chore: bump to 1.3.10

- Store owns window_size/stride and __getitem__/__len__/sample_window
- Dataset classes become thin delegators binding a Store to a train-type key mapping
- Drop BaseDataset.get_index and the RecordDataset中间类 (window死代码)
- DatasetFactory forces window_size=0 for record datasets so record semantics never get window-tainted
- token_count/num_records split the legacy len() semantics (raw stream length vs record count)
- Update tests to the new .store/.token_count API and window/record mode switching

README.md

@@ -20,7 +20,7 @@
   <a href="assets/docs/README-zh-CN.md">中文</a> •
   <a href="https://github.com/ViperEkura/AstrAI/issues">Issue Tracker</a> •
   <a href="https://github.com/ViperEkura/AstrAI/discussions">Discussions</a> •
-  <a href="https://huggingface.co/ViperEk/">HuggingFace</a>
+  <a href="https://huggingface.co/ViperEkura">HuggingFace</a>
 </div>
 
 <br>
@@ -241,7 +241,7 @@ For major changes, please open an issue first to discuss what you would like to
 
 - **GitHub Issues**: [Issue Tracker](https://github.com/ViperEkura/AstrAI/issues)
 - **Discussions**: [GitHub Discussions](https://github.com/ViperEkura/AstrAI/discussions)
-- **HuggingFace**: [Model Hub](https://huggingface.co/ViperEk)
+- **HuggingFace**: [Model Hub](https://huggingface.co/ViperEkura)
 
 ### License
 

assets/docs/README-zh-CN.md

@@ -27,7 +27,7 @@
   <a href="#chinese">中文</a> •
   <a href="https://github.com/ViperEkura/AstrAI/issues">问题追踪</a> •
   <a href="https://github.com/ViperEkura/AstrAI/discussions">讨论区</a> •
-  <a href="https://huggingface.co/ViperEk">HuggingFace</a>
+  <a href="https://huggingface.co/ViperEkura">HuggingFace</a>
 </div>
 <br>
 
@@ -247,7 +247,7 @@ SSE 流式格式、错误码和统计端点详见[推理文档](./inference.md)
 
 - **GitHub Issues**: [问题追踪](https://github.com/ViperEkura/AstrAI/issues)
 - **Discussions**: [GitHub 讨论区](https://github.com/ViperEkura/AstrAI/discussions)
-- **HuggingFace**: [模型中心](https://huggingface.co/ViperEk)
+- **HuggingFace**: [模型中心](https://huggingface.co/ViperEkura)
 
 ### 许可证
 

assets/docs/architecture.md

@@ -117,7 +117,7 @@ classDiagram
             +int n_epoch
             +int batch_per_device
             +int grad_accum_steps
-            +float max_grad_norm
+            +Optional[float] max_grad_norm
             +list gradient_checkpointing_modules
             +int start_epoch
             +int start_samples
@@ -166,6 +166,13 @@ classDiagram
             +__getitem__(index) Dict
         }
 
+        class RecordDataset {
+            +Optional[Callable] processor
+            +load(load_path, storage_type)
+            +__getitem__(index)
+            +__len__()
+        }
+
         class DPODataset {
             +__getitem__(index) Dict
         }
@@ -177,13 +184,26 @@ classDiagram
         class Store {
             +Dict[str, List[Tensor]] _data
             +Dict[str, List[int]] _cum
+            +Dict[str, List[int]] _offsets
             +int _length
+            +int _num_records
             +keys (property)
             +load(path)
-            +fetch(begin, end, keys)
             +__len__()
-            -_fetch_key(key, begin, end) Tensor
-            -_normalize(raw)
+            -_normalize(raw, offsets)
+        }
+
+        class Streamable {
+            <<mixin>>
+            +fetch(begin, end, keys)
+            -_fetch_stream_key(key, begin, end) Tensor
+        }
+
+        class Recordable {
+            <<mixin>>
+            +num_records (property)
+            +fetch_record(index, keys)
+            -_fetch_record_key(key, index) Tensor
         }
 
         class H5Store {
@@ -195,6 +215,13 @@ classDiagram
             +load(path)
         }
 
