fix : 修复存储层 bug，JSON 切换为 JSONL，补齐测试覆盖

- save_bin/load_bin: save_json/load_json 替换为直接 json.dump/json.load，修复致命 bug
- _normalize: 空 cum 列表 guard，防止 IndexError
- load_json: 改为仅支持 JSONL 逐行解析 (json.loads)，移除 .json 支持
- detect_format: 只匹配 *.jsonl，不再匹配 *.json
- save_json: 输出扩展名改为 .jsonl
- GRPODataset.__getitem__: 补齐 .to(dtype=torch.long/bool) 与其他数据集一致
- load_bin: np.memmap mode='r+' 消除 PyTorch 不可写 tensor 警告
- 新增 16 个测试: bin roundtrip, mmap load, 空 key, JSONL 多行/文本, GRPO dtype/load, detect_format bin/jsonl, fetch multi-key/越界, json_to_bin 转换, DPO from JSONL, 显式 storage_type

astrai/dataset/dataset.py

@@ -306,9 +306,11 @@ class GRPODataset(BaseDataset):
     def __getitem__(self, index: int) -> Dict[str, Tensor]:
         begin_idx, end_idx = self.get_index(index)
 
-        prompts = self._fetch_data(begin_idx, end_idx, "prompts")
+        prompts = self._fetch_data(begin_idx, end_idx, "prompts").to(dtype=torch.long)
-        responses = self._fetch_data(begin_idx, end_idx, "responses")
+        responses = self._fetch_data(begin_idx, end_idx, "responses").to(
-        masks = self._fetch_data(begin_idx, end_idx, "masks")
+            dtype=torch.long
+        )
+        masks = self._fetch_data(begin_idx, end_idx, "masks").to(dtype=torch.bool)
         rewards = self._fetch_data(begin_idx, end_idx, "rewards")
 
         return {

astrai/dataset/storage.py

@@ -70,7 +70,7 @@ def load_h5(file_path: str, share_memory=True) -> Dict[str, List[Tensor]]:
 
 def save_json(file_path: str, file_name: str, tensor_group: Dict[str, List[Tensor]]):
     os.makedirs(file_path, exist_ok=True)
-    full_file_path = os.path.join(file_path, f"{file_name}.json")
+    full_file_path = os.path.join(file_path, f"{file_name}.jsonl")
     json_data = {}
     for key, tensors in tensor_group.items():
         json_data[key] = [tensor.tolist() for tensor in tensors]
@@ -83,22 +83,26 @@ def load_json(
     share_memory: bool = True,
     tokenizer: Optional[Callable[[str], List[int]]] = None,
 ) -> Dict[str, List[Tensor]]:
-    """Load tensor data from JSON files.
+    """Load tensor data from JSONL files (one JSON object per line).
 
     Supports two modes:
-    - Pre-tokenized: JSON values are List[List[int]] (token IDs), loaded as-is.
+    - Pre-tokenized: values are List[List[int]] (token IDs), loaded as-is.
-    - Raw text: JSON values are List[str], tokenized via ``tokenizer`` callable
+    - Raw text: values are List[str], tokenized via ``tokenizer`` callable
       at load time.  A ``tokenizer`` receives a str and returns List[int].
 
     Non-data JSON files (e.g. config.json) with scalar/object values are
-    silently skipped.
+    silently skipped.  Empty lines are ignored.
     """
     tensor_group: Dict[str, List[Tensor]] = {}
     root_path = Path(file_path)
-    json_files = list(root_path.rglob("*.json")) + list(root_path.rglob("*.jsonl"))
+    jsonl_files = sorted(root_path.rglob("*.jsonl"))
-    for json_file in json_files:
+    for jsonl_file in jsonl_files:
-        with open(json_file, "r", encoding="utf-8") as f:
+        with open(jsonl_file, "r", encoding="utf-8") as f:
-            data = json.load(f)
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                data = json.loads(line)
                 if not isinstance(data, dict):
                     continue
                 for key, sequences in data.items():
@@ -125,17 +129,19 @@ def save_bin(file_path: str, tensor_group: Dict[str, List[Tensor]]):
         cat = torch.cat(tensors, dim=0)
         meta[key] = {"shape": list(cat.shape), "dtype": str(cat.dtype).split(".")[-1]}
         np.asarray(cat.cpu().numpy()).tofile(os.path.join(file_path, f"{key}.bin"))
-    save_json(meta, os.path.join(file_path, "meta.json"))
+    with open(os.path.join(file_path, "meta.json"), "w") as f:
+        json.dump(meta, f)
 
