refactor: break JsonlStore→preprocessing circular dependency

- move JSONL transform auto-creation from JsonlStore.load to DatasetFactory.load via _build_jsonl_transform helper
- remove TokenizeTransform and PipelineConfig imports from storage module
- JsonlStore.load now requires explicit transform= for eager mode
- DatasetFactory.load remains the public API with identical convenience behavior

astrai/dataset/dataset.py

@@ -25,20 +25,50 @@ function (pure ``record -> Dict[str, Tensor]``) is forwarded to
 
 from abc import ABC, abstractmethod
 from functools import partial
+from pathlib import Path
 from typing import Callable, Dict, List, Optional
 
 import torch
 from torch import Tensor
 from torch.utils.data import Dataset
 
+from astrai.config.preprocess_config import PipelineConfig
 from astrai.dataset.storage import (
     Store,
     StoreFactory,
     detect_format,
 )
 from astrai.factory import BaseFactory
+from astrai.preprocessing.transform import TokenizeTransform
 from astrai.tokenize import AutoTokenizer
 
+_DEFAULT_MESSAGES_CONFIG = {
+    "version": 1,
+    "input": {"sections": [{"field": "messages", "action": "$role", "template": True}]},
+    "mask": {"system": "mask", "user": "mask", "assistant": "train"},
+    "mask_default": "mask",
+    "output": {"position_ids_mode": "doc_reset"},
+}
+
+
+def _build_jsonl_transform(
+    path: str, tokenizer_path: Optional[str] = None
+) -> Optional["TokenizeTransform"]:
+    """Auto-build a TokenizeTransform for JSONL eager loading.
+
+    Reads ``dataset_config.json`` from the data dir if present, or
+    falls back to the built-in chatml SFT config when *tokenizer_path*
+    is provided.
+    """
+    root = Path(path)
+    config_path = root / "dataset_config.json" if root.is_dir() else None
+    if config_path is not None and config_path.exists():
+        return TokenizeTransform.from_config_file(str(config_path))
+    if tokenizer_path:
+        config = PipelineConfig.from_dict(_DEFAULT_MESSAGES_CONFIG)
+        return TokenizeTransform(config, tokenizer_path)
+    return None
+
 
 def dpo_tokenize(
     record: dict,
@@ -349,16 +379,18 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
         )
         if processor is not None:
             store.load(load_path, processor=processor, **kwargs)
+        elif storage_type == "jsonl":
+            transform = _build_jsonl_transform(load_path, tokenizer_path)
+            if transform is None:
+                raise FileNotFoundError(
+                    f"JSONL dataset config not found. Expected "
+                    f"dataset_config.json alongside *.jsonl files, pass "
+                    f"tokenizer_path= for the built-in messages config, or "
+                    f"use processor= for lazy on-the-fly tokenisation."
+                )
+            store.load(load_path, transform=transform, **kwargs)
         else:
-            load_kwargs = dict(kwargs)
+            store.load(load_path, **kwargs)
-            if (
-                tokenizer_path is not None
-                and storage_type == "jsonl"
-                and train_type in ("seq", "sft")
-                and "tokenizer_path" not in load_kwargs
-            ):
-                load_kwargs["tokenizer_path"] = tokenizer_path
-            store.load(load_path, **load_kwargs)
 
         return cls.create(train_type, store=store)
 

astrai/dataset/storage.py

@@ -55,9 +55,7 @@ from typing import Callable, Dict, List, Optional, Tuple, Union
 import torch
 from torch import Tensor
 
-from astrai.config.preprocess_config import PipelineConfig
 from astrai.factory import BaseFactory
-from astrai.preprocessing.transform import TokenizeTransform
 from astrai.serialization import (
     load_bin,
     load_bin_offsets,
@@ -536,19 +534,8 @@ class JsonlStore(Store, Streamable, Recordable):
       ``len(store)`` returns ``num_records``; stream primitives raise.
     """
 
-    CONFIG_NAME = "dataset_config.json"
     segments_are_records = True
 
-    _DEFAULT_MESSAGES_CONFIG = {
-        "version": 1,
-        "input": {
-            "sections": [{"field": "messages", "action": "$role", "template": True}]
-        },
-        "mask": {"system": "mask", "user": "mask", "assistant": "train"},
-        "mask_default": "mask",
-        "output": {"position_ids_mode": "doc_reset"},
-    }
-
     def __init__(
         self,
         window_size: int = 0,
@@ -569,22 +556,10 @@ class JsonlStore(Store, Streamable, Recordable):
             return
 
         if transform is None:
-            root = Path(path)
+            raise ValueError(
-            config_path = root / self.CONFIG_NAME if root.is_dir() else None
+                "JsonlStore eager mode requires transform=. "
-            if config_path is not None and config_path.exists():
+                "Use DatasetFactory.load() which auto-constructs it."
-                transform = TokenizeTransform.from_config_file(str(config_path))
-            else:
-                tokenizer_path = kwargs.get("tokenizer_path")
-                if not tokenizer_path:
-                    raise FileNotFoundError(
-                        f"JSONL dataset config not found. Expected "
-                        f"{self.CONFIG_NAME} alongside *.jsonl files, pass an "
-                        f"explicit transform, pass processor= for lazy "
-                        f"on-the-fly tokenisation, or pass tokenizer_path= to "
-                        f"use the built-in messages config."
             )
-                config = PipelineConfig.from_dict(self._DEFAULT_MESSAGES_CONFIG)
-                transform = TokenizeTransform(config, tokenizer_path)
 
         transformed = transform.apply(records)
         self._normalize(transformed)

tests/data/test_dataset.py

@@ -9,6 +9,7 @@ import torch
 from astrai.dataset.dataset import (
     DatasetFactory,
     GRPODataset,
+    _build_jsonl_transform,
     dpo_tokenize,
     grpo_collate_fn,
 )
@@ -525,7 +526,7 @@ def test_json_store_seq(base_test_env):
     )
 
     store = StoreFactory.create("jsonl")
-    store.load(data_dir)
+    store.load(data_dir, transform=_build_jsonl_transform(data_dir))
     assert len(store) > 0
     assert "sequence" in store.keys
 
@@ -580,7 +581,7 @@ def test_json_store_no_tokenizer_path(base_test_env):
         json.dump(config, f, ensure_ascii=False, indent=2)
 
     store = StoreFactory.create("jsonl")
-    store.load(data_dir)
+    store.load(data_dir, transform=_build_jsonl_transform(data_dir))
     assert len(store) > 0
     assert "sequence" in store.keys
     assert "loss_mask" in store.keys
@@ -597,7 +598,7 @@ def test_jsonl_store_seq(base_test_env):
     )
 
     store = StoreFactory.create("jsonl")
-    store.load(data_dir)
+    store.load(data_dir, transform=_build_jsonl_transform(data_dir))
     assert len(store) > 0
     assert "sequence" in store.keys
 
@@ -638,7 +639,7 @@ def test_jsonl_store_sft(base_test_env):
     )
 
     store = StoreFactory.create("jsonl")
-    store.load(data_dir)
+    store.load(data_dir, transform=_build_jsonl_transform(data_dir))
     assert "sequence" in store.keys
     assert "loss_mask" in store.keys
     assert "position_ids" in store.keys
@@ -1085,7 +1086,7 @@ def test_jsonl_store_eager_len_returns_token_count(base_test_env):
     )
 
     store = JsonlStore()
-    store.load(data_dir)
+    store.load(data_dir, transform=_build_jsonl_transform(data_dir))
 
     assert store.num_records == 2
     assert len(store.keys) > 0