+        class JsonlStore {
+            +JsonlSource _source
+            +Callable _processor
+            +load(path, transform, processor)
+            +fetch_record(index, keys)
+        }
+
         class ResumableDistributedSampler {
             +int epoch
             +int iter
@@ -210,7 +237,7 @@ classDiagram
             +Dict _entries
             +register(name) decorator
             +create(train_type, window_size, stride) BaseDataset
-            +load(train_type, load_path, window_size, stride, storage_type) BaseDataset
+            +load(train_type, load_path, window_size, stride, storage_type, tokenizer_path, max_len, store) BaseDataset
         }
     }
 
@@ -378,6 +405,7 @@ classDiagram
             +List[str] paths
             +str output_dir
             +str tokenizer_path
+            +AutoTokenizer tokenizer
             +BaseMaskBuilder mask_builder
             +PackingStrategy _packer
             +PositionIdStrategy _position_id
@@ -385,6 +413,18 @@ classDiagram
             +transform(item) Optional[dict]
             +run()
             +_flush(domains, shard_idx)
+            +_inject_doc_reset_position_ids(keys, mode, seqs) Dict
+            +_inject_continuous_position_ids(tensors, mode, seqs) Dict
+            +_to_tensors(keys) Dict
+        }
+
+        class TokenizeTransform {
+            +PipelineConfig config
+            +AutoTokenizer tokenizer
+            +BaseMaskBuilder mask_builder
+            +PositionIdStrategy position_strategy
+            +from_config_file(path) TokenizeTransform
+            +apply(records) Dict[str, list]
         }
     }
 
@@ -495,13 +535,13 @@ classDiagram
         }
 
         class GRPOStrategy {
+            +nn.Module old_model
             +nn.Module ref_model
             +float clip_eps
             +float kl_coef
             +int group_size
-            +int sync_interval
             +compute_loss(batch) Tensor
-            +sync_ref_model()
+            +sync_old_model()
         }
 
         class BaseScheduler {
@@ -551,7 +591,7 @@ classDiagram
         }
 
         class GradientClippingCallback {
-            +float max_grad_norm
+            +Optional[float] max_grad_norm
             +on_optimizer_step(context)
         }
 
@@ -1064,11 +1104,18 @@ classDiagram
     TrainCallback <|-- MetricCallback
     BaseDataset <|-- SEQDataset
     BaseDataset <|-- SFTDataset
-    BaseDataset <|-- DPODataset
-    BaseDataset <|-- GRPODataset
+    BaseDataset <|-- RecordDataset
+    RecordDataset <|-- DPODataset
+    RecordDataset <|-- GRPODataset
     Store <|-- H5Store
     Store <|-- MmapStore
     Store <|-- JsonlStore
+    H5Store --|> Streamable
+    H5Store --|> Recordable
+    MmapStore --|> Streamable
+    MmapStore --|> Recordable
+    JsonlStore --|> Streamable
+    JsonlStore --|> Recordable
     BaseSamplingStrategy <|-- TemperatureStrategy
     BaseSamplingStrategy <|-- TopKStrategy
     BaseSamplingStrategy <|-- TopPStrategy
@@ -1143,6 +1190,9 @@ classDiagram
     BaseDataset o-- Store
     Pipeline o-- PipelineConfig
     Pipeline o-- BaseMaskBuilder
+    Pipeline o-- AutoTokenizer
+    TokenizeTransform o-- AutoTokenizer
+    TokenizeTransform o-- BaseMaskBuilder
 
