444 lines
14 KiB
Python
444 lines
14 KiB
Python
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.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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load_bin,
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save_bin,
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save_h5,
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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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# Create multiple mmap dataset directories with random data
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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 = {
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"sequence": [
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torch.randint(0, 1000, (seq_length,), dtype=torch.int64)
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for _ in range(10)
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],
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}
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save_h5(test_dir, f"data_{i}", dummy_data)
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# Test loading with multiple paths
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loaded_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 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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# Create DPO-style data with memory mapping format
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seq_length = np.random.randint(100, 200)
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dummy_data = {
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"chosen": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
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"rejected": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
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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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save_h5(test_dir, "dpo_data", dummy_data)
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# Load DPO dataset
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dpo_dataset = DatasetFactory.load(
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train_type="dpo",
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load_path=test_dir,
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window_size=64,
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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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# Create SFT-style data with memory mapping format
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seq_length = np.random.randint(100, 200)
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dummy_data = {
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"sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
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"loss_mask": [torch.ones(seq_length, dtype=torch.bool)],
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}
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save_h5(test_dir, "sft_data", dummy_data)
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# Load SFT dataset
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sft_dataset = DatasetFactory.load(
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train_type="sft",
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load_path=test_dir,
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window_size=64,
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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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# Create test data
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seq_length = 200
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dummy_data = {
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"sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
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}
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save_h5(test_dir, "stride_test_data", dummy_data)
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# Test with custom stride
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custom_stride = 32
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dataset = DatasetFactory.load(
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train_type="seq", load_path=test_dir, window_size=64, stride=custom_stride
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)
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assert dataset is not None
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assert len(dataset) > 0
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# With stride 32 and window 64 on 200 length data, we should get more samples
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# than with default stride (which equals window size)
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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 the count property returns correct raw token count"""
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test_dir = base_test_env["test_dir"]
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seq_length = 200
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dummy_data = {
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"sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)],
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}
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save_h5(test_dir, "count_test_data", dummy_data)
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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 dataset.count == seq_length
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assert dataset.count > len(dataset) # raw tokens > windows
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assert len(dataset) == (seq_length - 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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"""Dataset shorter than window_size returns __len__ == 0"""
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test_dir = base_test_env["test_dir"]
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seq_length = 30
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save_h5(
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test_dir,
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"short",
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{"sequence": [torch.randint(0, 1000, (seq_length,), dtype=torch.int64)]},
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)
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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 == seq_length
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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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"""Store.fetch with begin == end returns empty tensor"""
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test_dir = base_test_env["test_dir"]
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dummy = {"sequence": [torch.randint(0, 1000, (100,), dtype=torch.int64)]}
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save_h5(test_dir, "empty_fetch", dummy)
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dataset = DatasetFactory.load("seq", test_dir, 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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"""MmapStore loads bin data and fetches correctly"""
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test_dir = base_test_env["test_dir"]
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data = {
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"sequence": [torch.randint(0, 1000, (200,), dtype=torch.int64)],
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}
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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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"""DatasetFactory.load auto-detects bin format"""
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test_dir = base_test_env["test_dir"]
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data = {
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"sequence": [torch.randint(0, 1000, (200,), dtype=torch.int64)],
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}
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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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"""GRPODataset returns correct dtypes"""
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test_dir = base_test_env["test_dir"]
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seq_len = 100
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data = {
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"prompts": [torch.randint(0, 100, (seq_len,), dtype=torch.int32)],
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"responses": [torch.randint(0, 100, (seq_len,), dtype=torch.int32)],
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"masks": [torch.ones(seq_len, dtype=torch.int32)],
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"rewards": [torch.ones(seq_len, dtype=torch.float32)],
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}
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save_h5(test_dir, "grpo_dtype", data)
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dataset = DatasetFactory.load("grpo", test_dir, window_size=32)
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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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"""GRPODataset loads and returns correct keys"""
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test_dir = base_test_env["test_dir"]
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seq_len = 200
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data = {
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"prompts": [torch.randint(0, 1000, (seq_len,), dtype=torch.int64)],
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"responses": [torch.randint(0, 1000, (seq_len,), dtype=torch.int64)],
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"masks": [torch.ones(seq_len, dtype=torch.int64)],
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"rewards": [torch.rand(seq_len, dtype=torch.float32)],
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}
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save_h5(test_dir, "grpo_test", data)
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dataset = DatasetFactory.load("grpo", test_dir, window_size=64)
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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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"""Store.fetch with List[str] returns Dict[str, Tensor]"""
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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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"""Store.fetch raises ValueError for out-of-bounds indices"""
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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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"""DatasetFactory.load with explicit storage_type bypasses auto-detect"""
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test_dir = base_test_env["test_dir"]
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save_h5(test_dir, "explicit", {"sequence": [torch.randint(0, 100, (200,))]})
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dataset = DatasetFactory.load("seq", test_dir, window_size=64, storage_type="h5")
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assert len(dataset) > 0
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assert dataset.count == 200
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