624 lines
20 KiB
Python
624 lines
20 KiB
Python
import json
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import os
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import numpy as np
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import pytest
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import torch
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from astrai.config.preprocess_config import PipelineConfig
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from astrai.dataset.dataset import DatasetFactory, SEQDataset
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from astrai.dataset.storage import (
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H5Store,
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StoreFactory,
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detect_format,
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)
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from astrai.serialization import (
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load_bin,
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save_bin,
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save_h5,
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)
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def _rand_seq(length, vocab=1000):
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return torch.randint(0, vocab, (length,), dtype=torch.int64)
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def _save_test_tokenizer(test_dir, tokenizer):
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tokenizer_path = os.path.join(test_dir, "tokenizer")
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os.makedirs(tokenizer_path, exist_ok=True)
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tokenizer.save_pretrained(tokenizer_path)
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return tokenizer_path
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def _write_jsonl_dataset(test_dir, tokenizer_path, records, config_overrides=None):
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data_dir = os.path.join(test_dir, "jsonl_data")
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os.makedirs(data_dir, exist_ok=True)
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with open(os.path.join(data_dir, "data.jsonl"), "w", encoding="utf-8") as f:
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for record in records:
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f.write(json.dumps(record, ensure_ascii=False) + "\n")
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config = {
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"tokenizer_path": tokenizer_path,
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"version": 1,
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"input": {"sections": [{"field": "text", "action": "train"}]},
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"preprocessing": {"max_seq_len": 128},
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"output": {"position_ids_mode": "continuous"},
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}
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if config_overrides:
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config.update(config_overrides)
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with open(
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os.path.join(data_dir, "dataset_config.json"), "w", encoding="utf-8"
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) as f:
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json.dump(config, f, ensure_ascii=False, indent=2)
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return data_dir
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def _make_seq_dataset(
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test_dir, name="data", seq_length=200, train_type="seq", data=None, **load_kwargs
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):
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if data is None:
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data = {"sequence": [_rand_seq(seq_length)]}
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save_h5(test_dir, name, data)
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return DatasetFactory.load(
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train_type,
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test_dir,
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window_size=load_kwargs.pop("window_size", 64),
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**load_kwargs,
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)
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def test_dataset_loader_random_paths(base_test_env):
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"""Test dataset loader with multiple random paths"""
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test_dir = base_test_env["test_dir"]
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num_files = np.random.randint(2, 5)
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for i in range(num_files):
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seq_length = np.random.randint(200, 400)
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dummy_data = {"sequence": [_rand_seq(seq_length) for _ in range(10)]}
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loaded_dataset = _make_seq_dataset(
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test_dir, f"data_{i}", seq_length, data=dummy_data
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)
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assert loaded_dataset is not None
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assert len(loaded_dataset) > 0
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# Test that we can get items without errors
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for i in range(len(loaded_dataset)):
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item = loaded_dataset[i]
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assert "input_ids" in item
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assert "target_ids" in item
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assert item["input_ids"].shape == item["target_ids"].shape
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assert item["input_ids"].shape[0] == 64
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def test_dpo_strategy_with_random_data(base_test_env):
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"""Test DPO strategy with randomized preference data"""
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test_dir = base_test_env["test_dir"]
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seq_length = np.random.randint(100, 200)
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dummy_data = {
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"chosen": [_rand_seq(seq_length)],
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"rejected": [_rand_seq(seq_length)],
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"chosen_mask": [torch.ones(seq_length, dtype=torch.bool)],
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"rejected_mask": [torch.ones(seq_length, dtype=torch.bool)],
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}
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dpo_dataset = _make_seq_dataset(
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test_dir, "dpo_data", seq_length, train_type="dpo", data=dummy_data
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)
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assert dpo_dataset is not None
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assert dpo_dataset.storage is not None
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assert len(dpo_dataset) > 0
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# Test that we can get DPO items without errors
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for i in range(min(3, len(dpo_dataset))):
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item = dpo_dataset[i]
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assert "chosen" in item
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assert "rejected" in item
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assert "chosen_mask" in item
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assert "rejected_mask" in item
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assert item["chosen"].shape == item["rejected"].shape
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assert item["chosen_mask"].shape == item["rejected_mask"].shape
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def test_sft_dataset_with_random_data(base_test_env):
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"""Test SFT dataset with random data"""
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test_dir = base_test_env["test_dir"]
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seq_length = np.random.randint(100, 200)
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dummy_data = {
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"sequence": [_rand_seq(seq_length)],
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"loss_mask": [torch.ones(seq_length, dtype=torch.bool)],
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"position_ids": [torch.arange(seq_length, dtype=torch.int32)],
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}
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sft_dataset = _make_seq_dataset(
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test_dir, "sft_data", seq_length, train_type="sft", data=dummy_data
