Source code for farm.data_handler.data_silo

import copy
import logging
import torch.multiprocessing as mp
from contextlib import ExitStack
from functools import partial
import random
from pathlib import Path

import numpy as np
from sklearn.utils.class_weight import compute_class_weight
from import ConcatDataset, Dataset, Subset, IterableDataset
from import DistributedSampler
from import RandomSampler, SequentialSampler
import torch
from sklearn.model_selection import StratifiedKFold, KFold
from tqdm import tqdm

from farm.data_handler.dataloader import NamedDataLoader
from farm.data_handler.processor import Processor, BertStyleLMProcessor
from farm.data_handler.utils import grouper
from farm.utils import MLFlowLogger as MlLogger
from farm.utils import log_ascii_workers, calc_chunksize
from farm.utils import get_dict_checksum
from farm.visual.ascii.images import TRACTOR_SMALL

logger = logging.getLogger(__name__)

[docs]class DataSilo: """ Generates and stores PyTorch DataLoader objects for the train, dev and test datasets. Relies upon functionality in the processor to do the conversion of the data. Will also calculate and display some statistics. """
[docs] def __init__( self, processor, batch_size, distributed=False, automatic_loading=True, max_multiprocessing_chunksize=2000, max_processes=128, caching=False, cache_path=Path("cache/data_silo"), ): """ :param processor: A dataset specific Processor object which will turn input (file or dict) into a Pytorch Dataset. :type processor: Processor :param batch_size: The size of batch that should be returned by the DataLoaders. :type batch_size: int :param distributed: Set to True if the program is running in a distributed setting. :type distributed: bool :param automatic_loading: Set to False, if you don't want to automatically load data at initialization. :type automatic_loading: bool :param max_multiprocessing_chunksize: max possible value for chunksize as calculated by `calc_chunksize()` in `farm.utils`. For certain cases like lm_finetuning, a smaller value can be set, as the default chunksize values are rather large that might cause memory issues. :type max_multiprocessing_chunksize: int :param max_processes: the maximum number of processes to spawn in the multiprocessing.Pool used in DataSilo. It can be set to 1 to disable the use of multiprocessing ot make debugging easier. :type max_processes: int :param caching: save the processed datasets on disk to save time/compute if the same train data is used to run multiple experiments. Each cache has a checksum based on the train_filename of the Processor and the batch size. :type caching: bool :param cache_path: root dir for storing the datasets' cache. :type cache_path: Path """ self.distributed = distributed self.processor = processor = {} self.batch_size = batch_size self.class_weights = None self.max_processes = max_processes self.max_multiprocessing_chunksize = max_multiprocessing_chunksize self.caching = caching self.cache_path = cache_path self.tensor_names = None if len(self.processor.tasks) == 0: raise Exception("No task initialized. Try initializing the processor with a metric and a label list. " "Alternatively you can add a task using Processor.add_task()") loaded_from_cache = False if self.caching: # Check if DataSets are present in cache checksum = self._get_checksum() dataset_path = self.cache_path / checksum if dataset_path.exists(): self._load_dataset_from_cache(dataset_path) loaded_from_cache = True if not loaded_from_cache and automatic_loading: # In most cases we want to load all data automatically, but in some cases we rather want to do this # later or load from dicts instead of file ( self._load_data()
