Add benchmark made of multiple text datasets

src/renate/benchmark/datasets/nlp_datasets.py

@@ -1,13 +1,16 @@
 # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
+import functools
 import logging
 from typing import Any, Dict, Optional
 
 import datasets
 import torch
 import transformers
+from datasets import load_dataset
 
 from renate import defaults
+from renate.benchmark.datasets.base import DataIncrementalDataModule
 from renate.data.data_module import RenateDataModule
 
 
@@ -134,3 +137,160 @@ def tokenize_fn(batch):
             self._val_data = _InputTargetWrapper(self._val_data, self._target_column)
         else:
             self._train_data, self._val_data = self._split_train_val_data(self._train_data)
+
+
+class MultiTextDataModule(DataIncrementalDataModule):
+    """
+    Inspired by the dataset used in "Episodic Memory in Lifelong Language Learning"
+    by d’Autume et al. this is a collection of five different datasets that we call domains:
+    AGNews, Yelp, DBPedia and Yahoo Answers.
+
+    The output space if the union of the output space of all the domains.
+    The dataset has 33 classes: 4 from AGNews, 5 from Yelp, 14 from DBPedia, and 10 from Yahoo.
+
+    The maximum allowed size for the training set is 115000 and for the test set is 7600.
+    Each domain will have the same fixed size.
+
+    Args:
+        data_path: The path to the folder where the data files will be downloaded to.
+        tokenizer: Tokenizer to apply to the dataset. See https://huggingface.co/docs/tokenizers/
+            for more information on tokenizers.
+        tokenizer_kwargs: Keyword arguments passed when calling the tokenizer's ``__call__``
+           function. Typical options are `max_length`, `padding` and `truncation`.
+           See https://huggingface.co/docs/tokenizers/
+           for more information on tokenizers. If `None` is passed, this defaults to
+           `{"padding": "max_length", max_length: 128, truncation: True}`.
+        data_id: The dataset to be used
+        train_size: The size of the data stored as training set, must be smaller than 115000.
+        test_size: The size of the data stored as test set, must be smaller than 7600.
+        val_size: Fraction of the training data to be used for validation.
+        seed: Seed used to fix random number generation.
+    """
+
+    _multi_dataset_info = {
+        "ag_news": ["text", "label"],
+        "yelp_review_full": ["text", "label"],
+        "dbpedia_14": ["content", "label"],
+        "yahoo_answers_topics": ["question_title", "topic"],
+    }
+    _labels_map = {
+        "ag_news0": 0,
+        "ag_news1": 1,
+        "ag_news2": 2,
+        "ag_news3": 3,
+        "yelp_review_full0": 4,
+        "yelp_review_full1": 5,
+        "yelp_review_full2": 6,
+        "yelp_review_full3": 7,
+        "yelp_review_full4": 8,
+        "dbpedia_140": 9,
+        "dbpedia_141": 10,
+        "dbpedia_142": 11,
+        "dbpedia_143": 12,
+        "dbpedia_144": 13,
+        "dbpedia_145": 14,
+        "dbpedia_146": 15,
+        "dbpedia_147": 16,
+        "dbpedia_148": 17,
+        "dbpedia_149": 18,
+        "dbpedia_1410": 19,
+        "dbpedia_1411": 20,
+        "dbpedia_1412": 21,
+        "dbpedia_1413": 22,
+        "yahoo_answers_topics0": 23,
+        "yahoo_answers_topics1": 24,
+        "yahoo_answers_topics2": 25,
+        "yahoo_answers_topics3": 26,
+        "yahoo_answers_topics4": 27,
+        "yahoo_answers_topics5": 28,
+        "yahoo_answers_topics6": 29,
+        "yahoo_answers_topics7": 30,
+        "yahoo_answers_topics8": 31,
+        "yahoo_answers_topics9": 32,
+    }
+
+    domains = _multi_dataset_info.keys()
+
+    def __init__(
+        self,
+        data_path: str,
+        tokenizer: transformers.PreTrainedTokenizer,
+        data_id: str,
+        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+        train_size: int = defaults.SMALL_TRAIN_SET_SIZE,
+        test_size: int = defaults.SMALL_TEST_SET_SIZE,
+        val_size: float = defaults.VALIDATION_SIZE,
+        seed: int = defaults.SEED,
+    ):
+        super().__init__(data_path=data_path, data_id=data_id, val_size=val_size, seed=seed)
+
+        if train_size > 115000:
+            raise ValueError("The `train_size` must be smaller than 115000")
+        self._train_size = train_size
+
+        if test_size > 7600:
+            raise ValueError("The `test_size` must be smaller than 7600")
+        self._test_size = test_size
+
+        self._tokenizer = tokenizer
+        self._tokenizer_kwargs = tokenizer_kwargs or defaults.TOKENIZER_KWARGS
+
+        if data_id not in self.domains:
+            raise ValueError(
+                f"The selected domain is not available. Select one among " f"{self.domains}"
+            )
+
+        self.data_id = data_id
+
+    def prepare_data(self) -> None:
+        """Download dataset."""
+
+        for split in ["train", "test"] + (["validation"] if self._val_size > 0 else []):
+            load_dataset(self.data_id, split=split, cache_dir=self._data_path)
+
+    def setup(self) -> None:
+        """Set up train, test and val datasets."""
+
+        def preprocess(example, text_field_name, label_field_name):
+            return {
+                **self._tokenizer(example[text_field_name], **self._tokenizer_kwargs),
+                "label": self._labels_map[f"{self.data_id}{example[label_field_name]}"],
+            }
+
+        def get_split(split_name):
+            dataset = load_dataset(self.data_id, split=split_name, cache_dir=self._data_path)
+            new_features = dataset.features.copy()
+            # the following is hack needed because the output space of the new dataset is
+            # the union of the output spaces of the single datasets
+            new_features[self._multi_dataset_info[self.data_id][1]] = datasets.ClassLabel(
+                num_classes=33
+            )
+
+            dataset = dataset.cast(new_features)
+
+            if "train" in split_name:
+                set_size = self._train_size
+            else:
+                set_size = self._test_size
+
+            rnd_idx = torch.randint(low=0, high=len(dataset), size=(set_size,)).tolist()
+            dataset = dataset.select(indices=rnd_idx)
+
+            dataset = dataset.map(
+                functools.partial(
+                    preprocess,
+                    text_field_name=self._multi_dataset_info[self.data_id][0],
+                    label_field_name=self._multi_dataset_info[self.data_id][1],
+                ),
+                remove_columns=list(dataset.features),
+                num_proc=4,
+            )
+
+            dataset.set_format(type="torch")
+
+            return _InputTargetWrapper(dataset)
+
+        self._train_data = get_split("train")
+        self._test_data = get_split("test")
+        if self._val_size > 0:
+            self._val_data = get_split("validation")