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)
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assert sft_dataset is not None
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assert sft_dataset.storage is not None
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assert len(sft_dataset) > 0
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# Test that we can get SFT items without errors
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for i in range(min(3, len(sft_dataset))):
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item = sft_dataset[i]
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assert "input_ids" in item
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assert "target_ids" in item
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assert "loss_mask" in item
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assert item["input_ids"].shape == item["target_ids"].shape
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assert item["loss_mask"].shape[0] == 64
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def test_dataset_with_custom_stride(base_test_env):
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"""Test dataset with custom stride parameter"""
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test_dir = base_test_env["test_dir"]
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custom_stride = 32
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dataset = _make_seq_dataset(test_dir, "stride_test_data", stride=custom_stride)
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assert dataset is not None
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assert len(dataset) > 0
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default_stride_dataset = DatasetFactory.load(
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train_type="seq",
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load_path=test_dir,
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window_size=64,
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)
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assert len(dataset) > len(default_stride_dataset)
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def test_dataset_count_property(base_test_env):
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test_dir = base_test_env["test_dir"]
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dataset = _make_seq_dataset(test_dir, "count_test_data")
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assert dataset.count == 200
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assert dataset.count > len(dataset)
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assert len(dataset) == (200 - 1 - 64) // 64 + 1
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def test_empty_dataset_count():
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"""Test count returns 0 when no data is loaded"""
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dataset = SEQDataset(window_size=64, stride=32)
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assert dataset.count == 0
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assert dataset.keys == []
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def test_dataset_too_short_for_window(base_test_env):
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test_dir = base_test_env["test_dir"]
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dataset = _make_seq_dataset(test_dir, "short", seq_length=30)
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assert len(dataset) == 0
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assert dataset.count == 30
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def test_unloaded_dataset_getitem_raises():
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"""__getitem__ without load() should fail clearly"""
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dataset = SEQDataset(window_size=64, stride=32)
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with pytest.raises(RuntimeError, match="not loaded"):
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dataset.get_index(0)
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def test_unloaded_dataset_len():
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"""__len__ without load() returns 0"""
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dataset = SEQDataset(window_size=64, stride=32)
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assert len(dataset) == 0
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def test_store_unloaded_len():
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"""Unloaded Store has __len__ == 0"""
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store = H5Store()
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assert len(store) == 0
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assert store.keys == []
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def test_store_fetch_begin_equals_end(base_test_env):
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test_dir = base_test_env["test_dir"]
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dataset = _make_seq_dataset(test_dir, "empty_fetch", seq_length=100, window_size=32)
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result = dataset.storage.fetch(10, 10, "sequence")
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assert result.numel() == 0
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def test_store_fetch_before_load():
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"""Store.fetch before load raises RuntimeError"""
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store = H5Store()
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with pytest.raises(RuntimeError, match="not loaded"):
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store.fetch(0, 10, "sequence")
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def test_detect_format_nonexistent_path():
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"""detect_format raises FileNotFoundError for bad path"""
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with pytest.raises(FileNotFoundError, match="No supported"):
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detect_format("/nonexistent/path/xyz")
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def test_detect_format_unsupported_file(base_test_env):
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"""detect_format raises ValueError for unsupported file extension"""
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test_dir = base_test_env["test_dir"]
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path = os.path.join(test_dir, "data.txt")
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with open(path, "w") as f:
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f.write("hello")
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with pytest.raises(ValueError, match="Unsupported"):
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detect_format(path)
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def test_create_store_invalid_type():
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"""StoreFactory.create raises ValueError for unknown type"""
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with pytest.raises(ValueError, match="Unknown component"):
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StoreFactory.create("parquet")
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def test_store_multi_segment_concat(base_test_env):
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"""Multi-segment H5 data is concatenated into single tensor at load time"""
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import os
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test_dir = base_test_env["test_dir"]
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data_dir = os.path.join(test_dir, "multi_seg")
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os.makedirs(data_dir, exist_ok=True)
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segs = [
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torch.tensor([1, 2, 3]),
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torch.tensor([4, 5, 6, 7]),
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torch.tensor([8, 9]),
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]
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save_h5(data_dir, "data", {"sequence": segs})
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store = StoreFactory.create("h5")
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store.load(data_dir)
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assert len(store) == 9
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result = store.fetch(2, 7, "sequence")
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assert result.tolist() == [3, 4, 5, 6, 7]
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def test_save_load_bin_roundtrip(base_test_env):
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"""save_bin + load_bin roundtrip preserves data"""
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test_dir = base_test_env["test_dir"]
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data = {
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"sequence": [torch.tensor([1, 2, 3, 4, 5], dtype=torch.int64)],
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"loss_mask": [torch.tensor([0, 1, 1, 0, 1], dtype=torch.int64)],
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}
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save_bin(test_dir, data)
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result = load_bin(test_dir)
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assert "sequence" in result
