cd856b45b1
* feat:add support dataset_num_processes * chore * required changes * requested chnages * required chnages * required changes * required changes * elif get_default_process_count() * add:del data * Update cicd/Dockerfile.jinja Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> * Update cicd/single_gpu.py Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> --------- Co-authored-by: salman <salman.mohammadi@outlook.com> Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
430 lines
15 KiB
Python
430 lines
15 KiB
Python
"""Test suite for functions in the `axolotl.utils.data.utils` module, focusing on the
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`deduplicate_and_log_datasets` function.
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Additionally, this test suite includes tests for functions that indirectly call
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`deduplicate_and_log_datasets` during the execution of the preprocess command.
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"""
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import unittest
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from unittest.mock import patch
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import pytest
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from datasets import Dataset
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from axolotl.loaders import load_processor, load_tokenizer
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.data import prepare_datasets, prepare_preference_datasets
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from axolotl.utils.data.utils import deduplicate_and_log_datasets
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from axolotl.utils.dict import DictDefault
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from tests.constants import ALPACA_MESSAGES_CONFIG_REVISION
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from tests.hf_offline_utils import enable_hf_offline
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def verify_deduplication(actual_dataset, expected_dataset, dataset_name):
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"""Validates deduplication results and size consistency.
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Parameters:
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- actual_dataset: Deduplicated dataset.
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- expected_dataset: Expected dataset.
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- dataset_name: Name of the dataset (e.g., 'train' or 'eval').
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Asserts:
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- Datasets match in content.
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- Dataset size matches unique row count.
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"""
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# Convert datasets to sets of tuples for unordered comparison
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actual_rows = set(tuple(row.values()) for row in actual_dataset)
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expected_rows = set(tuple(row.values()) for row in expected_dataset)
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# Verify deduplication correctness
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assert actual_rows == expected_rows, f"Mismatch in {dataset_name} dataset"
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# Verify size consistency
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assert len(actual_rows) == len(actual_dataset), (
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f"Size mismatch in {dataset_name} dataset after deduplication"
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)
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class TestDeduplicateIndividualFunctions(unittest.TestCase):
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"""Test class for deduplication function in data utils"""
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def setUp(self):
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# Sample data with duplicates
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self.data = {
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"column1": ["apple", "banana", "apple", "orange", "banana"],
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"column2": [1, 2, 1, 3, 2],
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"column3": ["red", "yellow", "red", "orange", "yellow"],
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}
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# Expected result after deduplication
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self.expected_data = {
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"column1": ["apple", "banana", "orange"],
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"column2": [1, 2, 3],
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"column3": ["red", "yellow", "orange"],
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}
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# Convert to Dataset format
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self.dataset = Dataset.from_dict(self.data)
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self.expected_dataset = Dataset.from_dict(self.expected_data)
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def test_deduplication(self):
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train_dataset, _ = deduplicate_and_log_datasets(dataset=self.dataset)
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eval_dataset, _ = deduplicate_and_log_datasets(
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dataset=self.dataset, dataset_name="eval"
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)
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verify_deduplication(train_dataset, self.expected_dataset, "train_dataset")
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verify_deduplication(eval_dataset, self.expected_dataset, "eval_dataset")
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def test_exact_duplicates(self):
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# Test when datasets are exact duplicates
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duplicate_data = {
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"column1": ["apple", "apple", "apple"],
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"column2": [1, 1, 1],
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"column3": ["red", "red", "red"],
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}
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expected_data = {"column1": ["apple"], "column2": [1], "column3": ["red"]}
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# Convert to Dataset format
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dataset = Dataset.from_dict(duplicate_data)
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expected_dataset = Dataset.from_dict(expected_data)
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# Run deduplication
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train_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
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eval_dataset, _ = deduplicate_and_log_datasets(
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dataset=dataset, dataset_name="eval"
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)
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verify_deduplication(train_dataset, expected_dataset, "train_dataset")
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verify_deduplication(eval_dataset, expected_dataset, "eval_dataset")
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def test_partial_duplicates(self):
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# Test when only part of the dataset is a duplicate
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partial_duplicate_data = {
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"column1": ["apple", "banana", "apple"],
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"column2": [1, 2, 1],
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"column3": ["red", "yellow", "red"],
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}
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expected_data = {
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"column1": ["apple", "banana"],
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"column2": [1, 2],
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"column3": ["red", "yellow"],
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}
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# Convert to Dataset format
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dataset = Dataset.from_dict(partial_duplicate_data)
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expected_dataset = Dataset.from_dict(expected_data)
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# Run deduplication
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train_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
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eval_dataset, _ = deduplicate_and_log_datasets(
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dataset=dataset, dataset_name="eval"
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)
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verify_deduplication(train_dataset, expected_dataset, "train_dataset")
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verify_deduplication(eval_dataset, expected_dataset, "eval_dataset")
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def test_combined_duplicates_empty(self):
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# Test when only part of the dataset is a duplicate
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partial_duplicate_data = {
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"column1": ["apple", "banana", "apple"],
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"column2": [1, 2, 1],
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"column3": ["red", "yellow", "red"],
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}
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expected_data_train = {
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"column1": ["apple", "banana"],
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"column2": [1, 2],