 
 def load_bin(file_path: str) -> Dict[str, List[Tensor]]:
-    meta = load_json(os.path.join(file_path, "meta.json"))
+    with open(os.path.join(file_path, "meta.json"), "r") as f:
+        meta = json.load(f)
     segments: Dict[str, List[Tensor]] = {}
     for key, info in meta.items():
         arr = np.memmap(
             os.path.join(file_path, f"{key}.bin"),
             dtype=info["dtype"],
-            mode="r",
+            mode="r+",
             shape=tuple(info["shape"]),
         )
         segments[key] = [torch.from_numpy(arr)]
@@ -167,7 +173,7 @@ def detect_format(load_path: str) -> str:
         suffix = root.suffix.lower()
         if suffix in (".h5", ".hdf5"):
             return "h5"
-        if suffix in (".json", ".jsonl"):
+        if suffix in (".jsonl"):
             return "json"
         raise ValueError(f"Unsupported file format: {suffix}")
 
@@ -177,8 +183,8 @@ def detect_format(load_path: str) -> str:
     bin_files = list(root.rglob("*.bin"))
     if bin_files and (root / "meta.json").exists():
         return "bin"
-    json_files = list(root.rglob("*.json")) + list(root.rglob("*.jsonl"))
+    jsonl_files = list(root.rglob("*.jsonl"))
-    if json_files:
+    if jsonl_files:
         return "json"
     raise FileNotFoundError(f"No supported data files found at {load_path}")
 
@@ -257,7 +263,11 @@ class Store(ABC):
                 total += t.shape[0]
                 cum.append(total)
             self._cum[key] = cum
-        self._length = min(cum[-1] for cum in self._cum.values()) if self._cum else 0
+        self._length = (
+            min((cum[-1] if cum else 0) for cum in self._cum.values())
+            if self._cum
+            else 0
+        )
 
 
 class StoreFactory(BaseFactory["Store"]):

tests/data/test_dataset.py

@@ -8,9 +8,13 @@ import torch
 from astrai.dataset.dataset import DatasetFactory, SEQDataset
 from astrai.dataset.storage import (
     H5Store,
+    MmapStore,
     StoreFactory,
     detect_format,
+    json_to_bin,
+    load_bin,
     load_json,
+    save_bin,
     save_h5,
 )
 
@@ -177,8 +181,8 @@ def test_seq_dataset_from_json_text(base_test_env):
         "machine learning is fascinating and powerful",
     ]
 
-    json_path = os.path.join(data_dir, "seq_data.json")
+    jsonl_path = os.path.join(data_dir, "seq_data.jsonl")
-    with open(json_path, "w", encoding="utf-8") as f:
+    with open(jsonl_path, "w", encoding="utf-8") as f:
         json.dump({"sequence": texts}, f, ensure_ascii=False)
 
     dataset = DatasetFactory.load(
@@ -211,8 +215,8 @@ def test_sft_dataset_from_json_text(base_test_env):
         "assistant provides a helpful response to the user",
     ]
 
-    json_path = os.path.join(data_dir, "sft_data.json")
+    jsonl_path = os.path.join(data_dir, "sft_data.jsonl")
-    with open(json_path, "w", encoding="utf-8") as f:
+    with open(jsonl_path, "w", encoding="utf-8") as f:
         json.dump(
             {"sequence": texts, "loss_mask": texts},
             f,
@@ -242,7 +246,7 @@ def test_json_storage_explicit_tokenizer(base_test_env):
 
     texts = ["abcdefghijklmnopqrstuvwxyz" * 10]
 
-    json_path = os.path.join(data_dir, "data.json")
+    json_path = os.path.join(data_dir, "data.jsonl")
     with open(json_path, "w", encoding="utf-8") as f:
         json.dump({"sequence": texts}, f, ensure_ascii=False)
 