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assert "loss_mask" in result
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assert result["sequence"][0].tolist() == [1, 2, 3, 4, 5]
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assert result["loss_mask"][0].tolist() == [0, 1, 1, 0, 1]
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def test_mmap_store_load_and_fetch(base_test_env):
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test_dir = base_test_env["test_dir"]
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data = {"sequence": [_rand_seq(200)]}
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save_bin(test_dir, data)
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store = StoreFactory.create("bin")
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store.load(test_dir)
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assert len(store) == 200
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assert "sequence" in store.keys
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result = store.fetch(10, 20, "sequence")
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assert result.tolist() == data["sequence"][0][10:20].tolist()
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def test_mmap_dataset_load(base_test_env):
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test_dir = base_test_env["test_dir"]
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data = {"sequence": [_rand_seq(200)]}
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save_bin(test_dir, data)
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dataset = DatasetFactory.load("seq", test_dir, window_size=64)
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assert len(dataset) > 0
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assert dataset.count == 200
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assert dataset[0]["input_ids"].shape[0] == 64
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def test_normalize_empty_key():
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"""_normalize with empty tensor list does not crash"""
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store = H5Store()
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store._normalize({"sequence": []})
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assert len(store) == 0
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assert store.keys == ["sequence"]
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def test_normalize_mixed_empty_key():
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"""_normalize with empty + non-empty keys returns min=0"""
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store = H5Store()
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store._normalize({"sequence": [torch.tensor([1, 2, 3])], "loss_mask": []})
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assert len(store) == 0
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assert set(store.keys) == {"sequence", "loss_mask"}
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def test_grpo_dataset_dtype(base_test_env):
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test_dir = base_test_env["test_dir"]
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dummy_data = {
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"prompts": [torch.randint(0, 100, (100,), dtype=torch.int32)],
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"responses": [torch.randint(0, 100, (100,), dtype=torch.int32)],
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"masks": [torch.ones(100, dtype=torch.int32)],
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"rewards": [torch.ones(100, dtype=torch.float32)],
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}
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dataset = _make_seq_dataset(
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test_dir, "grpo_dtype", train_type="grpo", data=dummy_data, window_size=32
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)
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item = dataset[0]
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assert item["prompts"].dtype == torch.long
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assert item["responses"].dtype == torch.long
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assert item["masks"].dtype == torch.bool
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assert item["rewards"].dtype == torch.float32
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def test_grpo_dataset_load(base_test_env):
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test_dir = base_test_env["test_dir"]
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dummy_data = {
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"prompts": [_rand_seq(200)],
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"responses": [_rand_seq(200)],
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"masks": [torch.ones(200, dtype=torch.int64)],
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"rewards": [torch.rand(200, dtype=torch.float32)],
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}
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dataset = _make_seq_dataset(
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test_dir, "grpo_test", train_type="grpo", data=dummy_data
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)
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assert len(dataset) > 0
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item = dataset[0]
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assert "prompts" in item
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assert "responses" in item
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assert "masks" in item
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assert "rewards" in item
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assert item["prompts"].shape[0] == 64
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assert item["responses"].shape[0] == 64
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def test_detect_format_bin_dir(base_test_env):
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"""detect_format returns 'bin' for directory with .bin + meta.json"""
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test_dir = base_test_env["test_dir"]
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save_bin(test_dir, {"sequence": [torch.randint(0, 100, (10,))]})
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assert detect_format(test_dir) == "bin"
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def test_store_fetch_multi_key(base_test_env):
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test_dir = base_test_env["test_dir"]
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save_h5(
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test_dir,
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"multi_key",
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{
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"sequence": [torch.randint(0, 100, (100,), dtype=torch.int64)],
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"loss_mask": [torch.ones(100, dtype=torch.int64)],
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},
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)
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store = StoreFactory.create("h5")
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store.load(test_dir)
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result = store.fetch(10, 20, ["sequence", "loss_mask"])
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assert isinstance(result, dict)
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assert result["sequence"].shape[0] == 10
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assert result["loss_mask"].shape[0] == 10
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def test_store_fetch_out_of_bounds(base_test_env):
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test_dir = base_test_env["test_dir"]
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save_h5(test_dir, "bounds", {"sequence": [torch.randint(0, 100, (50,))]})
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store = StoreFactory.create("h5")
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store.load(test_dir)
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with pytest.raises(ValueError, match="out of bounds"):
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store.fetch(-1, 10, "sequence")
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with pytest.raises(ValueError, match="out of bounds"):
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store.fetch(0, 51, "sequence")
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with pytest.raises(ValueError, match="out of bounds"):
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store.fetch(50, 50, "sequence")
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def test_dataset_load_explicit_storage_type(base_test_env):
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test_dir = base_test_env["test_dir"]
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dataset = _make_seq_dataset(test_dir, "explicit", storage_type="h5")
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assert len(dataset) > 0
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assert dataset.count == 200
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def _write_json_dataset(test_dir, tokenizer_path, records, config_overrides=None):
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"""Write JSON (not JSONL) dataset — array of objects."""
