05f03b541a
* hf offline decorator for tests to workaround rate limits * fail quicker so we can see logs * try new cache name * limit files downloaded * phi mini predownload * offline decorator for phi tokenizer * handle meta llama 8b offline too * make sure to return fixtures if they are wrapped too * more fixes * more things offline * more offline things * fix the env var * fix the model name * handle gemma also * force reload of modules to recheck offline status * prefetch mistral too * use reset_sessions so hub picks up offline mode * more fixes * rename so it doesn't seem like a context manager * fix backoff * switch out tinyshakespeare dataset since it runs a py script to fetch data and doesn't work offline * include additional dataset * more fixes * more fixes * replace tiny shakespeaere dataset * skip some tests for now * use more robust check using snapshot download to determine if a dataset name is on the hub * typo for skip reason * use local_files_only * more fixtures * remove local only * use tiny shakespeare as pretrain dataset and streaming can't be offline even if precached * make sure fixtures aren't offline improve the offline reset try bumping version of datasets reorder reloading and setting prime a new cache run the tests now with fresh cache try with a static cache * now run all the ci again with hopefully a correct cache * skip wonky tests for now * skip wonky tests for now * handle offline mode for model card creation
442 lines
16 KiB
Python
442 lines
16 KiB
Python
"""
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Test dataset loading under various conditions.
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"""
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import shutil
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import tempfile
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import unittest
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from pathlib import Path
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import pytest
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from constants import (
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ALPACA_MESSAGES_CONFIG_OG,
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ALPACA_MESSAGES_CONFIG_REVISION,
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SPECIAL_TOKENS,
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)
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from datasets import Dataset
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from huggingface_hub import snapshot_download
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from transformers import AutoTokenizer
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from utils import enable_hf_offline
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from axolotl.utils.data import load_tokenized_prepared_datasets
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from axolotl.utils.data.rl import load_prepare_preference_datasets
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from axolotl.utils.dict import DictDefault
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class TestDatasetPreparation(unittest.TestCase):
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"""Test a configured dataloader."""
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@enable_hf_offline
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def setUp(self) -> None:
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self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
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self.tokenizer.add_special_tokens(SPECIAL_TOKENS)
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# Alpaca dataset.
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self.dataset = Dataset.from_list(
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[
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{
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"instruction": "Evaluate this sentence for spelling and grammar mistakes",
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"input": "He finnished his meal and left the resturant",
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"output": "He finished his meal and left the restaurant.",
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}
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]
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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_load_hub(self):
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"""Core use case. Verify that processing data from the hub works"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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prepared_path = Path(tmp_dir) / "prepared"
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cfg = 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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"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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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 2000
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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@enable_hf_offline
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@pytest.mark.skip("datasets bug with local datasets when offline")
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def test_load_local_hub(self):
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"""Niche use case. Verify that a local copy of a hub dataset can be loaded"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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tmp_ds_path = Path(tmp_dir) / "mhenrichsen/alpaca_2k_test"
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tmp_ds_path.mkdir(parents=True, exist_ok=True)
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snapshot_path = snapshot_download(
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repo_id="mhenrichsen/alpaca_2k_test",
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repo_type="dataset",
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local_dir=tmp_ds_path,
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)
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# offline mode doesn't actually copy it to local_dir, so we
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# have to copy all the contents in the dir manually from the returned snapshot_path
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shutil.copytree(snapshot_path, tmp_ds_path, dirs_exist_ok=True)
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prepared_path = Path(tmp_dir) / "prepared"
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# Right now a local copy that doesn't fully conform to a dataset
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# must list data_files and ds_type otherwise the loader won't know
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# how to load it.
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cfg = DictDefault(
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{
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"tokenizer_config": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 1024,
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"ds_type": "parquet",
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"type": "alpaca",
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"data_files": [
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f"{tmp_ds_path}/alpaca_2000.parquet",
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],
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},
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],
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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 2000
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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shutil.rmtree(tmp_ds_path)
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@enable_hf_offline
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def test_load_from_save_to_disk(self):
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"""Usual use case. Verify datasets saved via `save_to_disk` can be loaded."""
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with tempfile.TemporaryDirectory() as tmp_dir:
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tmp_ds_name = Path(tmp_dir) / "tmp_dataset"
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self.dataset.save_to_disk(str(tmp_ds_name))
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prepared_path = Path(tmp_dir) / "prepared"
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cfg = DictDefault(
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{
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"tokenizer_config": "huggyllama/llama-7b",
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"sequence_len": 256,
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"datasets": [
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{
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"path": str(tmp_ds_name),
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"type": "alpaca",
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},
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],
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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 1
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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@enable_hf_offline
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def test_load_from_dir_of_parquet(self):
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"""Usual use case. Verify a directory of parquet files can be loaded."""
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with tempfile.TemporaryDirectory() as tmp_dir:
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tmp_ds_dir = Path(tmp_dir) / "tmp_dataset"
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tmp_ds_dir.mkdir()
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tmp_ds_path = tmp_ds_dir / "shard1.parquet"
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self.dataset.to_parquet(tmp_ds_path)
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prepared_path: Path = Path(tmp_dir) / "prepared"
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cfg = DictDefault(
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{
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"tokenizer_config": "huggyllama/llama-7b",
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"sequence_len": 256,
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"datasets": [
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{
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"path": str(tmp_ds_dir),
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"ds_type": "parquet",
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"name": "test_data",
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"data_files": [
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str(tmp_ds_path),
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],
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"type": "alpaca",
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},
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],
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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 1
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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@enable_hf_offline
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def test_load_from_dir_of_json(self):
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"""Standard use case. Verify a directory of json files can be loaded."""
