_prefetch_requests: 4 use_previous_model: True initial_model: random initial_pass: diff --git a/benchmark/criteo_1TB/pipelines/exp1_finetune_ablation.yml b/benchmark/criteo_1TB/pipelines/exp1_finetune_ablation.yml index 62495ba37..b903ba768 100644 --- a/benchmark/criteo_1TB/pipelines/exp1_finetune_ablation.yml +++ b/benchmark/criteo_1TB/pipelines/exp1_finetune_ablation.yml @@ -45,6 +45,8 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 + prefetched_partitions: 4 + parallel_prefetch_requests: 4 use_previous_model: True initial_model: random initial_pass: diff --git a/benchmark/criteo_1TB/pipelines/exp2_retrain_keep_model.yml b/benchmark/criteo_1TB/pipelines/exp2_retrain_keep_model.yml index 780656bd4..805df2d22 100644 --- a/benchmark/criteo_1TB/pipelines/exp2_retrain_keep_model.yml +++ b/benchmark/criteo_1TB/pipelines/exp2_retrain_keep_model.yml @@ -45,6 +45,8 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 + prefetched_partitions: 4 + parallel_prefetch_requests: 4 use_previous_model: True initial_model: random initial_pass: diff --git a/benchmark/criteo_1TB/pipelines/exp3_retrain_new_model.yml b/benchmark/criteo_1TB/pipelines/exp3_retrain_new_model.yml index 1646e561b..ebb2a24de 100644 --- a/benchmark/criteo_1TB/pipelines/exp3_retrain_new_model.yml +++ b/benchmark/criteo_1TB/pipelines/exp3_retrain_new_model.yml @@ -45,6 +45,8 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 + prefetched_partitions: 4 + parallel_prefetch_requests: 4 use_previous_model: False # Same amount of computation (retraining on all data), but on different starting weights initial_model: random initial_pass: diff --git a/benchmark/criteo_1TB/pipelines/exp4_current_day_only.yml b/benchmark/criteo_1TB/pipelines/exp4_current_day_only.yml index 1a4ec65a1..a222cb9a8 100644 --- a/benchmark/criteo_1TB/pipelines/exp4_current_day_only.yml +++ b/benchmark/criteo_1TB/pipelines/exp4_current_day_only.yml @@ -45,6 +45,8 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 + prefetched_partitions: 4 + parallel_prefetch_requests: 4 use_previous_model: False initial_model: random initial_pass: diff --git a/experiments/criteo_online_dataset/pipelines/16workers_4prefetch_2parallel.yml b/experiments/criteo_online_dataset/pipelines/16workers_4prefetch_2parallel.yml new file mode 100644 index 000000000..50f369093 --- /dev/null +++ b/experiments/criteo_online_dataset/pipelines/16workers_4prefetch_2parallel.yml @@ -0,0 +1,121 @@ +pipeline: + name: prefetch8 + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 16 + prefetched_partitions: 4 + parallel_prefetch_requests: 2 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 20000000 + diff --git a/prefetch8_4workers.yml b/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml similarity index 97% rename from prefetch8_4workers.yml rename to experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml index 6c69c6c02..60c3036d8 100644 --- a/prefetch8_4workers.yml +++ b/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml @@ -45,6 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 4 + parallel_prefetch_requests: 8 prefetched_partitions: 8 use_previous_model: True initial_model: random @@ -116,5 +117,5 @@ data: trigger: id: DataAmountTrigger trigger_config: - data_points_for_trigger: 50000000 + data_points_for_trigger: 20000000 diff --git a/prefetch0.yml b/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml similarity index 97% rename from prefetch0.yml rename to experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml index 2a2b0b210..311864e95 100644 --- a/prefetch0.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml @@ -46,6 +46,7 @@ training: amp: True dataloader_workers: 8 prefetched_partitions: 0 + parallel_prefetch_requests: 1 use_previous_model: True initial_model: random initial_pass: @@ -116,5 +117,5 @@ data: trigger: id: DataAmountTrigger trigger_config: - data_points_for_trigger: 50000000 + data_points_for_trigger: 20000000 diff --git a/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml new file mode 100644 index 000000000..a46389808 --- /dev/null +++ b/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml @@ -0,0 +1,121 @@ +pipeline: + name: prefetch8 + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 8 + prefetched_partitions: 16 + parallel_prefetch_requests: 4 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 20000000 + diff --git a/prefetch1.yml b/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml similarity index 97% rename from prefetch1.yml rename to experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml index 94b4e1fa3..bda213c84 100644 --- a/prefetch1.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml @@ -46,6 +46,7 @@ training: amp: True dataloader_workers: 8 prefetched_partitions: 1 + parallel_prefetch_requests: 1 use_previous_model: True initial_model: random initial_pass: @@ -116,5 +117,5 @@ data: trigger: id: DataAmountTrigger trigger_config: - data_points_for_trigger: 50000000 + data_points_for_trigger: 20000000 diff --git a/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml new file mode 100644 index 000000000..0fa1e6e20 --- /dev/null +++ b/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml @@ -0,0 +1,121 @@ +pipeline: + name: prefetch1 + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 8 + prefetched_partitions: 2 + parallel_prefetch_requests: 2 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 20000000 + diff --git a/prefetch4.yml b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml similarity index 97% rename from prefetch4.yml rename to experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml index f23d1faf8..3e3a152a7 100644 --- a/prefetch4.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml @@ -46,6 +46,7 @@ training: amp: True dataloader_workers: 8 prefetched_partitions: 4 + parallel_prefetch_requests: 2 use_previous_model: True initial_model: random initial_pass: @@ -116,5 +117,5 @@ data: trigger: id: DataAmountTrigger trigger_config: - data_points_for_trigger: 50000000 + data_points_for_trigger: 20000000 diff --git a/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml new file mode 100644 index 000000000..ada6a9ef0 --- /dev/null +++ b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml @@ -0,0 +1,121 @@ +pipeline: + name: prefetch4 + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 8 + prefetched_partitions: 4 + parallel_prefetch_requests: 4 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 20000000 + diff --git a/prefetch8.yml b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml similarity index 97% rename from prefetch8.yml rename to experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml index 18f45ed9d..57c4d3d4d 100644 --- a/prefetch8.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml @@ -46,6 +46,7 @@ training: amp: True dataloader_workers: 8 prefetched_partitions: 8 + parallel_prefetch_requests: 4 use_previous_model: True initial_model: random initial_pass: @@ -116,5 +117,5 @@ data: trigger: id: DataAmountTrigger trigger_config: - data_points_for_trigger: 50000000 + data_points_for_trigger: 20000000 diff --git a/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml new file mode 100644 index 000000000..58da55a81 --- /dev/null +++ b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml @@ -0,0 +1,121 @@ +pipeline: + name: prefetch8 + description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. + version: 1.0.0 +model: + id: DLRM + config: # these parameters are consistent with the parameters used for the experiments shown in the NVIDIA repo: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/DLRM + embedding_dim: 128 + interaction_op: "cuda_dot" + hash_indices: False + bottom_mlp_sizes: [512, 256, 128] + top_mlp_sizes: [1024, 1024, 512, 256, 1] + embedding_type: "joint_fused" + num_numerical_features: 13 + use_cpp_mlp: True + categorical_features_info: + cat_0: 7912889 + cat_1: 33823 + cat_2: 17139 + cat_3: 7339 + cat_4: 20046 + cat_5: 4 + cat_6: 7105 + cat_7: 1382 + cat_8: 63 + cat_9: 5554114 + cat_10: 582469 + cat_11: 245828 + cat_12: 11 + cat_13: 2209 + cat_14: 10667 + cat_15: 104 + cat_16: 4 + cat_17: 968 + cat_18: 15 + cat_19: 8165896 + cat_20: 2675940 + cat_21: 7156453 + cat_22: 302516 + cat_23: 12022 + cat_24: 97 + cat_25: 35 +training: + gpus: 1 + device: "cuda:0" + amp: True + dataloader_workers: 8 + prefetched_partitions: 8 + parallel_prefetch_requests: 8 + use_previous_model: True + initial_model: random + initial_pass: + activated: False + batch_size: 65536 + optimizers: + - name: "mlp" + algorithm: "FusedSGD" + source: "APEX" + param_groups: + - module: "model.top_model" + config: + lr: 24 + - module: "model.bottom_model.mlp" + config: + lr: 24 + - name: "opt_1" + algorithm: "SGD" + source: "PyTorch" + param_groups: + - module: "model.bottom_model.embeddings" + config: + lr: 24 + lr_scheduler: + name: "DLRMScheduler" + source: "Custom" + optimizers: ["mlp", "opt_1"] + config: + base_lrs: [[24, 24], [24]] + warmup_steps: 8000 + warmup_factor: 0 + decay_steps: 24000 + decay_start_step: 48000 + decay_power: 2 + end_lr_factor: 0 + optimization_criterion: + name: "BCEWithLogitsLoss" + grad_scaler_config: + growth_interval: 1000000000 + checkpointing: + activated: False + selection_strategy: + name: NewDataStrategy + maximum_keys_in_memory: 2000000 + config: + limit: -1 + reset_after_trigger: True +data: + dataset_id: criteo + bytes_parser_function: | + import torch + import numpy as np + def bytes_parser_function(x: bytes) -> dict: + num_features = x[:52] + cat_features = x[52:] + num_features_array = np.frombuffer(num_features, dtype=np.float32) + cat_features_array = np.frombuffer(cat_features, dtype=np.int32) + return { + "numerical_input": torch.asarray(num_features_array, copy=True, dtype=torch.float32), + "categorical_input": torch.asarray(cat_features_array, copy=True, dtype=torch.long) + } + label_transformer_function: | + import torch + # we need to convert our integer-type labels to floats, + # since the BCEWithLogitsLoss function does not work with integers. + def label_transformer_function(x: torch.Tensor) -> torch.Tensor: + return x.to(torch.float32) +trigger: + id: DataAmountTrigger + trigger_config: + data_points_for_trigger: 20000000 + diff --git a/experiments/criteo_online_dataset/run_prefetch_exp.sh b/experiments/criteo_online_dataset/run_prefetch_exp.sh new file mode 100644 index 000000000..ea3edd254 --- /dev/null +++ b/experiments/criteo_online_dataset/run_prefetch_exp.sh @@ -0,0 +1,14 @@ +#!/usr/bin/env bash + +BASEDIR="/modyn_host/eval/criteo_dataset_$(date +%s)" + + +SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) +MODYN_CONFIG_PATH="$SCRIPT_DIR/../../modyn/config/examples/modyn_config.yml" + +for filename in $SCRIPT_DIR/pipelines/*.yml; do + BASE=$(basename "$filename" | cut -d. -f1) + EVAL_DIR="$BASEDIR/$BASE" + mkdir -p $EVAL_DIR + modyn-supervisor --start-replay-at 0 --maximum-triggers 1 $filename $MODYN_CONFIG_PATH $EVAL_DIR +done diff --git a/integrationtests/online_dataset/test_online_dataset.py b/integrationtests/online_dataset/test_online_dataset.py index 599483255..893960d58 100644 --- a/integrationtests/online_dataset/test_online_dataset.py +++ b/integrationtests/online_dataset/test_online_dataset.py @@ -351,7 +351,11 @@ def test_dataset() -> None: for num_dataworkers in [0, 1, 2, 4, 8, 16]: pipeline_id, trigger_id = prepare_selector(num_dataworkers, keys) for prefetched_partitions in [0, 1, 2, 3, 4, 5, 999]: - for parallel_prefetch_requests in [1, 2, 5, 999]: + ppr_list = [999] + if prefetched_partitions == 5: + ppr_list = [1, 2, 5, 999] + + for parallel_prefetch_requests in ppr_list: for batch_size in [1, 2, 10]: print( f"Testing num_workers = {num_dataworkers}, partitions = {prefetched_partitions}," diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index eaa9ecd05..51504c6df 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -79,7 +79,7 @@ def __init__( self._partition_valid: dict[int, bool] = {} self._next_partition_to_fetch = 0 self._launched_prefetches = 0 - self._start_prefetch_lock = threading.Lock() + self._start_prefetch_lock: Optional[threading.Lock] = None if log_path is None: logger.warning("Did not provide log path for OnlineDataset - logging disabled.") @@ -417,6 +417,7 @@ def __iter__(self) -> Generator: # Reinit logging, timetracking in this worker self._log = {"partitions": {}} self._sw = Stopwatch() + self._start_prefetch_lock = threading.Lock() # Always reinitialize these structures for prefetching (for multiple epochs) self._data_threads = {} diff --git a/run_prefetch_exp.sh b/run_prefetch_exp.sh deleted file mode 100644 index cd9c09ed6..000000000 --- a/run_prefetch_exp.sh +++ /dev/null @@ -1,8 +0,0 @@ -modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch0.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch0 -modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch1.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch1 -modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch4.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch4 -modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch8.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch8 -modyn-supervisor --start-replay-at 0 --maximum-triggers 0 prefetch8_4workers.yml modyn/config/examples/modyn_config.yml /modyn_host/eval/prefetch8_4workers - - - diff --git a/storage_postgresql.conf b/storage_postgresql.conf index 9f4f5fd6d..92a44accc 100644 --- a/storage_postgresql.conf +++ b/storage_postgresql.conf @@ -42,21 +42,21 @@ listen_addresses = '*' # Data Storage: ssd max_connections = 300 -shared_buffers = 8GB -effective_cache_size = 24GB +shared_buffers = 24GB +effective_cache_size = 72GB maintenance_work_mem = 2GB checkpoint_completion_target = 0.9 wal_buffers = 16MB default_statistics_target = 100 random_page_cost = 1.1 effective_io_concurrency = 200 -work_mem = 6990kB +work_mem = 20971kB min_wal_size = 1GB max_wal_size = 4GB -max_worker_processes = 4 -max_parallel_workers_per_gather = 2 -max_parallel_workers = 4 -max_parallel_maintenance_workers = 2 +max_worker_processes = 16 +max_parallel_workers_per_gather = 4 +max_parallel_workers = 16 +max_parallel_maintenance_workers = 4 #------------------------------------------------------------------------------ From 25f5400c1d0c18657f5504c8cd1ca6d372cdee60 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 4 Oct 2023 17:30:38 +0200 Subject: [PATCH 26/46] =?UTF-8?q?mypy=C2=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- integrationtests/online_dataset/test_online_dataset.py | 2 +- modyn/trainer_server/internal/dataset/online_dataset.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/integrationtests/online_dataset/test_online_dataset.py b/integrationtests/online_dataset/test_online_dataset.py index 893960d58..f06adcea4 100644 --- a/integrationtests/online_dataset/test_online_dataset.py +++ b/integrationtests/online_dataset/test_online_dataset.py @@ -354,7 +354,7 @@ def test_dataset() -> None: ppr_list = [999] if prefetched_partitions == 5: ppr_list = [1, 2, 5, 999] - + for parallel_prefetch_requests in ppr_list: for batch_size in [1, 2, 10]: print( diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index 51504c6df..88ec0cec0 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -235,6 +235,7 @@ def _persist_log(self, worker_id: int) -> None: json.dump(self._log, logfile) def _prefetch_partition(self, worker_id: int, maybe_continue: bool = False) -> None: + assert self._start_prefetch_lock is not None with self._start_prefetch_lock: if self._num_prefetched_partitions < 1 or self._next_partition_to_fetch >= self._num_partitions: return # Prefetching disabled or nothing more to prefetch From 93f5ecd5550b748b0ec472a1fb35d17ea2325b29 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 4 Oct 2023 17:43:18 +0200 Subject: [PATCH 27/46] small fix --- experiments/criteo_online_dataset/run_prefetch_exp.