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data_dir = os.path.join(test_dir, "json_data")
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os.makedirs(data_dir, exist_ok=True)
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with open(os.path.join(data_dir, "data.json"), "w", encoding="utf-8") as f:
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json.dump(records, f, ensure_ascii=False)
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config = {
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"tokenizer_path": tokenizer_path,
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"version": 1,
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"input": {"sections": [{"field": "text", "action": "train"}]},
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"preprocessing": {"max_seq_len": 128, "min_chars": 0},
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"output": {"position_ids_mode": "continuous"},
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}
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if config_overrides:
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config.update(config_overrides)
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with open(
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os.path.join(data_dir, "dataset_config.json"), "w", encoding="utf-8"
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) as f:
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json.dump(config, f, ensure_ascii=False, indent=2)
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return data_dir
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def test_detect_format_jsonl_dir(base_test_env):
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test_dir = base_test_env["test_dir"]
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tokenizer_path = _save_test_tokenizer(test_dir, base_test_env["tokenizer"])
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data_dir = _write_jsonl_dataset(
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test_dir,
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tokenizer_path,
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[{"text": "hello world"}, {"text": "foo bar baz"}],
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)
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assert detect_format(data_dir) == "jsonl"
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def test_detect_format_json_dir(base_test_env):
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"""detect_format returns 'jsonl' for directory with .json files."""
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test_dir = base_test_env["test_dir"]
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tokenizer_path = _save_test_tokenizer(test_dir, base_test_env["tokenizer"])
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data_dir = _write_json_dataset(
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test_dir,
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tokenizer_path,
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[{"text": "hello world"}, {"text": "foo bar baz qux"}],
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)
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assert detect_format(data_dir) == "jsonl"
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def test_json_store_seq(base_test_env):
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"""JsonlStore loads .json array correctly."""
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test_dir = base_test_env["test_dir"]
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tokenizer_path = _save_test_tokenizer(test_dir, base_test_env["tokenizer"])
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data_dir = _write_json_dataset(
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test_dir,
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tokenizer_path,
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[{"text": "hello world"}, {"text": "foo bar baz qux"}],
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)
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store = StoreFactory.create("jsonl")
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store.load(data_dir)
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assert len(store) > 0
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assert "sequence" in store.keys
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dataset = DatasetFactory.load("seq", data_dir, window_size=8)
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assert len(dataset) > 0
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item = dataset[0]
|
|
assert "input_ids" in item
|
|
assert "target_ids" in item
|
|
|
|
|
|
def test_json_store_no_tokenizer_path(base_test_env):
|
|
"""JsonlStore uses dataset dir as tokenizer_path when omitted."""