@@ -342,7 +346,7 @@ def test_store_empty_data_len(base_test_env):
     data_dir = os.path.join(test_dir, "empty_store")
     os.makedirs(data_dir, exist_ok=True)
 
-    with open(os.path.join(data_dir, "data.json"), "w") as f:
+    with open(os.path.join(data_dir, "data.jsonl"), "w") as f:
         json.dump({"sequence": [[1, 2, 3]]}, f)
 
     store = StoreFactory.create("json")
@@ -388,7 +392,7 @@ def test_json_pretokenized_without_tokenizer(base_test_env):
     data_dir = os.path.join(test_dir, "json_pretok")
     os.makedirs(data_dir, exist_ok=True)
 
-    json_path = os.path.join(data_dir, "data.json")
+    json_path = os.path.join(data_dir, "data.jsonl")
     with open(json_path, "w", encoding="utf-8") as f:
         json.dump({"sequence": [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]}, f)
 
@@ -407,7 +411,7 @@ def test_load_json_skips_config_file(base_test_env):
     with open(os.path.join(test_dir, "config.json"), "w") as f:
         json.dump({"vocab_size": 1000, "dim": 16}, f)
 
-    with open(os.path.join(test_dir, "data.json"), "w") as f:
+    with open(os.path.join(test_dir, "data.jsonl"), "w") as f:
         json.dump({"sequence": [[1, 2, 3, 4, 5]]}, f)
 