src/renate/defaults.py

@@ -104,6 +104,8 @@
 
 # Benchmark datasets/models
 TOKENIZER_KWARGS = {"padding": "max_length", "max_length": 128, "truncation": True}
+SMALL_TRAIN_SET_SIZE = 1000
+SMALL_TEST_SET_SIZE = 1000
 
 
 def scheduler(config_space: Dict[str, Any], mode: str, metric: str):

test/renate/benchmark/datasets/test_multi_data_nlp.py

@@ -0,0 +1,65 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+import pytest
+import torch
+import transformers as transformers
+
+from renate.benchmark.datasets.nlp_datasets import MultiTextDataModule
+
+
+@pytest.mark.skip(reason="This test create problems with the syne-tune redirect test")
+def test_multi_data_nlp_small(tmpdir):
+    TRAIN_SIZE = 100
+    TEST_SIZE = 100
+
+    data = MultiTextDataModule(
+        tmpdir,
+        train_size=TRAIN_SIZE,
+        test_size=TEST_SIZE,
+        data_id="ag_news",
+        tokenizer=transformers.DistilBertTokenizer.from_pretrained("distilbert-base-uncased"),
+        seed=42,
+    )
+
+    data.prepare_data()
+    data.setup()
+
+    assert len(data.train_data()) == TRAIN_SIZE
+    assert len(data.test_data()) == TEST_SIZE
+
+    first_input_agnews = data.train_data()[0][0]["input_ids"]
+
+    data.data_id = "dbpedia_14"
+    data.setup()
+
+    tr_data_dbpedia = data.train_data()
+    te_data_dbpedia = data.test_data()
+    assert len(tr_data_dbpedia) == TRAIN_SIZE
+    assert len(te_data_dbpedia) == TEST_SIZE
+
+    first_input_dbpedia = data.train_data()[0][0]["input_ids"]
+
+    assert not torch.all(torch.eq(first_input_dbpedia, first_input_agnews))
+
+
+@pytest.mark.skip(reason="This test requires downloading and processing four datasets.")
+def test_multi_data_nlp_full(tmpdir):
+    TRAIN_SIZE = 115000
+    TEST_SIZE = 7600
+
+    for d in MultiTextDataModule.domains:
+        data = MultiTextDataModule(
+            tmpdir,
+            train_size=TRAIN_SIZE,
+            test_size=TEST_SIZE,
+            data_id=d,
+            tokenizer=transformers.DistilBertTokenizer.from_pretrained("distilbert-base-uncased"),
+        )
+
+        data.prepare_data()
+        data.setup()
+
+        tr_data = data.train_data()
+        te_data = data.test_data()
+        assert len(tr_data) == TRAIN_SIZE
+        assert len(te_data) == TEST_SIZE