     %% --- Dependency (uses temporarily) ---
     TrainConfig ..> BaseStrategy : selects
@@ -1186,7 +1236,7 @@ classDiagram
     %% --- Association (general usage) ---
     Trainer --> TrainConfig
     DPOStrategy --> AutoModel
-    GRPOStrategy --> AutoModel
+    GRPOStrategy --> AutoModel : policy/old/ref
     InferenceScheduler --> Task
     InferenceScheduler --> TaskStatus
     Task --> TaskStatus
@@ -1203,8 +1253,8 @@ classDiagram
 | Module | Components | Description |
 |--------|------------|-------------|
 | **astrai.config** | BaseConfig, BaseModelConfig, AutoRegressiveLMConfig, EncoderConfig, ConfigFactory, TrainConfig, PipelineConfig, InputConfig, ProcessingConfig, OutputConfig | Configuration management (to_dict/from_dict, to_file/from_file) |
-| **astrai.preprocessing** | BaseMaskBuilder, MaskBuilderFactory, SectionedMaskBuilder, Pipeline, filter_by_length, PackingStrategy, PackingStrategyFactory, PositionIdStrategy, PositionIdStrategyFactory, StoreWriter, StoreWriterFactory | Declarative JSON-driven data preprocessing |
-| **astrai.dataset** | BaseDataset–GRPODataset, Store–JsonlStore/MmapStore/H5Store, StoreFactory, ResumableDistributedSampler, DatasetFactory | Dataset loading and management |
+| **astrai.preprocessing** | BaseMaskBuilder, MaskBuilderFactory, SectionedMaskBuilder, SingleOutputMaskBuilder, MultiOutputMaskBuilder, Pipeline, TokenizeTransform, filter_by_length, PackingStrategy, PackingStrategyFactory, plan_bfd, PositionIdStrategy, PositionIdStrategyFactory, StoreWriter, StoreWriterFactory, core (shared helpers) | Declarative JSON-driven data preprocessing |
+| **astrai.dataset** | BaseDataset–RecordDataset–DPO/GRPODataset, SEQDataset, SFTDataset, Store, Streamable, Recordable, H5Store, MmapStore, JsonlStore, StoreFactory, ResumableDistributedSampler, DatasetFactory | Dataset loading and management |
 | **astrai.serialization** | Checkpoint | Model serialization |
 | **astrai.model** | AutoModel, AutoRegressiveLM, EmbeddingEncoder, DecoderBlock, GQA, MLA, MLP, DeepSeekMoE, AttnFactory, FFNFactory, RMSNorm, Linear, RotaryEmbedding, Embedding | Neural network model |
 | **astrai.tokenize** | AutoTokenizer, ChatTemplate | Tokenizer and chat template |
@@ -1246,4 +1296,4 @@ classDiagram
 10. **AutoModel**: `from_pretrained()` loads `config.json` + `model.safetensors`, `_disable_random_init` replaces `nn.init.*` with no-ops
 11. **Protocols**: `OptimizerProtocol` / `SchedulerProtocol` — structural subtyping for `AccumOptimizer` / `AccumScheduler` wrappers
 