     result = load_json(test_dir)
@@ -436,3 +440,265 @@ def test_store_multi_segment_concat(base_test_env):
     assert len(store) == 9
     result = store.fetch(2, 7, "sequence")
     assert result.tolist() == [3, 4, 5, 6, 7]
+
+
+def test_save_load_bin_roundtrip(base_test_env):
+    """save_bin + load_bin roundtrip preserves data"""
+    test_dir = base_test_env["test_dir"]
+
+    data = {
+        "sequence": [torch.tensor([1, 2, 3, 4, 5], dtype=torch.int64)],
+        "loss_mask": [torch.tensor([0, 1, 1, 0, 1], dtype=torch.int64)],
+    }
+    save_bin(test_dir, data)
+    result = load_bin(test_dir)
+
+    assert "sequence" in result
+    assert "loss_mask" in result
+    assert result["sequence"][0].tolist() == [1, 2, 3, 4, 5]
+    assert result["loss_mask"][0].tolist() == [0, 1, 1, 0, 1]
+
+
+def test_mmap_store_load_and_fetch(base_test_env):
+    """MmapStore loads bin data and fetches correctly"""
+    test_dir = base_test_env["test_dir"]
+
+    data = {
+        "sequence": [torch.randint(0, 1000, (200,), dtype=torch.int64)],
+    }
+    save_bin(test_dir, data)
+
+    store = StoreFactory.create("bin")
+    store.load(test_dir)
+    assert len(store) == 200
+    assert "sequence" in store.keys
+
+    result = store.fetch(10, 20, "sequence")
+    assert result.tolist() == data["sequence"][0][10:20].tolist()
+
+
+def test_mmap_dataset_load(base_test_env):
+    """DatasetFactory.load auto-detects bin format"""
+    test_dir = base_test_env["test_dir"]
+
+    data = {
+        "sequence": [torch.randint(0, 1000, (200,), dtype=torch.int64)],
+    }
+    save_bin(test_dir, data)
+
+    dataset = DatasetFactory.load("seq", test_dir, window_size=64)
+    assert len(dataset) > 0
+    assert dataset.count == 200
+    assert dataset[0]["input_ids"].shape[0] == 64
+
+
+def test_normalize_empty_key():
+    """_normalize with empty tensor list does not crash"""
+    store = H5Store()
+    store._normalize({"sequence": []})
+    assert len(store) == 0
+    assert store.keys == ["sequence"]
+
+
+def test_normalize_mixed_empty_key():
+    """_normalize with empty + non-empty keys returns min=0"""
+    store = H5Store()
+    store._normalize({"sequence": [torch.tensor([1, 2, 3])], "loss_mask": []})
+    assert len(store) == 0
+    assert set(store.keys) == {"sequence", "loss_mask"}
+
+
+def test_load_jsonl_multiline(base_test_env):
+    """JSONL files are loaded line-by-line and accumulated"""
+    test_dir = base_test_env["test_dir"]
+    data_dir = os.path.join(test_dir, "jsonl_test")
+    os.makedirs(data_dir, exist_ok=True)
+
+    jsonl_path = os.path.join(data_dir, "data.jsonl")
+    with open(jsonl_path, "w", encoding="utf-8") as f:
+        f.write('{"sequence": [[1, 2, 3]]}\n')
+        f.write('{"sequence": [[4, 5, 6]]}\n')
+        f.write('{"sequence": [[7, 8, 9]]}\n')
+
+    store = StoreFactory.create("json")
+    store.load(data_dir)
+    assert len(store) == 9
+    assert store.fetch(0, 9, "sequence").tolist() == [1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+
+def test_load_jsonl_with_text_and_tokenizer(base_test_env):
+    """JSONL with raw text + tokenizer works"""
+    tokenizer = base_test_env["tokenizer"]
+    tokenizer_fn = lambda text: tokenizer.encode(text, add_special_tokens=False)
+
+    test_dir = base_test_env["test_dir"]
+    data_dir = os.path.join(test_dir, "jsonl_text")
+    os.makedirs(data_dir, exist_ok=True)
+
+    jsonl_path = os.path.join(data_dir, "data.jsonl")
+    with open(jsonl_path, "w", encoding="utf-8") as f:
+        f.write('{"sequence": ["hello world how are you today this is a test"]}\n')
+
+    dataset = DatasetFactory.load(
+        "seq", data_dir, window_size=8, tokenizer=tokenizer_fn
+    )
+    assert len(dataset) > 0
+    assert dataset.count > 0
+
+
+def test_grpo_dataset_dtype(base_test_env):
+    """GRPODataset returns correct dtypes"""
+    test_dir = base_test_env["test_dir"]
+
+    seq_len = 100
+    data = {
+        "prompts": [torch.randint(0, 100, (seq_len,), dtype=torch.int32)],
+        "responses": [torch.randint(0, 100, (seq_len,), dtype=torch.int32)],
+        "masks": [torch.ones(seq_len, dtype=torch.int32)],
+        "rewards": [torch.ones(seq_len, dtype=torch.float32)],
+    }
+    save_h5(test_dir, "grpo_dtype", data)
+
+    dataset = DatasetFactory.load("grpo", test_dir, window_size=32)
+    item = dataset[0]
+
+    assert item["prompts"].dtype == torch.long
+    assert item["responses"].dtype == torch.long
+    assert item["masks"].dtype == torch.bool
+    assert item["rewards"].dtype == torch.float32
+
+
+def test_grpo_dataset_load(base_test_env):
+    """GRPODataset loads and returns correct keys"""
+    test_dir = base_test_env["test_dir"]