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"column3": ["red", "yellow"],
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}
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expected_data_eval = {
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"column1": [],
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"column2": [],
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"column3": [],
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}
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# Convert to Dataset format
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dataset = Dataset.from_dict(partial_duplicate_data)
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expected_dataset_train = Dataset.from_dict(expected_data_train)
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expected_dataset_eval = Dataset.from_dict(expected_data_eval)
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# Run deduplication
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train_dataset, eval_dataset = deduplicate_and_log_datasets(
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dataset=dataset, other_dataset=dataset
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)
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verify_deduplication(train_dataset, expected_dataset_train, "train_dataset")
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verify_deduplication(eval_dataset, expected_dataset_eval, "eval_dataset")
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def test_combined_duplicates_one(self):
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# Test when only part of the dataset is a duplicate
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partial_duplicate_data_train = {
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"column1": ["apple", "banana", "apple"],
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"column2": [1, 2, 1],
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"column3": ["red", "yellow", "red"],
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}
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partial_duplicate_data_eval = {
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"column1": ["apple", "orange", "apple"],
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"column2": [1, 2, 1],
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"column3": ["red", "orange", "red"],
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}
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expected_data_train = {
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"column1": ["apple", "banana"],
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"column2": [1, 2],
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"column3": ["red", "yellow"],
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}
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expected_data_eval = {
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"column1": ["orange"],
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"column2": [2],
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"column3": ["orange"],
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}
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# Convert to Dataset format
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dataset_train = Dataset.from_dict(partial_duplicate_data_train)
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dataset_eval = Dataset.from_dict(partial_duplicate_data_eval)
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expected_dataset_train = Dataset.from_dict(expected_data_train)
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expected_dataset_eval = Dataset.from_dict(expected_data_eval)
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# Run deduplication
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train_dataset, eval_dataset = deduplicate_and_log_datasets(
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dataset=dataset_train, other_dataset=dataset_eval
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)
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verify_deduplication(train_dataset, expected_dataset_train, "train_dataset")
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verify_deduplication(eval_dataset, expected_dataset_eval, "eval_dataset")
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class TestDeduplicateRLDataset:
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"""Test a configured dataloader with deduplication."""
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@pytest.fixture
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def cfg(self):
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fixture = DictDefault(
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{
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"tokenizer_config": "huggyllama/llama-7b",
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"sequence_len": 1024,
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"rl": "dpo",
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"chat_template": "llama3",
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"dataset_exact_deduplication": True,
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"datasets": [
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ALPACA_MESSAGES_CONFIG_REVISION,
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ALPACA_MESSAGES_CONFIG_REVISION,
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],
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"dataset_num_proc": 4,
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}
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)
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yield fixture
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@enable_hf_offline
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def test_load_with_deduplication(
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self,
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cfg,
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dataset_fozziethebeat_alpaca_messages_2k_dpo_test_rev_ea82cff,
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tokenizer_huggyllama,
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):
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"""Verify that loading with deduplication removes duplicates."""
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with (
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patch(
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"axolotl.utils.data.rl.load_dataset_with_config"
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) as mock_load_dataset,
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patch("axolotl.loaders.load_tokenizer") as mock_load_tokenizer,
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):
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# Set up the mock to return different values on successive calls
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mock_load_dataset.side_effect = [
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dataset_fozziethebeat_alpaca_messages_2k_dpo_test_rev_ea82cff,
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dataset_fozziethebeat_alpaca_messages_2k_dpo_test_rev_ea82cff,
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]
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mock_load_tokenizer.return_value = tokenizer_huggyllama
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tokenizer = load_tokenizer(cfg)
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train_dataset, _ = prepare_preference_datasets(cfg, tokenizer)
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# Verify that the dataset has been deduplicated
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assert len(train_dataset) == 1800, "Dataset was not properly deduplicated"
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@enable_hf_offline
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def test_load_without_deduplication(
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self,
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cfg,
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dataset_fozziethebeat_alpaca_messages_2k_dpo_test_rev_ea82cff,
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tokenizer_huggyllama,
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):
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with (
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patch(
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"axolotl.utils.data.rl.load_dataset_with_config"
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) as mock_load_dataset,
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patch("axolotl.loaders.load_tokenizer") as mock_load_tokenizer,
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):
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# Set up the mock to return different values on successive calls
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mock_load_dataset.side_effect = [
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dataset_fozziethebeat_alpaca_messages_2k_dpo_test_rev_ea82cff,
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dataset_fozziethebeat_alpaca_messages_2k_dpo_test_rev_ea82cff,
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]
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mock_load_tokenizer.return_value = tokenizer_huggyllama
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# Load the dataset without deduplication
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cfg.dataset_exact_deduplication = False
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tokenizer = load_tokenizer(cfg)
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train_dataset, _ = prepare_preference_datasets(cfg, tokenizer)
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# Verify that the dataset retains duplicates
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assert len(train_dataset) == 1800 * 2, (
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"Dataset deduplication occurred when it should not have"
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)
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class TestDeduplicateNonRL(unittest.TestCase):
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"""Test prepare_dataset function with different configurations."""