-> Document Update Time: 2026-07-09
+> Document Update Time: 2026-07-19

assets/docs/dataflow.md

@@ -61,41 +61,59 @@ StoreFactory.create("bin")   → MmapStore
 StoreFactory.create("jsonl") → JsonlStore
 ```
 
-**H5Store**: Reads HDF5 files. Tensors are loaded into host memory and normalized into segmented storage.
+All three inherit `Store` (base, owns `_data`/`_cum`/`_offsets`/`_normalize`) plus the `Streamable` and `Recordable` mixins, so every backend supports both `fetch(begin, end, keys)` (stream) and `fetch_record(index, keys)` (record) APIs.
 
-**MmapStore**: Memory-maps `.bin` files. OS page cache sharing is native — no explicit `share_memory_()` needed. Uses `torch.from_numpy(np.memmap(...))`.
+**H5Store**: Reads HDF5 files. Tensors are loaded into host memory and normalized into segmented storage. `segments_are_records=True` — each `data_i` dataset is one record.
 
-**JsonlStore**: On-the-fly tokenization of raw JSONL files at load time. Requires a `dataset_config.json` alongside the `.jsonl` files following the same `PipelineConfig` schema with an additional `tokenizer_path` field.
+**MmapStore**: Memory-maps `.bin` files. OS page cache sharing is native — no explicit `share_memory_()` needed. Uses `torch.from_numpy(np.memmap(...))`. `segments_are_records=False` — bin segments are contiguous streams; record access is driven by `_offsets` (written when `save_bin(..., record_keys=...)` was used at preprocessing time).
 
-All backends normalise tensors into `Store._data[Dict[str, List[Tensor]]]` + `Store._cum[Dict[str, List[int]]]` (cumulative lengths for bisect-based indexing).
+**JsonlStore**: On-the-fly tokenization of raw JSONL files at load time. Requires a `dataset_config.json` alongside the `.jsonl` files following the same `PipelineConfig` schema with an additional `tokenizer_path` field. Two modes: eager (default, applies `TokenizeTransform` to all records at load) and lazy (`processor=fn` given, defers tokenisation to `fetch_record` — used by DPO/GRPO).
+
+All backends normalise tensors into `Store._data[Dict[str, List[Tensor]]]` + `Store._cum[Dict[str, List[int]]]` (cumulative lengths for bisect-based stream indexing) + `Store._offsets[Dict[str, List[int]]]` (per-record offsets for record-mode indexing). Nested keys (GRPO `responses`/`masks` as `List[List[Tensor]]`) are stored as-is and excluded from both bookkeepings — they are only accessed record-by-record.
 
 ## Data Keys by Training Type
 
-| Type | Storage Keys |
-|------|-------------|
-| `seq` | `sequence` (→ input_ids, target_ids via offset-by-1) |
-| `sft` | `sequence`, `loss_mask`, `position_ids` |
-| `dpo` | `chosen`, `rejected`, `chosen_mask`, `rejected_mask` |
-| `grpo` | `prompts`, `responses`, `masks`, `rewards` |
+| Type | Storage Keys | Access Mode |
+|------|-------------|-------------|
+| `seq` | `sequence` (→ input_ids, target_ids via offset-by-1) | stream (`fetch`) |
+| `sft` | `sequence`, `loss_mask`, `position_ids` | stream (`fetch`) |
+| `dpo` | `chosen`, `rejected`, `chosen_mask`, `rejected_mask` | record (`fetch_record`) |
+| `grpo` | `prompts`, `responses`, `masks`, `rewards` | record (`fetch_record`) |
 
 ## Dataset Architecture
 