|
|
test_dir = base_test_env["test_dir"]
|
|
tokenizer = base_test_env["tokenizer"]
|
|
tokenizer.set_chat_template(
|
|
"{% for message in messages %}{{ message['role'] }}:{{ message['content'] }}\n{% endfor %}"
|
|
)
|
|
|
|
data_dir = os.path.join(test_dir, "self_contained")
|
|
os.makedirs(data_dir, exist_ok=True)
|
|
|
|
# Save tokenizer files directly in the dataset directory
|
|
tokenizer.save_pretrained(data_dir)
|
|
|
|
# Write .json data
|
|
records = [
|
|
{
|
|
"messages": [
|
|
{"role": "user", "content": "hi"},
|
|
{"role": "assistant", "content": "hello"},
|
|
]
|
|
}
|
|
]
|
|
with open(os.path.join(data_dir, "data.json"), "w", encoding="utf-8") as f:
|
|
json.dump(records, f, ensure_ascii=False)
|
|
|
|
# dataset_config.json WITHOUT tokenizer_path
|
|
config = {
|
|
"version": 1,
|
|
"input": {
|
|
"sections": [{"field": "messages", "action": "$role", "template": True}]
|
|
},
|
|
"mask": {"user": "mask", "assistant": "train"},
|
|
"mask_default": "mask",
|
|
"preprocessing": {"max_seq_len": 128, "min_chars": 0},
|
|
"output": {"position_ids_mode": "continuous"},
|
|
}
|
|
with open(
|
|
os.path.join(data_dir, "dataset_config.json"), "w", encoding="utf-8"
|
|
) as f:
|
|
json.dump(config, f, ensure_ascii=False, indent=2)
|
|
|
|
store = StoreFactory.create("jsonl")
|
|
store.load(data_dir)
|
|
assert len(store) > 0
|
|
assert "sequence" in store.keys
|
|
assert "loss_mask" in store.keys
|
|
|
|
|
|
def test_jsonl_store_seq(base_test_env):
|
|
test_dir = base_test_env["test_dir"]
|
|
tokenizer_path = _save_test_tokenizer(test_dir, base_test_env["tokenizer"])
|
|
data_dir = _write_jsonl_dataset(
|
|
test_dir,
|
|
tokenizer_path,
|
|
[{"text": "hello world"}, {"text": "foo bar baz qux"}],
|
|
config_overrides={"preprocessing": {"max_seq_len": 128, "min_chars": 0}},
|
|
)
|
|
|
|
store = StoreFactory.create("jsonl")
|
|
store.load(data_dir)
|
|
assert len(store) > 0
|
|
assert "sequence" in store.keys
|
|
|
|
dataset = DatasetFactory.load("seq", data_dir, window_size=8)
|
|
assert len(dataset) > 0
|
|
item = dataset[0]
|
|
assert "input_ids" in item
|
|
assert "target_ids" in item
|
|
assert item["input_ids"].dtype == torch.long
|
|
|
|
|
|
def test_jsonl_store_sft(base_test_env):
|
|
test_dir = base_test_env["test_dir"]
|
|
tokenizer = base_test_env["tokenizer"]
|
|
tokenizer.set_chat_template(
|
|
"{% for message in messages %}{{ message['role'] }}:{{ message['content'] }}\n{% endfor %}"
|
|
)
|
|
tokenizer_path = _save_test_tokenizer(test_dir, tokenizer)
|
|
data_dir = _write_jsonl_dataset(
|
|
test_dir,
|
|
tokenizer_path,
|
|
[
|
|
{
|
|
"messages": [
|
|
{"role": "system", "content": "sys"},
|
|
{"role": "user", "content": "hi"},
|
|
{"role": "assistant", "content": "hello"},
|
|
]
|
|
}
|
|
],
|
|
config_overrides={
|
|
"input": {
|
|
"sections": [{"field": "messages", "action": "$role", "template": True}]
|
|
},
|
|
"mask": {"system": "mask", "user": "mask", "assistant": "train"},
|
|
"mask_default": "mask",
|
|
},
|
|
)
|
|
|
|
store = StoreFactory.create("jsonl")
|
|
store.load(data_dir)
|
|
assert "sequence" in store.keys
|
|
assert "loss_mask" in store.keys
|
|
assert "position_ids" in store.keys
|
|
|
|
dataset = DatasetFactory.load("sft", data_dir, window_size=8)
|
|
item = dataset[0]
|
|
assert "input_ids" in item
|
|
assert "target_ids" in item
|
|
assert "loss_mask" in item
|
|
assert "position_ids" in item
|
|
assert item["loss_mask"].dtype == torch.bool
|
|
|
|
|
|
def test_jsonl_store_pipeline_config_roundtrip(base_test_env):
|
|
test_dir = base_test_env["test_dir"]
|
|
config_path = os.path.join(test_dir, "dataset_config.json")
|
|
with open(config_path, "w", encoding="utf-8") as f:
|
|
json.dump(
|
|
{
|
|
"tokenizer_path": os.path.join(test_dir, "tokenizer"),
|
|
"version": 1,
|
|
"input": {"sections": [{"field": "text", "action": "train"}]},
|
|
"mask": {"assistant": "train"},
|
|
"preprocessing": {"max_seq_len": 64},
|
|
"output": {"position_ids_mode": "doc_reset"},
|
|
},
|
|
f,
|
|
ensure_ascii=False,
|
|
indent=2,
|
|
)
|
|
|
|
with open(config_path, "r", encoding="utf-8") as f:
|
|
raw = json.load(f)
|
|
raw.pop("tokenizer_path")
|
|
config = PipelineConfig.from_dict(raw)
|
|
assert config.output.position_ids_mode == "doc_reset"
|
|
assert config.preprocessing.max_seq_len == 64
|