refactor: 删除数据流中的 JSONStore
- 移除 JSONStore 及相关函数，训练框架不再依赖 tokenizer - Store 层只保留 H5Store 和 MmapStore 两种后端
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+3
-262
@@ -11,9 +11,7 @@ from astrai.dataset.storage import (
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MmapStore,
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StoreFactory,
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detect_format,
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json_to_bin,
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load_bin,
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load_json,
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save_bin,
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save_h5,
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)
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@@ -159,111 +157,6 @@ def test_dataset_with_custom_stride(base_test_env):
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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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jsonl_path = os.path.join(data_dir, "seq_data.jsonl")
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with open(jsonl_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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jsonl_path = os.path.join(data_dir, "sft_data.jsonl")
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with open(jsonl_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.jsonl")
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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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@@ -338,25 +231,6 @@ def test_store_fetch_begin_equals_end(base_test_env):
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assert result.numel() == 0
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def test_store_empty_data_len(base_test_env):
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"""Store loaded with empty data has __len__ == 0"""
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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, "empty_store")
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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") as f:
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json.dump({"sequence": [[1, 2, 3]]}, f)
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store = StoreFactory.create("json")
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store.load(data_dir)
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assert len(store) > 0
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empty_store = H5Store()
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assert len(empty_store) == 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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@@ -386,40 +260,6 @@ def test_create_store_invalid_type():
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StoreFactory.create("parquet")
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def test_json_pretokenized_without_tokenizer(base_test_env):
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"""Pre-tokenized JSON (List[List[int]]) loads without tokenizer"""
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test_dir = base_test_env["test_dir"]
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data_dir = os.path.join(test_dir, "json_pretok")
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os.makedirs(data_dir, exist_ok=True)
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json_path = os.path.join(data_dir, "data.jsonl")
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with open(json_path, "w", encoding="utf-8") as f:
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json.dump({"sequence": [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]}, f)
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dataset = DatasetFactory.load("seq", data_dir, window_size=4, storage_type="json")
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assert len(dataset) > 0
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assert dataset.count == 10
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item = dataset[0]
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assert item["input_ids"].tolist() == [1, 2, 3, 4]
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assert item["target_ids"].tolist() == [2, 3, 4, 5]
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def test_load_json_skips_config_file(base_test_env):
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"""load_json skips scalar-value config files"""
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test_dir = base_test_env["test_dir"]
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with open(os.path.join(test_dir, "config.json"), "w") as f:
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json.dump({"vocab_size": 1000, "dim": 16}, f)
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with open(os.path.join(test_dir, "data.jsonl"), "w") as f:
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json.dump({"sequence": [[1, 2, 3, 4, 5]]}, f)
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result = load_json(test_dir)
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assert "sequence" in result
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assert "vocab_size" not in result
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assert len(result["sequence"]) == 1
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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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@@ -508,44 +348,6 @@ def test_normalize_mixed_empty_key():
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assert set(store.keys) == {"sequence", "loss_mask"}
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def test_load_jsonl_multiline(base_test_env):
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"""JSONL files are loaded line-by-line and accumulated"""
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test_dir = base_test_env["test_dir"]
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data_dir = os.path.join(test_dir, "jsonl_test")
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os.makedirs(data_dir, exist_ok=True)
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jsonl_path = os.path.join(data_dir, "data.jsonl")
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with open(jsonl_path, "w", encoding="utf-8") as f:
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f.write('{"sequence": [[1, 2, 3]]}\n')
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f.write('{"sequence": [[4, 5, 6]]}\n')
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f.write('{"sequence": [[7, 8, 9]]}\n')
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store = StoreFactory.create("json")
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store.load(data_dir)
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assert len(store) == 9
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assert store.fetch(0, 9, "sequence").tolist() == [1, 2, 3, 4, 5, 6, 7, 8, 9]
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def test_load_jsonl_with_text_and_tokenizer(base_test_env):
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"""JSONL with raw text + tokenizer works"""
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tokenizer = base_test_env["tokenizer"]
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tokenizer_fn = lambda text: tokenizer.encode(text, add_special_tokens=False)
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test_dir = base_test_env["test_dir"]
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data_dir = os.path.join(test_dir, "jsonl_text")
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os.makedirs(data_dir, exist_ok=True)
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jsonl_path = os.path.join(data_dir, "data.jsonl")
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with open(jsonl_path, "w", encoding="utf-8") as f:
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f.write('{"sequence": ["hello world how are you today this is a test"]}\n')
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dataset = DatasetFactory.load(
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"seq", data_dir, window_size=8, tokenizer=tokenizer_fn
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)
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assert len(dataset) > 0
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assert dataset.count > 0
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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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@@ -598,15 +400,6 @@ def test_detect_format_bin_dir(base_test_env):
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assert detect_format(test_dir) == "bin"
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def test_detect_format_jsonl_file(base_test_env):
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"""detect_format returns 'json' for a single .jsonl file"""
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test_dir = base_test_env["test_dir"]
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path = os.path.join(test_dir, "data.jsonl")
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with open(path, "w") as f:
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f.write('{"sequence": [[1,2,3]]}\n')
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assert detect_format(path) == "json"
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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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@@ -630,9 +423,7 @@ def test_store_fetch_multi_key(base_test_env):
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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(
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test_dir, "bounds", {"sequence": [torch.randint(0, 100, (50,))]}
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)
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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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@@ -644,61 +435,11 @@ def test_store_fetch_out_of_bounds(base_test_env):
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store.fetch(50, 50, "sequence")
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def test_json_to_bin_roundtrip(base_test_env):
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"""json_to_bin converts JSONL to bin and data is preserved"""
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test_dir = base_test_env["test_dir"]
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jsonl_dir = os.path.join(test_dir, "src")
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os.makedirs(jsonl_dir, exist_ok=True)
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with open(os.path.join(jsonl_dir, "data.jsonl"), "w") as f:
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f.write('{"sequence": [[1, 2, 3, 4, 5]]}\n')
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bin_dir = os.path.join(test_dir, "out")
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json_to_bin(jsonl_dir, bin_dir)
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store = StoreFactory.create("bin")
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store.load(bin_dir)
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assert len(store) == 5
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assert store.fetch(0, 5, "sequence").tolist() == [1, 2, 3, 4, 5]
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def test_dpo_dataset_from_jsonl(base_test_env):
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"""DPO dataset loaded from pre-tokenized JSONL"""
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test_dir = base_test_env["test_dir"]
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data_dir = os.path.join(test_dir, "dpo_jsonl")
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os.makedirs(data_dir, exist_ok=True)
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with open(os.path.join(data_dir, "dpo.jsonl"), "w") as f:
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f.write(
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json.dumps(
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{
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"chosen": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] * 10],
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"rejected": [[10, 9, 8, 7, 6, 5, 4, 3, 2, 1] * 10],
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"chosen_mask": [[1] * 100],
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"rejected_mask": [[1] * 100],
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}
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)
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+ "\n"
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)
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dataset = DatasetFactory.load("dpo", data_dir, window_size=32)
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assert len(dataset) > 0
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item = dataset[0]
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assert item["chosen"].dtype == torch.long
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assert item["rejected"].dtype == torch.long
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assert item["chosen_mask"].dtype == torch.bool
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assert item["rejected_mask"].dtype == torch.bool
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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(
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test_dir, "explicit", {"sequence": [torch.randint(0, 100, (200,))]}
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)
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save_h5(test_dir, "explicit", {"sequence": [torch.randint(0, 100, (200,))]})
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dataset = DatasetFactory.load(
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"seq", test_dir, window_size=64, storage_type="h5"
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)
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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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