 ```
-DatasetFactory.load(train_type, load_path, window_size, stride=None, storage_type=None)
+DatasetFactory.load(train_type, load_path, window_size, stride=None,
+                    storage_type=None, tokenizer_path=None,
+                    max_len=2048, store=None)
   → BaseDataset.load(load_path, storage_type=None)
     → detect_format(load_path)
     → StoreFactory.create(storage_type)
     → Store.load(load_path)
       → _normalize(raw)  # base Store, shared by both backends
-        → Store._data[Dict[str, List[Tensor]]] + _cum[Dict[str, List[int]]]
-          → BaseDataset.__getitem__(idx)
-            → get_index(idx) → [begin, end)
-            → Store.fetch(begin, end, keys) → Tensor / Dict[str, Tensor]
+        → Store._data[Dict[str, List[Tensor]]]
+          + _cum[Dict[str, List[int]]]   (stream mode)
+          + _offsets[Dict[str, List[int]]]  (record mode)
+
+Stream datasets (SEQ/SFT):
+  BaseDataset.__getitem__(idx)
+    → get_index(idx) → [begin, end)
+    → Store.fetch(begin, end, keys) → Tensor / Dict[str, Tensor]
+
+Record datasets (DPO/GRPO via RecordDataset):
+  RecordDataset.__getitem__(idx)
+    → Store.fetch_record(idx, keys) → Tensor / Dict[str, Tensor]
 ```
 
-`window_size` = max input length, `stride` = step between consecutive samples (defaults to `window_size`, optional). `storage_type` defaults to `None` (auto-detect via `detect_format`).
+Class hierarchy: `BaseDataset` ← `SEQDataset` / `SFTDataset` (stream); `BaseDataset` ← `RecordDataset` ← `DPODataset` / `GRPODataset` (record).
 
-`Store.fetch(begin, end, keys)` accepts a single key (`str`) returning a `Tensor`, or a list of keys returning `Dict[str, Tensor]`. Internally uses `bisect` across multi-segment tensors. Raises `RuntimeError("Store not loaded")` if called before `load()`.
+`window_size` = max input length, `stride` = step between consecutive samples (defaults to `window_size`, optional). Only meaningful for stream datasets — record datasets ignore both. `storage_type` defaults to `None` (auto-detect via `detect_format`).
+
+`tokenizer_path` triggers lazy on-the-fly tokenisation for record datasets on raw JSONL (DPO builds a `dpo_processor`; SEQ/SFT/pre-tokenised backends ignore it). `store` (pre-built `Store`) bypasses `load_path`/`storage_type`/`tokenizer_path` entirely — the caller controls Store construction.
+
+`Store.fetch(begin, end, keys)` (stream mode, on `Streamable`): accepts a single key (`str`) returning a `Tensor`, or a list of keys returning `Dict[str, Tensor]`. Internally uses `bisect` across multi-segment tensors. Raises `RuntimeError("Store not loaded")` if called before `load()`.
+
+`Store.fetch_record(index, keys)` (record mode, on `Recordable`): same key API. Uses `_offsets[key]` when present (bin layout with per-record offsets), otherwise indexes `_data[key]` directly (H5/JSONL where each segment is one record).
 
 ## Sampler
 
@@ -109,4 +127,4 @@ DatasetFactory.load(train_type, load_path, window_size, stride=None, storage_typ
 
 Standard PyTorch `DataLoader` with configurable `batch_size`, `num_workers`, `pin_memory`, `prefetch_factor`. Sampler produces indices; dataloader fetches tensor batches via `__getitem__`.
 
-> Document Update Time: 2026-07-09
+> Document Update Time: 2026-07-19

assets/docs/params.md

@@ -26,7 +26,7 @@
 |-----------|-------------|---------|
 | `--warmup_ratio` | Fraction of total steps used for LR warmup | 0.05 |
 | `--max_lr` | Maximum learning rate (cosine decay after warmup) | 3e-4 |
-| `--max_grad_norm` | Maximum gradient norm for clipping | 1.0 |
+| `--max_grad_norm` | Maximum gradient norm for clipping (None disables) | None |
 
 ### Optimizer (MuonMix)
 
@@ -201,4 +201,4 @@ See [Preprocessing Guide](preprocessing.md) for config file format and examples.
 
 ---
 
-> Document Update Time: 2026-07-09
+> Document Update Time: 2026-07-19

assets/docs/training.md

@@ -86,7 +86,7 @@ on_train_end
 | `on_error` | On exception during training | `CheckpointCallback`, `MetricCallback` |
 | `on_train_end` | Training ends (always via finally) | `CheckpointCallback`, `MetricCallback`, `GradientCheckpointingCallback` |
 
-Default callbacks (in order): `gradient_checkpointing` (activation checkpointing, optional), `checkpoint` (safetensors, rank-0), `metric` (JSONL + validation, rank-0), `progress_bar` (tqdm), `gradient_clipping`.