@classmethod def _dataset_from_chunk(cls, chunk, processor): """ Creating a dataset for a chunk (= subset) of dicts. In multiprocessing: * we read in all dicts from a file * split all dicts into chunks * feed *one chunk* to *one process* => the *one chunk* gets converted to *one dataset* (that's what we do here) * all datasets get collected and concatenated :param chunk: Instead of only having a list of dicts here we also supply an index (ascending int) for each. => [(0, dict), (1, dict) ...] :type chunk: list of tuples :param processor: FARM Processor (e.g. TextClassificationProcessor) :return: PyTorch Dataset """ dicts = [d[1] for d in chunk] indices = [x[0] for x in chunk] dataset = processor.dataset_from_dicts(dicts=dicts, indices=indices) return dataset def _get_dataset(self, filename, dicts=None): if not filename and not dicts: raise ValueError("You must either supply `filename` or `dicts`") # loading dicts from file (default) if dicts is None: dicts = list(self.processor.file_to_dicts(filename)) #shuffle list of dicts here if we later want to have a random dev set splitted from train set if str(self.processor.train_filename) in str(filename): if not self.processor.dev_filename: if self.processor.dev_split > 0.0: random.shuffle(dicts) num_dicts = len(dicts) multiprocessing_chunk_size, num_cpus_used = calc_chunksize( num_dicts=num_dicts, max_processes=self.max_processes, max_chunksize=self.max_multiprocessing_chunksize, ) with ExitStack() as stack: if self.max_processes > 1: # use multiprocessing only when max_processes > 1 p = stack.enter_context(mp.Pool(processes=num_cpus_used)) f"Got ya {num_cpus_used} parallel workers to convert {num_dicts} dictionaries " f"to pytorch datasets (chunksize = {multiprocessing_chunk_size})..." ) log_ascii_workers(num_cpus_used, logger) results = p.imap( partial(self._dataset_from_chunk, processor=self.processor), grouper(dicts, multiprocessing_chunk_size), chunksize=1, ) else: f"Multiprocessing disabled, using a single worker to convert {num_dicts}" f"dictionaries to pytorch datasets." ) results = map(partial(self._dataset_from_chunk, processor=self.processor), grouper(dicts, num_dicts)) datasets = [] desc = f"Preprocessing Dataset" if filename: desc += f" {filename}" with tqdm(total=len(dicts), unit=' Dicts', desc=desc) as pbar: for dataset, tensor_names in results: datasets.append(dataset) # update progress bar (last step can have less dicts than actual chunk_size) pbar.update(min(multiprocessing_chunk_size, concat_datasets = ConcatDataset(datasets) return concat_datasets, tensor_names def _load_data(self, train_dicts=None, dev_dicts=None, test_dicts=None): """ Loading the train, dev and test datasets either from files (default) or from supplied dicts. The processor is called to handle the full conversion from "raw data" to a Pytorch Dataset. The resulting datasets are loaded into :param train_dicts: (Optional) dicts containing examples for training. :param dev_dicts: (Optional) dicts containing examples for dev. :param test_dicts: (Optional) dicts containing examples for test. :return: None """"\nLoading data into the data silo ..." "{}".format(TRACTOR_SMALL)) # train data if train_dicts: # either from supplied dicts"Loading train set from supplied dicts ")["train"], self.tensor_names = self._get_dataset(filename=None, dicts=train_dicts) elif self.processor.train_filename: # or from a file (default) train_file = self.processor.data_dir / self.processor.train_filename"Loading train set from: {} ".format(train_file))["train"], self.tensor_names = self._get_dataset(train_file) else:"No train set is being loaded")["train"] = None # dev data if dev_dicts: # either from supplied dicts"Loading train set from supplied dicts ")["dev"], self.tensor_names = self._get_dataset(filename=None, dicts=dev_dicts) elif self.processor.dev_filename: # or from file (default) dev_file = self.processor.data_dir / self.processor.dev_filename"Loading dev set from: {}".format(dev_file))["dev"], _ = self._get_dataset(dev_file) elif self.processor.dev_split > 0.0: # or split it apart from train set"Loading dev set as a slice of train set") self._create_dev_from_train() else:"No dev set is being loaded")["dev"] = None # test data if test_dicts: # either from supplied dicts"Loading train set from supplied dicts ")["test"], self.tensor_names = self._get_dataset(filename=None, dicts=test_dicts) elif self.processor.test_filename: # or from file (default) test_file = self.processor.data_dir / self.processor.test_filename"Loading test set from: {}".format(test_file)) if self.tensor_names:["test"], _ = self._get_dataset(test_file) else:["test"], self.tensor_names = self._get_dataset(test_file) else:"No test set is being loaded")["test"] = None if self.caching: self._save_dataset_to_cache() # derive stats and meta data self._calculate_statistics() # self.calculate_class_weights() self._initialize_data_loaders() def _load_dataset_from_cache(self, cache_dir): """ Load serialized dataset from a cache. """"Loading datasets from cache at {cache_dir}")["train"] = torch.load(cache_dir / "train_dataset") dev_dataset_path = cache_dir / "dev_dataset" if dev_dataset_path.exists():["dev"] = torch.load(dev_dataset_path) else:["dev"] = None test_dataset_path = cache_dir / "test_dataset" if test_dataset_path.exists():["test"] = torch.load(test_dataset_path) else:["test"] = None self.tensor_names = torch.load(cache_dir / "tensor_names") # derive stats and meta data self._calculate_statistics() # self.calculate_class_weights() self._initialize_data_loaders() def _get_checksum(self): """ Get checksum based on a dict to ensure validity of cached DataSilo """ # keys in the dict identifies uniqueness for a given DataSilo. payload_dict = { "train_filename": str(Path(self.processor.train_filename).absolute()), "data_dir": str(self.processor.data_dir.absolute()), "max_seq_len": self.processor.max_seq_len, "dev_split": self.processor.dev_split, "tasks": self.processor.tasks } checksum = get_dict_checksum(payload_dict) return checksum def _save_dataset_to_cache(self): """ Serialize and save dataset to a cache. """ checksum = self._get_checksum() cache_dir = self.cache_path / checksum cache_dir.mkdir(parents=True, exist_ok=True)["train"], cache_dir / "train_dataset") if["dev"]:["dev"], cache_dir / "dev_dataset") if["test"]:["test"], cache_dir / "test_dataset"), cache_dir / "tensor_names")"Cached the datasets at {cache_dir}") def _initialize_data_loaders(self): """ Initializing train, dev and test data loaders for the already loaded datasets """ if self.processor.train_filename: if self.distributed: sampler_train = DistributedSampler(["train"]) else: sampler_train = RandomSampler(["train"]) data_loader_train = NamedDataLoader(["train"], sampler=sampler_train, batch_size=self.batch_size, tensor_names=self.tensor_names, ) else: data_loader_train = None if["dev"] is not None: data_loader_dev = NamedDataLoader(["dev"], sampler=SequentialSampler(["dev"]), batch_size=self.batch_size, tensor_names=self.tensor_names, ) else: data_loader_dev = None if self.processor.test_filename: data_loader_test = NamedDataLoader(["test"], sampler=SequentialSampler(["test"]), batch_size=self.batch_size, tensor_names=self.tensor_names, ) else: data_loader_test = None self.loaders = { "train": data_loader_train, "dev": data_loader_dev, "test": data_loader_test, } def _create_dev_from_train(self): """ Split a dev set apart from the train dataset """ n_dev = int(self.processor.dev_split * len(["train"])) n_train = len(["train"]) - n_dev train_dataset, dev_dataset = self.random_split_ConcatDataset(["train"], lengths=[n_train, n_dev])["train"] = train_dataset if(len(dev_dataset) > 0):["dev"] = dev_dataset else: logger.warning("No dev set created. Please adjust the dev_split parameter.") f"Took {len(dev_dataset)} samples out of train set to create dev set (dev split is roughly {self.processor.dev_split})" )
[docs] def random_split_ConcatDataset(self, ds, lengths): """ Roughly split a Concatdataset into non-overlapping new datasets of given lengths. Samples inside Concatdataset should already be shuffled :param ds: Dataset to be split :type ds: Dataset :param lengths: lengths of splits to be produced :type lengths: list """ if sum(lengths) != len(ds): raise ValueError("Sum of input lengths does not equal the length of the input dataset!") try: idx_dataset = np.where(np.array(ds.cumulative_sizes) > lengths[0])[0][0] except IndexError: raise Exception("All dataset chunks are being assigned to train set leaving no samples for dev set. " "Either consider increasing dev_split or setting it to 0.0\n" f"Cumulative chunk sizes: {ds.cumulative_sizes}\n" f"train/dev split: {lengths}") assert idx_dataset >= 1, "Dev_split ratio is too large, there is no data in train set. " \ f"Please lower dev_split = {self.processor.dev_split}" train = ConcatDataset(ds.datasets[:idx_dataset]) test = ConcatDataset(ds.datasets[idx_dataset:]) return train, test