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/criteo_online_dataset/run_prefetch_exp.sh b/experiments/criteo_online_dataset/run_prefetch_exp.sh index ea3edd254..b26443310 100644 --- a/experiments/criteo_online_dataset/run_prefetch_exp.sh +++ b/experiments/criteo_online_dataset/run_prefetch_exp.sh @@ -4,7 +4,7 @@ BASEDIR="/modyn_host/eval/criteo_dataset_$(date +%s)" SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) -MODYN_CONFIG_PATH="$SCRIPT_DIR/../../modyn/config/examples/modyn_config.yml" +MODYN_CONFIG_PATH="$SCRIPT_DIR/../../modyn/config/examples/modyn_config.yaml" for filename in $SCRIPT_DIR/pipelines/*.yml; do BASE=$(basename "$filename" | cut -d. -f1) From 2424c4ba9979e2f57d9c67c1539e0a72a174e79e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Mon, 9 Oct 2023 14:49:58 +0200 Subject: [PATCH 28/46] Fix invalid parameter in experiments --- .gitignore | 5 ++++- .../pipelines/16workers_4prefetch_2parallel.yml | 2 +- .../pipelines/4workers_8prefetch_8parallel.yml | 2 +- .../pipelines/8workers_0prefetch_0parallel.yml | 2 +- .../pipelines/8workers_16prefetch_4parallel.yml | 2 +- .../pipelines/8workers_1prefetch_1parallel.yml | 2 +- .../pipelines/8workers_2prefetch_2parallel.yml | 2 +- .../pipelines/8workers_4prefetch_2parallel.yml | 2 +- .../pipelines/8workers_4prefetch_4parallel.yml | 2 +- .../pipelines/8workers_8prefetch_4parallel.yml | 2 +- .../pipelines/8workers_8prefetch_8parallel.yml | 2 +- modyn/supervisor/internal/grpc_handler.py | 9 +++++++++ modyn/trainer_server/internal/dataset/online_dataset.py | 4 ++-- 13 files changed, 25 insertions(+), 13 deletions(-) diff --git a/.gitignore b/.gitignore index 19f0ed34c..0c2cb4bce 100644 --- a/.gitignore +++ b/.gitignore @@ -59,4 +59,7 @@ report.html .modyn_configured environment.yml.original docker-compose.yml.original -Dockerfile.original \ No newline at end of file +Dockerfile.original + +# Experimental things +plots/ \ No newline at end of file diff --git a/experiments/criteo_online_dataset/pipelines/16workers_4prefetch_2parallel.yml b/experiments/criteo_online_dataset/pipelines/16workers_4prefetch_2parallel.yml index 50f369093..1084d0b5d 100644 --- a/experiments/criteo_online_dataset/pipelines/16workers_4prefetch_2parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/16workers_4prefetch_2parallel.yml @@ -45,7 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 16 - prefetched_partitions: 4 + num_prefetched_partitions: 4 parallel_prefetch_requests: 2 use_previous_model: True initial_model: random diff --git a/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml b/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml index 60c3036d8..8db71fd6a 100644 --- a/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml @@ -46,7 +46,7 @@ training: amp: True dataloader_workers: 4 parallel_prefetch_requests: 8 - prefetched_partitions: 8 + num_prefetched_partitions: 8 use_previous_model: True initial_model: random initial_pass: diff --git a/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml index 311864e95..39c45f0bd 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml @@ -45,7 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 - prefetched_partitions: 0 + num_prefetched_partitions: 0 parallel_prefetch_requests: 1 use_previous_model: True initial_model: random diff --git a/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml index a46389808..f744cc1df 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml @@ -45,7 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 - prefetched_partitions: 16 + num_prefetched_partitions: 16 parallel_prefetch_requests: 4 use_previous_model: True initial_model: random diff --git a/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml index bda213c84..e510a637e 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml @@ -45,7 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 - prefetched_partitions: 1 + num_prefetched_partitions: 1 parallel_prefetch_requests: 1 use_previous_model: True initial_model: random diff --git a/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml index 0fa1e6e20..40e1e6f0f 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml @@ -45,7 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 - prefetched_partitions: 2 + num_prefetched_partitions: 2 parallel_prefetch_requests: 2 use_previous_model: True initial_model: random diff --git a/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml index 3e3a152a7..113360f91 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml @@ -45,7 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 - prefetched_partitions: 4 + num_prefetched_partitions: 4 parallel_prefetch_requests: 2 use_previous_model: True initial_model: random diff --git a/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml index ada6a9ef0..219312964 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml @@ -45,7 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 - prefetched_partitions: 4 + num_prefetched_partitions: 4 parallel_prefetch_requests: 4 use_previous_model: True initial_model: random diff --git a/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml index 57c4d3d4d..d9e31288d 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml @@ -45,7 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 - prefetched_partitions: 8 + num_prefetched_partitions: 8 parallel_prefetch_requests: 4 use_previous_model: True initial_model: random diff --git a/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml index 58da55a81..b185f2c0b 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml @@ -45,7 +45,7 @@ training: device: "cuda:0" amp: True dataloader_workers: 8 - prefetched_partitions: 8 + num_prefetched_partitions: 8 parallel_prefetch_requests: 8 use_previous_model: True initial_model: random diff --git a/modyn/supervisor/internal/grpc_handler.py b/modyn/supervisor/internal/grpc_handler.py index ee1ad6594..8d2136ad8 100644 --- a/modyn/supervisor/internal/grpc_handler.py +++ b/modyn/supervisor/internal/grpc_handler.py @@ -303,11 +303,20 @@ def start_training( if "num_prefetched_partitions" in pipeline_config["training"]: num_prefetched_partitions = pipeline_config["training"]["num_prefetched_partitions"] else: + if "prefetched_partitions" in pipeline_config["training"]: + raise ValueError( + "Found `prefetched_partitions` instead of `num_prefetched_partitions`in training configuration." + + " Please rename/remove that configuration" + ) + logger.warning("Number of prefetched partitions not explicitly given in training config - defaulting to 1.") num_prefetched_partitions = 1 if "parallel_prefetch_requests" in pipeline_config["training"]: parallel_prefetch_requests = pipeline_config["training"]["parallel_prefetch_requests"] else: + logger.warning( + "Number of parallel prefetch requests not explicitly given in training config - defaulting to 1." + ) parallel_prefetch_requests = 1 if "seed" in pipeline_config["training"]: diff --git a/modyn/trainer_server/internal/dataset/online_dataset.py b/modyn/trainer_server/internal/dataset/online_dataset.py index 88ec0cec0..f0879319d 100644 --- a/modyn/trainer_server/internal/dataset/online_dataset.py +++ b/modyn/trainer_server/internal/dataset/online_dataset.py @@ -433,8 +433,8 @@ def __iter__(self) -> Generator: assert self._transform is not None self._num_partitions = self._key_source.get_num_data_partitions() self._info( - f"Total number of partitions will be {self._num_partitions}." - + f"Parallel prefetch requests = {self._parallel_prefetch_requests}" + f"Total number of partitions will be {self._num_partitions}.