285 lines
8.5 KiB
Python
285 lines
8.5 KiB
Python
import json
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import os
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import numpy as np
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import torch
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from astrai.dataset.dataset import DatasetFactory
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from astrai.dataset.storage import save_h5
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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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# ============== JSON Storage Tests (raw text + tokenizer) ==============
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def _make_tokenizer_fn(tokenizer):
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"""Wrap tokenizer.encode() as a str -> List[int] callable."""
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return lambda text: tokenizer.encode(text, add_special_tokens=False)
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def test_seq_dataset_from_json_text(base_test_env):
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"""Test loading SEQ dataset from raw-text JSON with tokenizer"""
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tokenizer = base_test_env["tokenizer"]
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tokenizer_fn = _make_tokenizer_fn(tokenizer)
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test_dir = base_test_env["test_dir"]
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data_dir = os.path.join(test_dir, "json_text")
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os.makedirs(data_dir, exist_ok=True)
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texts = [
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"hello world this is a test sentence for tokenizer",
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"another sentence with different words and tokens",
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"machine learning is fascinating and powerful",
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]
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json_path = os.path.join(data_dir, "seq_data.json")
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with open(json_path, "w", encoding="utf-8") as f:
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json.dump({"sequence": texts}, f, ensure_ascii=False)
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dataset = DatasetFactory.load(
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train_type="seq",
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load_path=data_dir,
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window_size=16,
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tokenizer=tokenizer_fn,
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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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assert dataset.count > 0
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assert "sequence" in dataset.keys
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item = dataset[0]
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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[0] == 16
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def test_sft_dataset_from_json_text(base_test_env):
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"""Test loading SFT dataset from raw-text JSON with tokenizer"""
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tokenizer = base_test_env["tokenizer"]
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tokenizer_fn = _make_tokenizer_fn(tokenizer)
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test_dir = base_test_env["test_dir"]
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data_dir = os.path.join(test_dir, "json_sft")
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os.makedirs(data_dir, exist_ok=True)
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texts = [
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"user asks a question about the weather",
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"assistant provides a helpful response to the user",
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]
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json_path = os.path.join(data_dir, "sft_data.json")
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with open(json_path, "w", encoding="utf-8") as f:
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json.dump(
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{"sequence": texts, "loss_mask": texts},
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f,
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ensure_ascii=False,
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)
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dataset = DatasetFactory.load(
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train_type="sft",
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load_path=data_dir,
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window_size=16,
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tokenizer=tokenizer_fn,
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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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item = dataset[0]
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assert "loss_mask" in item
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def test_json_storage_explicit_tokenizer(base_test_env):
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"""Test explicit JSON storage with tokenizer"""
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tokenizer = base_test_env["tokenizer"]
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tokenizer_fn = _make_tokenizer_fn(tokenizer)
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test_dir = base_test_env["test_dir"]
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data_dir = os.path.join(test_dir, "json_explicit")
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os.makedirs(data_dir, exist_ok=True)
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texts = ["abcdefghijklmnopqrstuvwxyz" * 10]
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json_path = os.path.join(data_dir, "data.json")
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with open(json_path, "w", encoding="utf-8") as f:
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json.dump({"sequence": texts}, f, ensure_ascii=False)
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token_count = len(tokenizer_fn(texts[0]))
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dataset = DatasetFactory.load(
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train_type="seq",
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load_path=data_dir,
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window_size=32,
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storage_type="json",
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tokenizer=tokenizer_fn,
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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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assert dataset.count == token_count
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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(base_test_env):
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"""Test count returns 0 when no data is loaded"""
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from astrai.dataset.dataset import SEQDataset
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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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