def _calculate_statistics(self): """ Calculate and log simple summary statistics of the datasets """ self.counts = {} if["train"]: self.counts["train"] = len(["train"]) else: self.counts["train"] = 0 if["dev"]: self.counts["dev"] = len(["dev"]) else: self.counts["dev"] = 0 if["test"]: self.counts["test"] = len(["test"]) else: self.counts["test"] = 0 seq_lens = [] if["train"]: for dataset in["train"].datasets: train_input_numpy = dataset[:][0].numpy() seq_lens.extend(np.sum(train_input_numpy != self.processor.tokenizer.pad_token_id, axis=1)) max_seq_len = dataset[:][0].shape[1] self.clipped = np.mean(np.array(seq_lens) == max_seq_len) if seq_lens else 0 self.ave_len = np.mean(seq_lens) if seq_lens else 0"Examples in train: {}".format(self.counts["train"]))"Examples in dev : {}".format(self.counts["dev"]))"Examples in test : {}".format(self.counts["test"]))"") if["train"]:"Longest sequence length observed after clipping: {}".format(max(seq_lens)))"Average sequence length after clipping: {}".format(self.ave_len))"Proportion clipped: {}".format(self.clipped)) if self.clipped > 0.5:"[Farmer's Tip] {}% of your samples got cut down to {} tokens. " "Consider increasing max_seq_len. " "This will lead to higher memory consumption but is likely to " "improve your model performance".format(round(self.clipped * 100, 1), max_seq_len)) MlLogger.log_params( { "n_samples_train": self.counts["train"], "n_samples_dev": self.counts["dev"], "n_samples_test": self.counts["test"], "batch_size": self.batch_size, "ave_seq_len": self.ave_len, "clipped": self.clipped, } )
[docs] def calculate_class_weights(self, task_name, source="train"): """ For imbalanced datasets, we can calculate class weights that can be used later in the loss function of the prediction head to upweight the loss of minorities. :param task_name: name of the task as used in the processor :type task_name: str """ tensor_name = self.processor.tasks[task_name]["label_tensor_name"] label_list = self.processor.tasks[task_name]["label_list"] tensor_idx = list(self.tensor_names).index(tensor_name) # we need at least ONE observation for each label to avoid division by zero in compute_class_weights. observed_labels = copy.deepcopy(label_list) if source == "all": datasets = elif source == "train": datasets = [["train"]] else: raise Exception("source argument expects one of [\"train\", \"all\"]") for dataset in datasets: if dataset is not None: observed_labels += [label_list[x[tensor_idx].item()] for x in dataset] #TODO scale e.g. via logarithm to avoid crazy spikes for rare classes class_weights = list(compute_class_weight("balanced", np.asarray(label_list), observed_labels)) return class_weights
[docs] def get_data_loader(self, dataset_name): return self.loaders[dataset_name]
[docs] def n_samples(self, dataset_name): """ Returns the number of samples in a given dataset. :param dataset_name: Choose from train, dev or test :type dataset_name: str """ return self.counts[dataset_name]
[docs]class StreamingDataSilo: """ Streaming Data Silo loads and preprocesses datasets in parallel to the model training. The samples are lazily created from the input file and batches are yielded on-the-fly when required during training. This is useful if you: - work with large datasets that don't fit in memory - want to save time (by not preprocessing the entire dataset before starting training) For optimal training performance and efficient utilization of shiny GPUs, the pipeline always keeps a few pre-computed batches ready to avoid any waiting time when a batch is requested during training. To parallelize the creation of batches, PyTorch DataLoader provide an option to use multiple workers that utilize the available CPU cores and ensure enough pre-computed batches. """