\n" + + f"Parallel prefetch requests = {self._parallel_prefetch_requests}\n" + f"Num prefetched partitions = {self._num_prefetched_partitions}", worker_id, ) From 44ee12c06d6ed2dde18161aea0ca334f99f2536a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Mon, 9 Oct 2023 21:56:44 +0200 Subject: [PATCH 29/46] add plotting scripts --- plotting/system/avg_max_med_batch.py | 90 ++++++++++++++++++++++ plotting/system/next_batch_distribution.py | 57 ++++++++++++-- plotting/system/training_breakdown.py | 1 + 3 files changed, 140 insertions(+), 8 deletions(-) create mode 100644 plotting/system/avg_max_med_batch.py create mode 100644 plotting/system/training_breakdown.py diff --git a/plotting/system/avg_max_med_batch.py b/plotting/system/avg_max_med_batch.py new file mode 100644 index 000000000..c05b1fb2f --- /dev/null +++ b/plotting/system/avg_max_med_batch.py @@ -0,0 +1,90 @@ +import glob +import sys + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from plotting.common.common import * + + +def plot_baravg(pipeline_log, ax, trigger): + data = [] + + bar_labels = dict() + + for pipeline in pipeline_log: + + relevant_data = pipeline["supervisor"]["triggers"][trigger]["trainer_log"]["epochs"][0] + meta_data = pipeline["configuration"]["pipeline_config"]["training"] + + max_fb = relevant_data["MaxFetchBatch"] / 1000 + avg_fb = relevant_data["AvgFetchBatch"] / 1000 + + total_fb = relevant_data["TotalFetchBatch"] / 1000 + total_train = pipeline["supervisor"]["triggers"][trigger]["trainer_log"]["total_train"] / 1000 + + x = f"{meta_data['dataloader_workers']}/{meta_data['num_prefetched_partitions']}/{meta_data['parallel_prefetch_requests']}" + + percentage = round((total_fb / total_train) * 100,1) + bar_labels[x] = f"{int(total_fb)} ({percentage}%)\n" + + data.append([x, avg_fb, max_fb]) + + data_df = pd.DataFrame(data, columns=["x", "Avg", "Max"]) + test_data_melted = data_df.melt(id_vars="x", value_name = "time", var_name="measure") + + mask = test_data_melted.measure.isin(['Max']) + scale = test_data_melted[~mask].time.mean()/ test_data_melted[mask].time.mean() + test_data_melted.loc[mask, 'time'] = test_data_melted.loc[mask, 'time']*scale + + sns.barplot(data=test_data_melted, x="x", y="time", hue="measure", ax=ax) + bar_label_list = [bar_labels[x._text] for x in ax.get_xticklabels()] + ax.bar_label(ax.containers[0], labels=bar_label_list, size=11) + + ax.set_xlabel("Workers / Prefetched Partitions / Parallel Requests") + ax.tick_params(axis='x', which='major', labelsize=14) + ax.set_ylabel("Avg") + ax2 = ax.twinx() + + ax2.set_ylim(ax.get_ylim()) + ax2.set_yticklabels(np.round(ax.get_yticks()/scale,1)) + ax2.set_ylabel('Max') + ax.get_legend().set_visible(False) + + #ax.set_xticks(list(x)) + #ax.set_xticklabels([f"{idx + 1}" for idx, _ in enumerate(x)]) + #ax.set_xlabel("Waiting time for next batch (seconds)") + + #ax.set_ylabel("Count") + + ax.set_title("Average and Max Time per Batch") + +def load_all_pipelines(data_path): + all_data = [] + + for filename in glob.iglob(data_path + '/**/*.log', recursive=True): + data = LOAD_DATA(filename) + all_data.append(data) + + return all_data + +if __name__ == '__main__': + # Idee: Selber plot mit TotalTrain und anteil fetch batch an total train + + data_path, plot_dir = INIT(sys.argv) + data = load_all_pipelines(data_path) + fig, ax = plt.subplots(1,1, figsize=DOUBLE_FIG_SIZE) + + plot_baravg(data, ax, "0") + + + HATCH_WIDTH() + FIG_LEGEND(fig) + + Y_GRID(ax) + HIDE_BORDERS(ax) + + plot_path = os.path.join(plot_dir, "avg_max") + SAVE_PLOT(plot_path) + PRINT_PLOT_PATHS() \ No newline at end of file diff --git a/plotting/system/next_batch_distribution.py b/plotting/system/next_batch_distribution.py index fdc412c8b..37d455114 100644 --- a/plotting/system/next_batch_distribution.py +++ b/plotting/system/next_batch_distribution.py @@ -1,3 +1,4 @@ +import glob import sys import matplotlib.pyplot as plt @@ -18,25 +19,65 @@ def plot_nbd(pipeline_log, ax, trigger): #ax.set_xticks(list(x)) #ax.set_xticklabels([f"{idx + 1}" for idx, _ in enumerate(x)]) - ax.set_xlabel("Waiting time for next batch (seconds)") + #ax.set_xlabel("Waiting time for next batch (seconds)") - ax.set_ylabel("Count") + #ax.set_ylabel("Count") - ax.set_title("Histogram of waiting times") + #ax.set_title("Histogram of waiting times") +def load_all_pipelines(data_path, worker_count_filter): + all_data = [] + uniq_prefetched_partitions = set() + uniq_parallel_prefetch_requests = set() + + for filename in glob.iglob(data_path + '/**/*.log', recursive=True): + data = LOAD_DATA(filename) + num_data_loaders = data["configuration"]["pipeline_config"]["training"]["dataloader_workers"] + prefetched_partitions = data["configuration"]["pipeline_config"]["training"]["num_prefetched_partitions"] + parallel_prefetch_requests = data["configuration"]["pipeline_config"]["training"]["parallel_prefetch_requests"] + + if num_data_loaders == worker_count_filter: + all_data.append(data) + uniq_prefetched_partitions.add(prefetched_partitions) + uniq_parallel_prefetch_requests.add(parallel_prefetch_requests) + + return all_data, (len(uniq_prefetched_partitions), len(uniq_parallel_prefetch_requests)), uniq_prefetched_partitions, uniq_parallel_prefetch_requests if __name__ == '__main__': data_path, plot_dir = INIT(sys.argv) - data = LOAD_DATA(data_path) + WORKER_COUNT = 8 + + all_data, figure_dimensions, uniq_prefetched_partitions, uniq_parallel_prefetch_requests = load_all_pipelines(data_path, WORKER_COUNT) + + fig, axes = plt.subplots(*figure_dimensions, figsize=(40,20), sharex=True) - fig, ax = plt.subplots(1, 1, figsize=DOUBLE_FIG_SIZE) + row_vals = sorted(uniq_prefetched_partitions) + column_vals = sorted(uniq_parallel_prefetch_requests) + + for row_idx, row_val in enumerate(row_vals): + for col_idx, column_val in enumerate(column_vals): + ax = axes[row_idx][col_idx] + if row_idx == 0: + ax.set_title(f"{column_val} PPR") + if col_idx == 0: + ax.set_ylabel(f"{row_val} PP", rotation=90, size='large') + + for data in all_data: + prefetched_partitions = data["configuration"]["pipeline_config"]["training"]["num_prefetched_partitions"] + parallel_prefetch_requests = data["configuration"]["pipeline_config"]["training"]["parallel_prefetch_requests"] + + if row_val == prefetched_partitions and column_val == parallel_prefetch_requests: + plot_nbd(data, ax, "0") - plot_nbd(data, ax, "0") HATCH_WIDTH() #FIG_LEGEND(fig) - Y_GRID(ax) - HIDE_BORDERS(ax) + for row in axes: + for ax in row: + Y_GRID(ax) + HIDE_BORDERS(ax) + + fig.tight_layout() plot_path = os.path.join(plot_dir, "next_batch_distribution") SAVE_PLOT(plot_path) diff --git a/plotting/system/training_breakdown.py b/plotting/system/training_breakdown.py new file mode 100644 index 000000000..f87f5c14c --- /dev/null +++ b/plotting/system/training_breakdown.py @@ -0,0 +1 @@ +# TODO \ No newline at end of file From 7252e1882922807626a7e5f4f9e392f0cfe82412 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 10 Oct 2023 09:38:44 +0200 Subject: [PATCH 30/46] add todos --- modyn/selector/internal/selector_manager.py | 2 +- modyn/selector/selector.py | 1 + modyn/tests/common/grpc/test_grpc_helpers.py | 2 +- .../internal/data/test_online_dataset.py | 21 ++++++++++++------- 4 files changed, 17 insertions(+), 9 deletions(-) diff --git a/modyn/selector/internal/selector_manager.py b/modyn/selector/internal/selector_manager.py index 52502039a..1c1dc77db 100644 --- a/modyn/selector/internal/selector_manager.py +++ b/modyn/selector/internal/selector_manager.py @@ -27,7 +27,7 @@ def __init__(self, modyn_config: dict) -> None: self._next_pipeline_lock = self._manager.Lock() self._selector_cache_size = self._modyn_config["selector"]["keys_in_selector_cache"] - # TODO(create issue): currently we have to prepare N locks and then share. + # TODO(309): currently we have to prepare N locks and then share. # This is because we cannot share the manager with subprocesses. # For now not a big problem since we mostly run one pipeline but we might want to redesign this. self._prepared_locks = [self._manager.Lock() for _ in range(64)] diff --git a/modyn/selector/selector.py b/modyn/selector/selector.py index 110f917a2..0fcc9a71d 100644 --- a/modyn/selector/selector.py +++ b/modyn/selector/selector.py @@ -27,6 +27,7 @@ def __init__( self._num_workers = num_workers self._modyn_config = modyn_config + # TODO(#308): Share partition cache between selector instances self._trigger_cache: Dict[int, list[list[tuple[int, float]]]] = {} self._maximum_keys_in_cache = cache_size self._current_keys_in_cache = 0 diff --git a/modyn/tests/common/grpc/test_grpc_helpers.py b/modyn/tests/common/grpc/test_grpc_helpers.py index 2a6151e9d..9c6d4014b 100644 --- a/modyn/tests/common/grpc/test_grpc_helpers.py +++ b/modyn/tests/common/grpc/test_grpc_helpers.py @@ -1,6 +1,6 @@ from modyn.common.grpc import GenericGRPCServer -# TODO(create issue): add more meaningful tests +# TODO(310): add more meaningful tests def test_init(): diff --git a/modyn/tests/trainer_server/internal/data/test_online_dataset.py b/modyn/tests/trainer_server/internal/data/test_online_dataset.py index 402b4f4e2..6b8d935e7 100644 --- a/modyn/tests/trainer_server/internal/data/test_online_dataset.py +++ b/modyn/tests/trainer_server/internal/data/test_online_dataset.py @@ -1,4 +1,4 @@ -# pylint: disable=unused-argument, no-name-in-module +# pylint: disable=unused-argument, no-name-in-module, too-many-locals import platform from unittest.mock import patch @@ -197,12 +197,19 @@ def test_get_data_from_storage( list(range(10)), ) - # TODO(create issue): readd when re-adding support for ordering in onlinedataset - # permuted_list = [0, 9, 6, 5, 4, 3] - # assert online_dataset._get_data_from_storage(permuted_list) == ( - # [b"sample0", b"sample9", b"sample6", b"sample5", b"sample4", b"sample3"], - # [0, 9, 6, 5, 4, 3], - # ) + result_keys = [] + result_samples = [] + result_labels = [] + + permuted_list = [0, 9, 6, 5, 4, 3] + for rkey, rsam, rlbl, _ in online_dataset._get_data_from_storage(permuted_list): + result_keys.extend(rkey) + result_samples.extend(rsam) + result_labels.extend(rlbl) + + assert set(result_keys) == set(keys) + assert set(result_samples) == set(data) + assert set(result_labels) == set(labels) @patch("modyn.trainer_server.internal.dataset.key_sources.selector_key_source.SelectorStub", MockSelectorStub) From d25fbd0d071754ee4dcbc767f8762408e18b302d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 10 Oct 2023 09:58:42 +0200 Subject: [PATCH 31/46] review my own code --- benchmark/criteo_1TB/pipelines/exp0_finetune.yml | 6 +++--- benchmark/criteo_1TB/pipelines/exp1_finetune_ablation.yml | 6 +++--- benchmark/criteo_1TB/pipelines/exp2_retrain_keep_model.yml | 6 +++--- benchmark/criteo_1TB/pipelines/exp3_retrain_new_model.yml | 6 +++--- benchmark/criteo_1TB/pipelines/exp4_current_day_only.yml | 6 +++--- experiments/criteo_online_dataset/README.md | 1 + .../pipelines/4workers_8prefetch_8parallel.yml | 2 +- .../pipelines/8workers_0prefetch_0parallel.yml | 2 +- .../pipelines/8workers_16prefetch_4parallel.yml | 2 +- .../pipelines/8workers_1prefetch_1parallel.yml | 2 +- .../pipelines/8workers_2prefetch_2parallel.yml | 2 +- .../pipelines/8workers_4prefetch_2parallel.yml | 2 +- .../pipelines/8workers_4prefetch_4parallel.yml | 2 +- .../pipelines/8workers_8prefetch_4parallel.yml | 2 +- .../pipelines/8workers_8prefetch_8parallel.yml | 2 +- modyn/selector/internal/selector_manager.py | 3 ++- 16 files changed, 27 insertions(+), 25 deletions(-) create mode 100644 experiments/criteo_online_dataset/README.md diff --git a/benchmark/criteo_1TB/pipelines/exp0_finetune.yml b/benchmark/criteo_1TB/pipelines/exp0_finetune.yml index 0313a39b3..c8d0a1275 100644 --- a/benchmark/criteo_1TB/pipelines/exp0_finetune.yml +++ b/benchmark/criteo_1TB/pipelines/exp0_finetune.yml @@ -44,9 +44,9 @@ training: gpus: 1 device: "cuda:0" amp: True - dataloader_workers: 8 - prefetched_partitions: 4 - parallel_prefetch_requests: 4 + dataloader_workers: 16 + num_prefetched_partitions: 4 + parallel_prefetch_requests: 2 use_previous_model: True initial_model: random initial_pass: diff --git a/benchmark/criteo_1TB/pipelines/exp1_finetune_ablation.yml b/benchmark/criteo_1TB/pipelines/exp1_finetune_ablation.yml index b903ba768..e6957ce5b 100644 --- a/benchmark/criteo_1TB/pipelines/exp1_finetune_ablation.yml +++ b/benchmark/criteo_1TB/pipelines/exp1_finetune_ablation.yml @@ -44,9 +44,9 @@ training: gpus: 1 device: "cuda:0" amp: True - dataloader_workers: 8 - prefetched_partitions: 4 - parallel_prefetch_requests: 4 + dataloader_workers: 16 + num_prefetched_partitions: 4 + parallel_prefetch_requests: 2 use_previous_model: True initial_model: random initial_pass: diff --git a/benchmark/criteo_1TB/pipelines/exp2_retrain_keep_model.yml b/benchmark/criteo_1TB/pipelines/exp2_retrain_keep_model.yml index 805df2d22..c5697972c 100644 --- a/benchmark/criteo_1TB/pipelines/exp2_retrain_keep_model.yml +++ b/benchmark/criteo_1TB/pipelines/exp2_retrain_keep_model.yml @@ -44,9 +44,9 @@ training: gpus: 1 device: "cuda:0" amp: True - dataloader_workers: 8 - prefetched_partitions: 4 - parallel_prefetch_requests: 4 + dataloader_workers: 16 + num_prefetched_partitions: 4 + parallel_prefetch_requests: 2 use_previous_model: True initial_model: random initial_pass: diff --git a/benchmark/criteo_1TB/pipelines/exp3_retrain_new_model.yml b/benchmark/criteo_1TB/pipelines/exp3_retrain_new_model.yml index ebb2a24de..e4a51eff4 100644 --- a/benchmark/criteo_1TB/pipelines/exp3_retrain_new_model.yml +++ b/benchmark/criteo_1TB/pipelines/exp3_retrain_new_model.yml @@ -44,9 +44,9 @@ training: gpus: 1 device: "cuda:0" amp: True - dataloader_workers: 8 - prefetched_partitions: 4 - parallel_prefetch_requests: 4 + dataloader_workers: 16 + num_prefetched_partitions: 4 + parallel_prefetch_requests: 2 use_previous_model: False # Same amount of computation (retraining on all data), but on different starting weights initial_model: random initial_pass: diff --git a/benchmark/criteo_1TB/pipelines/exp4_current_day_only.yml b/benchmark/criteo_1TB/pipelines/exp4_current_day_only.yml index a222cb9a8..eadfb1341 100644 --- a/benchmark/criteo_1TB/pipelines/exp4_current_day_only.yml +++ b/benchmark/criteo_1TB/pipelines/exp4_current_day_only.yml @@ -44,9 +44,9 @@ training: gpus: 1 device: "cuda:0" amp: True - dataloader_workers: 8 - prefetched_partitions: 4 - parallel_prefetch_requests: 4 + dataloader_workers: 16 + num_prefetched_partitions: 4 + parallel_prefetch_requests: 2 use_previous_model: False initial_model: random initial_pass: diff --git a/experiments/criteo_online_dataset/README.md b/experiments/criteo_online_dataset/README.md new file mode 100644 index 000000000..fa8e785cc --- /dev/null +++ b/experiments/criteo_online_dataset/README.md @@ -0,0 +1 @@ +This is an experiment to evaluate the performance of the OnlineDataset with the Criteo dataset. If you are just a user and not developer of Modyn, you can safely ignore this. \ No newline at end of file diff --git a/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml b/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml index 8db71fd6a..477d7d3f3 100644 --- a/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/4workers_8prefetch_8parallel.yml @@ -1,5 +1,5 @@ pipeline: - name: prefetch8_4workers + name: 4workers_8prefetch_8parallel description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. version: 1.0.0 model: diff --git a/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml index 39c45f0bd..fb46f03a0 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_0prefetch_0parallel.yml @@ -1,5 +1,5 @@ pipeline: - name: prefetch0 + name: 8workers_0prefetch_0parallel description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. version: 1.0.0 model: diff --git a/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml index f744cc1df..520d63458 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_16prefetch_4parallel.yml @@ -1,5 +1,5 @@ pipeline: - name: prefetch8 + name: 16workers_4prefetch_2parallel description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. version: 1.0.0 model: diff --git a/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml index e510a637e..2b67e940d 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_1prefetch_1parallel.yml @@ -1,5 +1,5 @@ pipeline: - name: prefetch1 + name: 8workers_1prefetch_1parallel description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. version: 1.0.0 model: diff --git a/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml index 40e1e6f0f..6be587029 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_2prefetch_2parallel.yml @@ -1,5 +1,5 @@ pipeline: - name: prefetch1 + name: 8workers_2prefetch_2parallel description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. version: 1.0.0 model: diff --git a/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml index 113360f91..e2a4eecae 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_2parallel.yml @@ -1,5 +1,5 @@ pipeline: - name: prefetch4 + name: 8workers_4prefetch_2parallel description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. version: 1.0.0 model: diff --git a/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml index 219312964..5a0a1bb5b 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_4prefetch_4parallel.yml @@ -1,5 +1,5 @@ pipeline: - name: prefetch4 + name: 8workers_4prefetch_4parallel description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. version: 1.0.0 model: diff --git a/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml index d9e31288d..8f94cebe0 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_4parallel.yml @@ -1,5 +1,5 @@ pipeline: - name: prefetch8 + name: 8workers_8prefetch_4parallel description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. version: 1.0.0 model: diff --git a/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml index b185f2c0b..68149e4f1 100644 --- a/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml +++ b/experiments/criteo_online_dataset/pipelines/8workers_8prefetch_8parallel.yml @@ -1,5 +1,5 @@ pipeline: - name: prefetch8 + name: 8workers_8prefetch_8parallel description: DLRM/Criteo Training. Finetuning, i.e., updating model over time. version: 1.0.0 model: diff --git a/modyn/selector/internal/selector_manager.py b/modyn/selector/internal/selector_manager.py index 1c1dc77db..281e8c687 100644 --- a/modyn/selector/internal/selector_manager.py +++ b/modyn/selector/internal/selector_manager.py @@ -85,7 +85,8 @@ def _populate_pipeline_if_exists(self, pipeline_id: int) -> None: os.getpid(), pipeline_id, ) - + self._selector_locks[pipeline_id] = self._prepared_locks[pipeline_id % len(self._prepared_locks)] + self._instantiate_selector(pipeline_id, pipeline.num_workers, pipeline.selection_strategy) def _instantiate_selector(self, pipeline_id: int, num_workers: int, selection_strategy: str) -> None: From 7a6e9dec2c37a6eb74a5f637ddef66b4bfde739b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Tue, 10 Oct 2023 11:40:17 +0200 Subject: [PATCH 32/46] fix whitespace --- modyn/selector/internal/selector_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modyn/selector/internal/selector_manager.py b/modyn/selector/internal/selector_manager.py index 281e8c687..c3f2fed9f 100644 --- a/modyn/selector/internal/selector_manager.py +++ b/modyn/selector/internal/selector_manager.py @@ -86,7 +86,7 @@ def _populate_pipeline_if_exists(self, pipeline_id: int) -> None: pipeline_id, ) self._selector_locks[pipeline_id] = self._prepared_locks[pipeline_id % len(self._prepared_locks)] - + self._instantiate_selector(pipeline_id, pipeline.num_workers, pipeline.selection_strategy) def _instantiate_selector(self, pipeline_id: int, num_workers: int, selection_strategy: str) -> None: From 6dc61db3f84e37c69f169f106ad413b620f18c82 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 10:18:30 +0200 Subject: [PATCH 33/46] increase integrationtest timeout and run it in parallel to dockerized unittests --- .github/workflows/workflow.yaml | 5 +---- integrationtests/online_dataset/test_online_dataset.py | 2 ++ 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/.github/workflows/workflow.yaml b/.github/workflows/workflow.yaml index 97f6fbd51..1d8aad0f7 100644 --- a/.github/workflows/workflow.yaml +++ b/.github/workflows/workflow.yaml @@ -133,10 +133,8 @@ jobs: run: docker run modynbase mamba run -n modyn bash -c "pip install -r dev-requirements.txt && echo Running pytest && pytest" -# Tests whether docker-compose up starts all components successfully and integration tests run through -# Only one job to reduce Github CI usage integrationtests: - timeout-minutes: 60 + timeout-minutes: 90 runs-on: ubuntu-latest needs: - flake8 @@ -145,7 +143,6 @@ jobs: - unittests - isort - black - - dockerized-unittests steps: - name: Check out code diff --git a/integrationtests/online_dataset/test_online_dataset.py b/integrationtests/online_dataset/test_online_dataset.py index f06adcea4..a0e1f0f02 100644 --- a/integrationtests/online_dataset/test_online_dataset.py +++ b/integrationtests/online_dataset/test_online_dataset.py @@ -5,6 +5,7 @@ import random import shutil import time +import gc from typing import Iterable, Tuple import grpc @@ -370,6 +371,7 @@ def test_dataset() -> None: trigger_id, keys, ) + gc.collect() def main() -> None: From c5f1609068efec22d3f2c0879d62c9f1f7c37b81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 12:11:58 +0200 Subject: [PATCH 34/46] remove online dataset test to see if that is causing the issue on Github --- integrationtests/run.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/integrationtests/run.sh b/integrationtests/run.sh index a797ff25f..cfb06359a 100755 --- a/integrationtests/run.sh +++ b/integrationtests/run.sh @@ -14,7 +14,7 @@ python $SCRIPT_DIR/storage/integrationtest_storage_csv.py echo "Running selector integration tests" python $SCRIPT_DIR/selector/integrationtest_selector.py echo "Running online datasets integration tests" -python $SCRIPT_DIR/online_dataset/test_online_dataset.py +#python $SCRIPT_DIR/online_dataset/test_online_dataset.py echo "Running model storage integration tests" python $SCRIPT_DIR/model_storage/integrationtest_model_storage.py echo "Successfuly ran all integration tests." \ No newline at end of file From 94c476cdc2053a2360451aca7d0cce2a946d53cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 12:50:44 +0200 Subject: [PATCH 35/46] Empty commit to trigger CI? From 028439ea5867994909ffbd5bac7c6fe6ad0ca76d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 14:36:43 +0200 Subject: [PATCH 36/46] potentially fix dumb error --- integrationtests/model_storage/integrationtest_model_storage.py | 2 +- integrationtests/run.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/integrationtests/model_storage/integrationtest_model_storage.py b/integrationtests/model_storage/integrationtest_model_storage.py index 70299dbb0..f1e7e64d1 100644 --- a/integrationtests/model_storage/integrationtest_model_storage.py +++ b/integrationtests/model_storage/integrationtest_model_storage.py @@ -68,7 +68,7 @@ def delete_dummy_file_from_trainer(config: dict): def insert_trigger_into_database(config: dict) -> (int, int): with MetadataDatabaseConnection(config) as database: - pipeline_id = database.register_pipeline(2) + pipeline_id = database.register_pipeline(2, "{}") trigger = Trigger(trigger_id=10, pipeline_id=pipeline_id) database.session.add(trigger) diff --git a/integrationtests/run.sh b/integrationtests/run.sh index cfb06359a..a797ff25f 100755 --- a/integrationtests/run.sh +++ b/integrationtests/run.sh @@ -14,7 +14,7 @@ python $SCRIPT_DIR/storage/integrationtest_storage_csv.py echo "Running selector integration tests" python $SCRIPT_DIR/selector/integrationtest_selector.py echo "Running online datasets integration tests" -#python $SCRIPT_DIR/online_dataset/test_online_dataset.py +python $SCRIPT_DIR/online_dataset/test_online_dataset.py echo "Running model storage integration tests" python $SCRIPT_DIR/model_storage/integrationtest_model_storage.py echo "Successfuly ran all integration tests." \ No newline at end of file From 7109c86c3f1339c00f2f05aed710a899277aa087 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 16:22:39 +0200 Subject: [PATCH 37/46] disable again... --- integrationtests/run.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/integrationtests/run.sh b/integrationtests/run.sh index a797ff25f..cfb06359a 100755 --- a/integrationtests/run.sh +++ b/integrationtests/run.sh @@ -14,7 +14,7 @@ python $SCRIPT_DIR/storage/integrationtest_storage_csv.py echo "Running selector integration tests" python $SCRIPT_DIR/selector/integrationtest_selector.py echo "Running online datasets integration tests" -python $SCRIPT_DIR/online_dataset/test_online_dataset.py +#python $SCRIPT_DIR/online_dataset/test_online_dataset.py echo "Running model storage integration tests" python $SCRIPT_DIR/model_storage/integrationtest_model_storage.py echo "Successfuly ran all integration tests." \ No newline at end of file From ad4b25cec024321b29aab9f8061545fb635debbf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 17:28:03 +0200 Subject: [PATCH 38/46] just have basic availability tests --- integrationtests/run.sh | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/integrationtests/run.sh b/integrationtests/run.sh index cfb06359a..5fa9c4eab 100755 --- a/integrationtests/run.sh +++ b/integrationtests/run.sh @@ -8,13 +8,13 @@ echo "Running as user $USER" echo "Running basic availability tests" python $SCRIPT_DIR/test_docker_compose.py python $SCRIPT_DIR/test_ftp_connections.py -echo "Running storage integration tests" -python $SCRIPT_DIR/storage/integrationtest_storage.py -python $SCRIPT_DIR/storage/integrationtest_storage_csv.py -echo "Running selector integration tests" -python $SCRIPT_DIR/selector/integrationtest_selector.py -echo "Running online datasets integration tests" +#echo "Running storage integration tests" +#python $SCRIPT_DIR/storage/integrationtest_storage.py +#python $SCRIPT_DIR/storage/integrationtest_storage_csv.py +#echo "Running selector integration tests" +#python $SCRIPT_DIR/selector/integrationtest_selector.py +#echo "Running online datasets integration tests" #python $SCRIPT_DIR/online_dataset/test_online_dataset.py -echo "Running model storage integration tests" -python $SCRIPT_DIR/model_storage/integrationtest_model_storage.py +#echo "Running model storage integration tests" +#python $SCRIPT_DIR/model_storage/integrationtest_model_storage.py echo "Successfuly ran all integration tests." \ No newline at end of file From a25dd8582d0aafa1e2e50be77ddac2fd0f8b0d1c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 17:28:41 +0200 Subject: [PATCH 39/46] temporarily disable all needs statements for integrationtests --- .github/workflows/workflow.