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with tempfile.TemporaryDirectory() as tmp_dir:
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tmp_ds_dir = Path(tmp_dir) / "tmp_dataset"
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tmp_ds_dir.mkdir()
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tmp_ds_path = tmp_ds_dir / "shard1.json"
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self.dataset.to_json(tmp_ds_path)
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prepared_path: Path = Path(tmp_dir) / "prepared"
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cfg = DictDefault(
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{
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"tokenizer_config": "huggyllama/llama-7b",
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"sequence_len": 256,
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"datasets": [
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{
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"path": str(tmp_ds_dir),
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"ds_type": "json",
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"name": "test_data",
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"data_files": [
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str(tmp_ds_path),
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],
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"type": "alpaca",
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},
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],
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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 1
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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@enable_hf_offline
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def test_load_from_single_parquet(self):
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"""Standard use case. Verify a single parquet file can be loaded."""
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with tempfile.TemporaryDirectory() as tmp_dir:
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tmp_ds_path = Path(tmp_dir) / "tmp_dataset.parquet"
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self.dataset.to_parquet(tmp_ds_path)
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prepared_path: Path = Path(tmp_dir) / "prepared"
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cfg = DictDefault(
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{
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"tokenizer_config": "huggyllama/llama-7b",
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"sequence_len": 256,
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"datasets": [
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{
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"path": str(tmp_ds_path),
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"name": "test_data",
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"type": "alpaca",
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},
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],
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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 1
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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@enable_hf_offline
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def test_load_from_single_json(self):
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"""Standard use case. Verify a single json file can be loaded."""
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with tempfile.TemporaryDirectory() as tmp_dir:
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tmp_ds_path = Path(tmp_dir) / "tmp_dataset.json"
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self.dataset.to_json(tmp_ds_path)
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prepared_path: Path = Path(tmp_dir) / "prepared"
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cfg = DictDefault(
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{
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"tokenizer_config": "huggyllama/llama-7b",
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"sequence_len": 256,
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"datasets": [
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{
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"path": str(tmp_ds_path),
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"name": "test_data",
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"type": "alpaca",
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},
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],
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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 1
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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@pytest.mark.skip(reason="TODO: fix hf offline mode for CI rate limits")
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@enable_hf_offline
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def test_load_hub_with_dpo(self):
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"""Verify that processing dpo data from the hub works"""
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cfg = 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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"datasets": [ALPACA_MESSAGES_CONFIG_OG],
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}
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)
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train_dataset, _ = load_prepare_preference_datasets(cfg)
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assert len(train_dataset) == 1800
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assert "conversation" in train_dataset.features
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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_load_hub_with_revision(self):
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"""Verify that processing data from the hub works with a specific revision"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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prepared_path = Path(tmp_dir) / "prepared"
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# make sure prepared_path is empty
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shutil.rmtree(prepared_path, ignore_errors=True)
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cfg = 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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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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"revision": "d05c1cb",
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},
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],
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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 2000
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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@enable_hf_offline
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def test_load_hub_with_revision_with_dpo(self):
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"""Verify that processing dpo data from the hub works with a specific revision"""
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cfg = 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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"datasets": [ALPACA_MESSAGES_CONFIG_REVISION],
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}
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)
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train_dataset, _ = load_prepare_preference_datasets(cfg)
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assert len(train_dataset) == 1800
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assert "conversation" in train_dataset.features
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@enable_hf_offline
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@pytest.mark.skip("datasets bug with local datasets when offline")
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def test_load_local_hub_with_revision(self):
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"""Verify that a local copy of a hub dataset can be loaded with a specific revision"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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tmp_ds_path = Path(tmp_dir) / "mhenrichsen/alpaca_2k_test"
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tmp_ds_path.mkdir(parents=True, exist_ok=True)
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snapshot_path = snapshot_download(
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repo_id="mhenrichsen/alpaca_2k_test",
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repo_type="dataset",
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local_dir=tmp_ds_path,
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revision="d05c1cb",
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)
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shutil.copytree(snapshot_path, tmp_ds_path, dirs_exist_ok=True)
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prepared_path = Path(tmp_dir) / "prepared"
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cfg = 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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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"ds_type": "parquet",
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"type": "alpaca",
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"data_files": [
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f"{tmp_ds_path}/alpaca_2000.parquet",
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],
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"revision": "d05c1cb",
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},
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],
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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 2000
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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shutil.rmtree(tmp_ds_path)
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@enable_hf_offline
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def test_loading_local_dataset_folder(self):
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"""Verify that a dataset downloaded to a local folder can be loaded"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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tmp_ds_path = Path(tmp_dir) / "mhenrichsen/alpaca_2k_test"
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tmp_ds_path.mkdir(parents=True, exist_ok=True)
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snapshot_path = snapshot_download(
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repo_id="mhenrichsen/alpaca_2k_test",
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repo_type="dataset",
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local_dir=tmp_ds_path,
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)
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shutil.copytree(snapshot_path, tmp_ds_path, dirs_exist_ok=True)
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prepared_path = Path(tmp_dir) / "prepared"
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cfg = 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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"datasets": [
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{
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"path": str(tmp_ds_path),
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"type": "alpaca",
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},
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],
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}
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)
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dataset, _ = load_tokenized_prepared_datasets(
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self.tokenizer, cfg, prepared_path
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)
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assert len(dataset) == 2000
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assert "input_ids" in dataset.features
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assert "attention_mask" in dataset.features
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assert "labels" in dataset.features
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shutil.rmtree(tmp_ds_path)
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if __name__ == "__main__":
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unittest.main()
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