[docs] def __init__(self, processor, batch_size, dataloader_workers=8): """ :param processor: A dataset specific Processor object which will turn input file into a Pytorch Dataset. :type processor: Processor :param batch_size: The size of batch to use for model training. :type batch_size: int :param dataloader_workers: number of workers for PyTorch DataLoader to create batches in parallel :type dataloader_workers: int """ self.processor = processor self.batch_size = batch_size self.dataloader_workers = dataloader_workers
[docs] def get_data_loader(self, dataset_name): """ Returns a new instance of dataloader for the given dataset. The dataloader lazily yields from Iterable DataSets. After a complete iteration over the input data, the generators gets exhausted. So, for instance, in the case of model training, a new train dataloader must be used for each train epoch. :param dataset_name: 'train', 'dev', or 'test' set. :type dataset_name: str """ filename = None if dataset_name == "train": filename = self.processor.train_filename elif dataset_name == "dev": if self.processor.dev_split > 0.0: raise NotImplemented( "StreamingDataSilo does not have dev_split implemented. " "To use dev data, supply a dev filename when creating the Processor." ) elif self.processor.dev_filename: filename = self.processor.dev_filename elif dataset_name == "test": if self.processor.test_filename: filename = self.processor.test_filename if not filename: return None # Batching: # # The model Trainer is passed a PyTorch DataLoader instance that yields dataset batches for training. # # By default, the PyTorch DataLoader prefetch (2 * num_workers) samples. However, given the higher # batch sizes(usually >64) for model training, the default prefetch is not sufficient to keep the # model Training saturated with datasets. # # As a workaround, we yield batches of samples instead of yielding individual samples. The DataLoader # can then prefetch (2 * num_workers) number of batches of samples. # # Since the batching is now handled within _StreamingDataSet, we disable the batching on DataLoader side # by initializing the data loader with batch_size as 1. data_set = _StreamingDataSet( processor=self.processor, filepath=self.processor.data_dir / filename, batch_size=self.batch_size, dataloader_workers=self.dataloader_workers, ) data_loader = NamedDataLoader( dataset=data_set, batch_size=1, num_workers=self.dataloader_workers, pin_memory=True ) return data_loader
class _StreamingDataSet(IterableDataset): def __init__(self, processor, filepath, batch_size, dataloader_workers): """ :param processor: A dataset specific Processor object which will turn input file into a Pytorch Dataset. :type processor: Processor :param batch_size: The size of batch that should be returned by the DataLoaders. :type batch_size: int :param filepath: input filename to load the dataset from :type filepath: Path :param dataloader_workers: number of workers for PyTorch Dataloader :type dataloader_workers: int """ self.batch_size = batch_size self.processor = processor self.filepath = filepath self.dataloader_workers = dataloader_workers # calculate number of samples for __len__() total_lines = sum(1 for line in open(filepath, encoding="utf-8")) empty_lines = sum(1 if line == "\n" else 0 for line in open(filepath, encoding="utf-8")) self.n_samples = total_lines - (2 * empty_lines) self.file_to_dicts_generator = processor.file_to_dicts(filepath) def __len__(self): return self.n_samples def __iter__(self): # With IterableDataset, the same __iter__ is copied over to the multiple workers of # a Dataloader. Hence, we need to configure the __iter__ to not yield duplicated data # when more than 1 workers are used. # # To avoid duplicates, we need to split the input dicts between the workers. # The grouper() converts a dict generator given as input and yields only the # dicts that are to be processed by the given worker_id. # # For instance, consider input as [dictA, dictB, dictC, ...], then the grouper # (with n=2) will return, [[dictA, dictB], [dictE, dictF] ...] for worker 1 and # [[dictC, dictD], [dictG, dictH] ...] for worker 2. if self.dataloader_workers > 1: worker_info = worker_id = dicts = grouper( self.file_to_dicts_generator, n=10, worker_id=worker_id, total_workers=self.dataloader_workers ) else: dicts = grouper(self.file_to_dicts_generator, n=10) results = map(self._dataset_from_chunk, dicts) batch = [] for datasets, tensor_names in results: if not datasets: continue self.tensor_names = tensor_names for ds in datasets: batch.append(ds) if len(batch) == self.batch_size: yield batch batch = [] if batch: yield batch def _dataset_from_chunk(self, chunk): """ Creating a dataset for a chunk (= subset) of dicts. :param chunk: Instead of only having a list of dicts here we also supply an index (ascending int) for each. => [(0, dict), (1, dict) ...] :type chunk: list of tuples :return: PyTorch Dataset """ dicts = [d[1] for d in chunk] # need at least 2 documents to sample random sentences from if len(dicts) < 2 and type(self.processor) == BertStyleLMProcessor:"Skipping a dict chunk as it contains less than 2 documents ...") return None, None indices = [x[0] for x in chunk] datasets, tensor_names = self.processor.dataset_from_dicts(dicts=dicts, indices=indices) return datasets, tensor_names
[docs]class DataSiloForCrossVal: """ For performing cross validation, we really want to combine all the instances from all the sets or just some of the sets, then create a different data silo instance for each fold. Calling DataSiloForCrossVal.make() creates a list of DataSiloForCrossVal instances - one for each fold. """
[docs] def __init__(self, origsilo, trainset, devset, testset): self.tensor_names = origsilo.tensor_names = {"train": trainset, "dev": devset, "test": testset} self.processor = origsilo.processor self.batch_size = origsilo.batch_size # should not be necessary, xval makes no sense with huge data # sampler_train = DistributedSampler(["train"]) sampler_train = RandomSampler(trainset) self.data_loader_train = NamedDataLoader( dataset=trainset, sampler=sampler_train, batch_size=self.batch_size, tensor_names=self.tensor_names, ) self.data_loader_dev = NamedDataLoader( dataset=devset, sampler=SequentialSampler(devset), batch_size=self.batch_size, tensor_names=self.tensor_names, ) self.data_loader_test = NamedDataLoader( dataset=testset, sampler=SequentialSampler(testset), batch_size=self.batch_size, tensor_names=self.tensor_names, ) self.loaders = { "train": self.data_loader_train, "dev": self.data_loader_dev, "test": self.data_loader_test, }
[docs] def get_data_loader(self, which): return self.loaders[which]
[docs] @staticmethod def make(datasilo, sets=["train", "dev", "test"], n_splits=5, stratified=True, shuffle=True, random_state=None, dev_split=0.2): """ Create number of folds data-silo-like objects which can be used for training from the original data silo passed on. :param datasilo: the data silo that contains the original data :param sets: which sets to use to create the xval folds :param n_splits: number of folds to create :param stratified: if class stratificiation should be done :param shuffle: shuffle each class' samples before splitting :param random_state: random state for shuffling :param dev_split: size of the dev set for a fold, held out from the training set """ setstoconcat = [[setname] for setname in sets] ds_all = ConcatDataset(setstoconcat) idxs = list(range(len(ds_all))) if stratified: # get all the labels for stratification ytensors = [t[3][0] for t in ds_all] Y = torch.stack(ytensors) xval = StratifiedKFold(n_splits=n_splits, shuffle=shuffle, random_state=random_state) xval_split = xval.split(idxs,Y) else: xval = KFold(n_splits=n_splits, shuffle=shuffle, random_state=random_state) xval_split = xval.split(idxs) # for each fold create a DataSilo4Xval instance, where the training set is further # divided into actual train and dev set silos = [] for train_idx, test_idx in xval_split: n_dev = int(dev_split * len(train_idx)) n_actual_train = len(train_idx) - n_dev # TODO: this split into actual train and test set could/should also be stratified, for now # we just do this by taking the first/last indices from the train set (which should be # shuffled by default) actual_train_idx = train_idx[:n_actual_train] dev_idx = train_idx[n_actual_train:] # create the actual datasets ds_train = Subset(ds_all, actual_train_idx) ds_dev = Subset(ds_all, dev_idx) ds_test = Subset(ds_all, test_idx) silos.append(DataSiloForCrossVal(datasilo, ds_train, ds_dev, ds_test)) return silos