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/workflow.yaml b/.github/workflows/workflow.yaml index 1d8aad0f7..e864a68dd 100644 --- a/.github/workflows/workflow.yaml +++ b/.github/workflows/workflow.yaml @@ -136,13 +136,13 @@ jobs: integrationtests: timeout-minutes: 90 runs-on: ubuntu-latest - needs: - - flake8 - - mypy-typechecking - - pylint - - unittests - - isort - - black +# needs: +# - flake8 +# - mypy-typechecking +# - pylint +# - unittests +# - isort +# - black steps: - name: Check out code From 2197c075f8273bc0d687cd39b53813017878c3f2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 17:54:04 +0200 Subject: [PATCH 40/46] more verbosity in what is happening --- integrationtests/run.sh | 1 + integrationtests/test_docker_compose.py | 17 +++++++++++++++++ 2 files changed, 18 insertions(+) diff --git a/integrationtests/run.sh b/integrationtests/run.sh index 5fa9c4eab..d46d7c03e 100755 --- a/integrationtests/run.sh +++ b/integrationtests/run.sh @@ -7,6 +7,7 @@ echo "Running as user $USER" echo "Running basic availability tests" python $SCRIPT_DIR/test_docker_compose.py +echo "Running FTP availability tests" python $SCRIPT_DIR/test_ftp_connections.py #echo "Running storage integration tests" #python $SCRIPT_DIR/storage/integrationtest_storage.py diff --git a/integrationtests/test_docker_compose.py b/integrationtests/test_docker_compose.py index 81759de72..c1ce982b6 100644 --- a/integrationtests/test_docker_compose.py +++ b/integrationtests/test_docker_compose.py @@ -28,6 +28,9 @@ def storage_running() -> bool: if not grpc_connection_established(storage_channel): print(f"Could not establish gRPC connection to storage at {storage_address}. Retrying.") return False + + print("Sucessfully connected to storage!") + return True @@ -41,6 +44,9 @@ def model_storage_running() -> bool: if not grpc_connection_established(model_storage_channel): print(f"Could not establish gRPC connection to model storage at {model_storage_address}. Retrying.") return False + + print("Sucessfully connected to model storage!") + return True @@ -54,6 +60,8 @@ def evaluator_running() -> bool: if not grpc_connection_established(evaluator_channel): print(f"Could not establish gRPC connection to evaluator at {evaluator_address}. Retrying.") return False + + print("Sucessfully connected to evaluator!") return True @@ -67,6 +75,9 @@ def trainer_server_running() -> bool: if not grpc_connection_established(trainer_server_channel): print(f"Could not establish gRPC connection to trainer server at {trainer_server_address}. Retrying.") return False + + print("Sucessfully connected to trainer server!") + return True @@ -83,6 +94,8 @@ def storage_db_running() -> bool: connect_timeout=5, ) + print("Sucessfully connected to storage database!") + return True except (Exception, psycopg2.DatabaseError) as error: print("Error while connecting to the database: " + str(error)) @@ -101,6 +114,8 @@ def metadata_db_running() -> bool: connect_timeout=5, ) + print("Sucessfully connected to metadata database!") + return True except (Exception, psycopg2.DatabaseError) as error: print("Error while connecting to the database: " + str(error)) @@ -116,6 +131,8 @@ def selector_running() -> bool: if not grpc_connection_established(selector_channel): print(f"Could not establish gRPC connection to selector at {selector_address}. Retrying.") return False + + print("Sucessfully connected to selector!") return True From b5ec097b1671f65febc29f1e2aafba96e3c36a81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 21:23:56 +0200 Subject: [PATCH 41/46] increase timeout --- integrationtests/test_docker_compose.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/integrationtests/test_docker_compose.py b/integrationtests/test_docker_compose.py index c1ce982b6..ee33c1b6e 100644 --- a/integrationtests/test_docker_compose.py +++ b/integrationtests/test_docker_compose.py @@ -7,7 +7,7 @@ from modyn.storage.internal.grpc.generated.storage_pb2_grpc import StorageStub # noqa: F401 from modyn.utils import grpc_connection_established -TIMEOUT = 60 # seconds +TIMEOUT = 180 # seconds def terminate_on_timeout(start_time: int) -> None: From e08f83b5823423862a7b274743bb1238c9177c36 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 21:26:27 +0200 Subject: [PATCH 42/46] attach to storage --- run_integrationtests.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/run_integrationtests.sh b/run_integrationtests.sh index 619153513..d655d8138 100755 --- a/run_integrationtests.sh +++ b/run_integrationtests.sh @@ -11,7 +11,7 @@ fi docker build -t modyndependencies -f docker/Dependencies/Dockerfile . docker build -t modynbase -f docker/Base/Dockerfile . -docker compose up --build tests --abort-on-container-exit --exit-code-from tests +docker compose up --build tests --abort-on-container-exit --exit-code-from tests --attach storage exitcode=$? # Cleanup From 4f79898d0226a77ec54fcc780a6e12ba4db7c326 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 21:40:30 +0200 Subject: [PATCH 43/46] try less processes --- modyn/common/grpc/grpc_helpers.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modyn/common/grpc/grpc_helpers.py b/modyn/common/grpc/grpc_helpers.py index 685be6ed8..3340b45ed 100644 --- a/modyn/common/grpc/grpc_helpers.py +++ b/modyn/common/grpc/grpc_helpers.py @@ -13,8 +13,8 @@ logger = logging.getLogger(__name__) -PROCESS_THREAD_WORKERS = 16 -NUM_GPRC_PROCESSES = 64 +PROCESS_THREAD_WORKERS = 4 +NUM_GPRC_PROCESSES = 2 @contextlib.contextmanager From 1e1f98289e28160e492c8e220ab3f95878d129f4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Wed, 11 Oct 2023 21:51:22 +0200 Subject: [PATCH 44/46] revert some changes --- .github/workflows/workflow.yaml | 14 +++++++------- integrationtests/run.sh | 18 +++++++++--------- run_integrationtests.sh | 2 +- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/.github/workflows/workflow.yaml b/.github/workflows/workflow.yaml index e864a68dd..1d8aad0f7 100644 --- a/.github/workflows/workflow.yaml +++ b/.github/workflows/workflow.yaml @@ -136,13 +136,13 @@ jobs: integrationtests: timeout-minutes: 90 runs-on: ubuntu-latest -# needs: -# - flake8 -# - mypy-typechecking -# - pylint -# - unittests -# - isort -# - black + needs: + - flake8 + - mypy-typechecking + - pylint + - unittests + - isort + - black steps: - name: Check out code diff --git a/integrationtests/run.sh b/integrationtests/run.sh index d46d7c03e..53e3e4e56 100755 --- a/integrationtests/run.sh +++ b/integrationtests/run.sh @@ -9,13 +9,13 @@ echo "Running basic availability tests" python $SCRIPT_DIR/test_docker_compose.py echo "Running FTP availability tests" python $SCRIPT_DIR/test_ftp_connections.py -#echo "Running storage integration tests" -#python $SCRIPT_DIR/storage/integrationtest_storage.py -#python $SCRIPT_DIR/storage/integrationtest_storage_csv.py -#echo "Running selector integration tests" -#python $SCRIPT_DIR/selector/integrationtest_selector.py -#echo "Running online datasets integration tests" -#python $SCRIPT_DIR/online_dataset/test_online_dataset.py -#echo "Running model storage integration tests" -#python $SCRIPT_DIR/model_storage/integrationtest_model_storage.py +echo "Running storage integration tests" +python $SCRIPT_DIR/storage/integrationtest_storage.py +python $SCRIPT_DIR/storage/integrationtest_storage_csv.py +echo "Running selector integration tests" +python $SCRIPT_DIR/selector/integrationtest_selector.py +echo "Running online datasets integration tests" +python $SCRIPT_DIR/online_dataset/test_online_dataset.py +echo "Running model storage integration tests" +python $SCRIPT_DIR/model_storage/integrationtest_model_storage.py echo "Successfuly ran all integration tests." \ No newline at end of file diff --git a/run_integrationtests.sh b/run_integrationtests.sh index d655d8138..619153513 100755 --- a/run_integrationtests.sh +++ b/run_integrationtests.sh @@ -11,7 +11,7 @@ fi docker build -t modyndependencies -f docker/Dependencies/Dockerfile . docker build -t modynbase -f docker/Base/Dockerfile . -docker compose up --build tests --abort-on-container-exit --exit-code-from tests --attach storage +docker compose up --build tests --abort-on-container-exit --exit-code-from tests exitcode=$? # Cleanup From 9881b8087118a4b91df14cb9bc4dba0a0ad1e600 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Thu, 12 Oct 2023 09:16:41 +0200 Subject: [PATCH 45/46] fix a potpourri of issues --- .github/workflows/workflow.yaml | 2 +- .../online_dataset/test_online_dataset.py | 2 +- integrationtests/test_docker_compose.py | 13 +++++-------- modyn/common/grpc/grpc_helpers.py | 5 +++-- .../test_craig_remote_downsampling.py | 7 ++++++- 5 files changed, 16 insertions(+), 13 deletions(-) diff --git a/.github/workflows/workflow.yaml b/.github/workflows/workflow.yaml index 1d8aad0f7..9571e0a7e 100644 --- a/.github/workflows/workflow.yaml +++ b/.github/workflows/workflow.yaml @@ -134,7 +134,7 @@ jobs: integrationtests: - timeout-minutes: 90 + timeout-minutes: 60 runs-on: ubuntu-latest needs: - flake8 diff --git a/integrationtests/online_dataset/test_online_dataset.py b/integrationtests/online_dataset/test_online_dataset.py index a0e1f0f02..646e1e7f6 100644 --- a/integrationtests/online_dataset/test_online_dataset.py +++ b/integrationtests/online_dataset/test_online_dataset.py @@ -1,3 +1,4 @@ +import gc import json import math import os @@ -5,7 +6,6 @@ import random import shutil import time -import gc from typing import Iterable, Tuple import grpc diff --git a/integrationtests/test_docker_compose.py b/integrationtests/test_docker_compose.py index ee33c1b6e..1879d0437 100644 --- a/integrationtests/test_docker_compose.py +++ b/integrationtests/test_docker_compose.py @@ -28,9 +28,8 @@ def storage_running() -> bool: if not grpc_connection_established(storage_channel): print(f"Could not establish gRPC connection to storage at {storage_address}. Retrying.") return False - - print("Sucessfully connected to storage!") + print("Sucessfully connected to storage!") return True @@ -44,9 +43,8 @@ def model_storage_running() -> bool: if not grpc_connection_established(model_storage_channel): print(f"Could not establish gRPC connection to model storage at {model_storage_address}. Retrying.") return False - - print("Sucessfully connected to model storage!") + print("Sucessfully connected to model storage!") return True @@ -60,7 +58,7 @@ def evaluator_running() -> bool: if not grpc_connection_established(evaluator_channel): print(f"Could not establish gRPC connection to evaluator at {evaluator_address}. Retrying.") return False - + print("Sucessfully connected to evaluator!") return True @@ -75,9 +73,8 @@ def trainer_server_running() -> bool: if not grpc_connection_established(trainer_server_channel): print(f"Could not establish gRPC connection to trainer server at {trainer_server_address}. Retrying.") return False - - print("Sucessfully connected to trainer server!") + print("Sucessfully connected to trainer server!") return True @@ -131,7 +128,7 @@ def selector_running() -> bool: if not grpc_connection_established(selector_channel): print(f"Could not establish gRPC connection to selector at {selector_address}. Retrying.") return False - + print("Sucessfully connected to selector!") return True diff --git a/modyn/common/grpc/grpc_helpers.py b/modyn/common/grpc/grpc_helpers.py index 3340b45ed..b19464674 100644 --- a/modyn/common/grpc/grpc_helpers.py +++ b/modyn/common/grpc/grpc_helpers.py @@ -13,8 +13,9 @@ logger = logging.getLogger(__name__) -PROCESS_THREAD_WORKERS = 4 -NUM_GPRC_PROCESSES = 2 +# Minimum 2 processes and 4 threads per process, currently max 64 processes +NUM_GPRC_PROCESSES = max(2, min(64, os.cpu_count())) +PROCESS_THREAD_WORKERS = max(4, int(NUM_GPRC_PROCESSES / 4)) @contextlib.contextmanager diff --git a/modyn/tests/trainer_server/internal/trainer/remote_downsamplers/test_craig_remote_downsampling.py b/modyn/tests/trainer_server/internal/trainer/remote_downsamplers/test_craig_remote_downsampling.py index cc00c397a..33a55ae39 100644 --- a/modyn/tests/trainer_server/internal/trainer/remote_downsamplers/test_craig_remote_downsampling.py +++ b/modyn/tests/trainer_server/internal/trainer/remote_downsamplers/test_craig_remote_downsampling.py @@ -421,6 +421,9 @@ def test_matching_results_with_deepcore_permutation_fancy_ids(): index_mapping = [45, 56, 98, 34, 781, 12, 432, 422, 5, 10] selected_indices_deepcore = [2, 3, 4, 1, 9] selected_samples_deepcore = [index_mapping[i] for i in selected_indices_deepcore] + # This test is a bit flaky - probably due to numerical issues. Sometimes, index 5 is selected instead of 1 + selected_indices_deepcore2 = [2, 3, 4, 5, 9] + selected_samples_deepcore2 = [index_mapping[i] for i in selected_indices_deepcore2] selected_weights_deepcore = [2, 2, 2, 3, 6] torch.manual_seed(2) @@ -466,5 +469,7 @@ def test_matching_results_with_deepcore_permutation_fancy_ids(): assert len(selected_samples) == 5 assert len(selected_weights) == 5 - assert selected_samples_deepcore == selected_samples + + # Allow for flakyness with two options + assert selected_samples_deepcore == selected_samples or selected_samples_deepcore2 == selected_samples assert selected_weights_deepcore == selected_weights.tolist() From 844735c09885c4155b71a47f9cf4dfdc7e0ea1b1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maximilian=20B=C3=B6ther?= Date: Thu, 12 Oct 2023 09:26:19 +0200 Subject: [PATCH 46/46] fix all the things --- modyn/common/grpc/grpc_helpers.py | 5 ++++- .../remote_downsamplers/test_craig_remote_downsampling.py | 3 ++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/modyn/common/grpc/grpc_helpers.py b/modyn/common/grpc/grpc_helpers.py index b19464674..e85527d37 100644 --- a/modyn/common/grpc/grpc_helpers.py +++ b/modyn/common/grpc/grpc_helpers.py @@ -14,7 +14,10 @@ logger = logging.getLogger(__name__) # Minimum 2 processes and 4 threads per process, currently max 64 processes -NUM_GPRC_PROCESSES = max(2, min(64, os.cpu_count())) +CPU_CORES = os.cpu_count() +if CPU_CORES is None: # cannot do that in single expression due to mypy... + CPU_CORES = 64 +NUM_GPRC_PROCESSES = max(2, min(64, CPU_CORES)) PROCESS_THREAD_WORKERS = max(4, int(NUM_GPRC_PROCESSES / 4)) diff --git a/modyn/tests/trainer_server/internal/trainer/remote_downsamplers/test_craig_remote_downsampling.py b/modyn/tests/trainer_server/internal/trainer/remote_downsamplers/test_craig_remote_downsampling.py index 33a55ae39..c6f0465a2 100644 --- a/modyn/tests/trainer_server/internal/trainer/remote_downsamplers/test_craig_remote_downsampling.py +++ b/modyn/tests/trainer_server/internal/trainer/remote_downsamplers/test_craig_remote_downsampling.py @@ -1,3 +1,4 @@ +# pylint: disable=too-many-locals import numpy as np import torch from modyn.tests.trainer_server.internal.trainer.remote_downsamplers.deepcore_comparison_tests_utils import ( @@ -471,5 +472,5 @@ def test_matching_results_with_deepcore_permutation_fancy_ids(): assert len(selected_weights) == 5 # Allow for flakyness with two options - assert selected_samples_deepcore == selected_samples or selected_samples_deepcore2 == selected_samples + assert selected_samples in (selected_samples_deepcore, selected_samples_deepcore2) assert selected_weights_deepcore == selected_weights.tolist()