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@enable_hf_offline
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def setUp(self) -> None:
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self.cfg_1 = DictDefault(
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{
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"base_model": "huggyllama/llama-7b",
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"tokenizer_config": "huggyllama/llama-7b",
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"sequence_len": 1024,
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"dataset_exact_deduplication": True,
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"val_set_size": 0.0,
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"gradient_accumulation_steps": 2,
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"batch_size": 10,
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"micro_batch_size": 10,
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"num_epochs": 1,
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}
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)
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self.cfg_1 = validate_config(self.cfg_1)
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normalize_config(self.cfg_1)
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@pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
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@enable_hf_offline
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def test_prepare_dataset_with_deduplication_train(self):
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"""Verify that prepare_dataset function processes the dataset correctly with deduplication."""
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self.cfg_1.dataset_exact_deduplication = True
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# Load tokenizer and processor
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tokenizer = load_tokenizer(self.cfg_1)
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processor = (
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load_processor(self.cfg_1, tokenizer=tokenizer)
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if self.cfg_1.processor_type
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else None
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)
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# Prepare dataset using the prepare_dataset function
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train_dataset, _, _, _ = prepare_datasets(
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self.cfg_1,
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tokenizer,
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processor=processor,
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)
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self.assertEqual(
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len(train_dataset),
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2000,
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"Train dataset should have 2000 samples after deduplication.",
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)
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@pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
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@enable_hf_offline
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def test_prepare_dataset_with_deduplication_eval(self):
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"""Verify that prepare_dataset function processes the dataset correctly with deduplication."""
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self.cfg_1.dataset_exact_deduplication = True
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self.cfg_1.val_set_size = 0.5
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# Load tokenizer and processor
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tokenizer = load_tokenizer(self.cfg_1)
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processor = (
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load_processor(self.cfg_1, tokenizer=tokenizer)
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if self.cfg_1.processor_type
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else None
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)
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# Prepare dataset using the prepare_dataset function
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_, eval_dataset, _, _ = prepare_datasets(
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self.cfg_1,
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tokenizer,
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processor=processor,
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)
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self.assertEqual(
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len(eval_dataset),
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1000,
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"Eval dataset should have 2000 samples after deduplication.",
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)
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@pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
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@enable_hf_offline
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def test_prepare_dataset_without_deduplication(self):
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"""Verify that prepare_dataset function processes the dataset correctly without deduplication."""
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self.cfg_1.dataset_exact_deduplication = False
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self.cfg_1.val_set_size = 0.1
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# Load tokenizer and processor
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tokenizer = load_tokenizer(self.cfg_1)
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processor = (
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load_processor(self.cfg_1, tokenizer=tokenizer)
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if self.cfg_1.processor_type
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else None
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)
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# Prepare dataset using the prepare_dataset function
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train_dataset, eval_dataset, _, _ = prepare_datasets(
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self.cfg_1,
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tokenizer,
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processor=processor,
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)
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# Verify that the dataset has been prepared correctly
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self.assertEqual(
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len(train_dataset),
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1800 * 2,
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"Train dataset should have 3600 samples without deduplication.",
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)
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self.assertEqual(
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len(eval_dataset),
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200 * 2,
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"Train dataset should have 400 samples after deduplication.",
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)
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class TestWrongCollisions(unittest.TestCase):
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"""Creating mock datasets for testing wrong collisions."""
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def setUp(self):
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self.train_data = {"text": ["sample 5", "sample 6"], "label": [1, 2]}
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self.eval_data = {
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"text": [
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"sample 5",
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"sample 7",
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], # Different label but same text as in train_data
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"label": [2, 3],
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}
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self.dataset_data = {
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"text": ["sample 5", "sample 9", "sample 5"],
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"label": [1, 2, 8],
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}
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self.train_dataset = Dataset.from_dict(self.train_data)
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self.eval_dataset = Dataset.from_dict(self.eval_data)
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self.dataset = Dataset.from_dict(self.dataset_data)
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def test_deduplication_dataset_only(self):
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dedup_dataset, _ = deduplicate_and_log_datasets(dataset=self.dataset)
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self.assertEqual(
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len(dedup_dataset), 3, "Dataset should have all original values"
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)
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self.assertEqual(
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str(dedup_dataset),
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str(self.dataset),
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"The string representation of the output dataset should not differ.",
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)
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if __name__ == "__main__":
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unittest.main()
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