+    seq_len = 200
+    data = {
+        "prompts": [torch.randint(0, 1000, (seq_len,), dtype=torch.int64)],
+        "responses": [torch.randint(0, 1000, (seq_len,), dtype=torch.int64)],
+        "masks": [torch.ones(seq_len, dtype=torch.int64)],
+        "rewards": [torch.rand(seq_len, dtype=torch.float32)],
+    }
+    save_h5(test_dir, "grpo_test", data)
+
+    dataset = DatasetFactory.load("grpo", test_dir, window_size=64)
+    assert len(dataset) > 0
+    item = dataset[0]
+    assert "prompts" in item
+    assert "responses" in item
+    assert "masks" in item
+    assert "rewards" in item
+    assert item["prompts"].shape[0] == 64
+    assert item["responses"].shape[0] == 64
+
+
+def test_detect_format_bin_dir(base_test_env):
+    """detect_format returns 'bin' for directory with .bin + meta.json"""
+    test_dir = base_test_env["test_dir"]
+    save_bin(test_dir, {"sequence": [torch.randint(0, 100, (10,))]})
+    assert detect_format(test_dir) == "bin"
+
+
+def test_detect_format_jsonl_file(base_test_env):
+    """detect_format returns 'json' for a single .jsonl file"""
+    test_dir = base_test_env["test_dir"]
+    path = os.path.join(test_dir, "data.jsonl")
+    with open(path, "w") as f:
+        f.write('{"sequence": [[1,2,3]]}\n')
+    assert detect_format(path) == "json"
+
+
+def test_store_fetch_multi_key(base_test_env):
+    """Store.fetch with List[str] returns Dict[str, Tensor]"""
+    test_dir = base_test_env["test_dir"]
+    save_h5(
+        test_dir,
+        "multi_key",
+        {
+            "sequence": [torch.randint(0, 100, (100,), dtype=torch.int64)],
+            "loss_mask": [torch.ones(100, dtype=torch.int64)],
+        },
+    )
+
+    store = StoreFactory.create("h5")
+    store.load(test_dir)
+    result = store.fetch(10, 20, ["sequence", "loss_mask"])
+    assert isinstance(result, dict)
+    assert result["sequence"].shape[0] == 10
+    assert result["loss_mask"].shape[0] == 10
+
+
+def test_store_fetch_out_of_bounds(base_test_env):
+    """Store.fetch raises ValueError for out-of-bounds indices"""
+    test_dir = base_test_env["test_dir"]
+    save_h5(
+        test_dir, "bounds", {"sequence": [torch.randint(0, 100, (50,))]}
+    )
+
+    store = StoreFactory.create("h5")
+    store.load(test_dir)
+    with pytest.raises(ValueError, match="out of bounds"):
+        store.fetch(-1, 10, "sequence")
+    with pytest.raises(ValueError, match="out of bounds"):
+        store.fetch(0, 51, "sequence")
+    with pytest.raises(ValueError, match="out of bounds"):
+        store.fetch(50, 50, "sequence")
+
+
+def test_json_to_bin_roundtrip(base_test_env):
+    """json_to_bin converts JSONL to bin and data is preserved"""
+    test_dir = base_test_env["test_dir"]
+    jsonl_dir = os.path.join(test_dir, "src")
+    os.makedirs(jsonl_dir, exist_ok=True)
+
+    with open(os.path.join(jsonl_dir, "data.jsonl"), "w") as f:
+        f.write('{"sequence": [[1, 2, 3, 4, 5]]}\n')
+
+    bin_dir = os.path.join(test_dir, "out")
+    json_to_bin(jsonl_dir, bin_dir)
+
+    store = StoreFactory.create("bin")
+    store.load(bin_dir)
+    assert len(store) == 5
+    assert store.fetch(0, 5, "sequence").tolist() == [1, 2, 3, 4, 5]
+
+
+def test_dpo_dataset_from_jsonl(base_test_env):
+    """DPO dataset loaded from pre-tokenized JSONL"""
+    test_dir = base_test_env["test_dir"]
+    data_dir = os.path.join(test_dir, "dpo_jsonl")
+    os.makedirs(data_dir, exist_ok=True)
+
+    with open(os.path.join(data_dir, "dpo.jsonl"), "w") as f:
+        f.write(
+            json.dumps(
+                {
+                    "chosen": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] * 10],
+                    "rejected": [[10, 9, 8, 7, 6, 5, 4, 3, 2, 1] * 10],
+                    "chosen_mask": [[1] * 100],
+                    "rejected_mask": [[1] * 100],
+                }
+            )
+            + "\n"
+        )
+
+    dataset = DatasetFactory.load("dpo", data_dir, window_size=32)
+    assert len(dataset) > 0
+    item = dataset[0]
+    assert item["chosen"].dtype == torch.long
+    assert item["rejected"].dtype == torch.long
+    assert item["chosen_mask"].dtype == torch.bool
+    assert item["rejected_mask"].dtype == torch.bool
+
+
+def test_dataset_load_explicit_storage_type(base_test_env):
+    """DatasetFactory.load with explicit storage_type bypasses auto-detect"""
+    test_dir = base_test_env["test_dir"]
+    save_h5(
+        test_dir, "explicit", {"sequence": [torch.randint(0, 100, (200,))]}
+    )
+
+    dataset = DatasetFactory.load(
+        "seq", test_dir, window_size=64, storage_type="h5"
+    )
+    assert len(dataset) > 0
+    assert dataset.count == 200