+Default callbacks (in order): `gradient_checkpointing` (activation checkpointing, optional), `checkpoint` (safetensors, rank-0), `metric` (JSONL + validation, rank-0), `progress_bar` (tqdm), `gradient_clipping` (always registered; computes grad norm, clips only when `max_grad_norm` is not `None`).
 
 ## Strategies
 
@@ -118,7 +118,7 @@ $$
 L_{\text{DPO}} = -\mathbb{E}\left[\log\sigma\left(\beta\log\frac{\pi_\theta(y_w\mid x)}{\pi_{\text{ref}}(y_w\mid x)} - \beta\log\frac{\pi_\theta(y_l\mid x)}{\pi_{\text{ref}}(y_l\mid x)}\right)\right]
 $$
 
-Parameters: `beta=0.1`, `reduction="mean"`. Keys: `chosen`, `rejected`, `chosen_mask`, `rejected_mask`.
+Parameters: `beta=0.1`, `reduction="sum"`. Keys: `chosen`, `rejected`, `chosen_mask`, `rejected_mask`.
 
 ### GRPO (Group Relative Policy Optimization)
 
@@ -135,10 +135,14 @@ $$
 L_{\text{GRPO}} = -\mathbb{E}_t\left[\min\left(\rho_t A,\; \text{clip}\left(\rho_t, 1-\epsilon, 1+\epsilon\right)A\right)\right] + \lambda \cdot \mathbb{E}_t\left[\frac{\pi_{\text{ref}}}{\pi_\theta} - \log\frac{\pi_{\text{ref}}}{\pi_\theta} - 1\right]
 $$
 
-where $\rho_t = \pi_\theta(a_t|s_t) / \pi_{\text{ref}}(a_t|s_t)$ is the
-per-token probability ratio and the expectations are over valid response tokens.
+where $\rho_t = \pi_\theta(a_t|s_t) / \pi_{\text{old}}(a_t|s_t)$ is the
+per-token importance sampling ratio against the behaviour policy
+(`old_model`, synced externally between data-generation rounds) and the
+expectations are over valid response tokens. The KL term regularises
+$\pi_\theta$ towards a frozen reference model (`ref_model`, typically
+the SFT checkpoint).
 
-Parameters: `group_size=4`, `clip_eps=0.2`, `kl_coef=0.01`, `sync_interval=200`.
+Parameters: `group_size=4`, `clip_eps=0.2`, `kl_coef=0.01`. External sync of `old_model` weights via `sync_old_model()` between data-generation rounds.
 
 Keys: `prompts`, `responses`, `masks`, `rewards`.
 
@@ -218,4 +222,4 @@ nohup python scripts/tools/train.py \
 
 Full parameter reference at [params.md](params.md).
 
-> Document Update Time: 2026-07-09
+> Document Update Time: 2026-07-19

astrai/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.3.9"
+__version__ = "1.3.10"
 __author__ = "ViperEkura"
 
 from astrai.config import (
@@ -12,7 +12,7 @@ from astrai.config import (
 from astrai.dataset import (
     BaseDataset,
     DatasetFactory,
-    ResumableDistributedSampler,
+    RDSampler,
     Store,
     StoreFactory,
 )
@@ -77,7 +77,7 @@ __all__ = [
     "Pipeline",
     "PipelineConfig",
     "ProtocolHandler",
-    "ResumableDistributedSampler",
+    "RDSampler",
     "SamplingPipeline",
     "SchedulerFactory",
     "Store",

astrai/config/train_config.py

@@ -37,8 +37,9 @@ class TrainConfig(BaseConfig):
     grad_accum_steps: int = field(
         default=1, metadata={"help": "Number of iterations between steps."}
     )
-    max_grad_norm: float = field(
-        default=1.0, metadata={"help": "Maximum gradient norm."}
+    max_grad_norm: Optional[float] = field(
+        default=None,
+        metadata={"help": "Maximum gradient norm. None disables clipping."},
     )
     gradient_checkpointing_modules: List[str] = field(
         default_factory=list,
@@ -87,6 +88,10 @@ class TrainConfig(BaseConfig):
     pin_memory: bool = field(
         default=False, metadata={"help": "Pin memory for dataloader."}
     )
+    collate_fn: Optional[Callable[[List[Any]], Any]] = field(
+        default=None,
+        metadata={"help": "Collate function for dataloader (e.g. dpo_collate_fn)."},
+    )
 
     # distributed training
     nprocs: int = field(

astrai/dataset/__init__.py

@@ -1,15 +1,18 @@
 from astrai.dataset.dataset import (
     BaseDataset,
     DatasetFactory,
+    dpo_collate_fn,
     grpo_collate_fn,
 )
-from astrai.dataset.sampler import ResumableDistributedSampler
+from astrai.dataset.sampler import RDSampler
 from astrai.dataset.storage import (
     H5Store,
     JsonlStore,
     MmapStore,
+    Recordable,
     Store,
     StoreFactory,
+    Streamable,
     detect_format,
 )
 from astrai.serialization import (
@@ -22,8 +25,11 @@ from astrai.serialization import (
 __all__ = [
     "BaseDataset",
     "DatasetFactory",
+    "dpo_collate_fn",
     "grpo_collate_fn",
     "Store",
+    "Streamable",
+    "Recordable",
     "StoreFactory",
     "H5Store",
     "MmapStore",
@@ -33,5 +39,5 @@ __all__ = [
     "load_h5",
     "save_bin",
     "load_bin",
-    "ResumableDistributedSampler",
+    "RDSampler",
 ]

astrai/dataset/dataset.py

@@ -1,7 +1,31 @@
-"""Dataset implementations with factory pattern for training."""
+"""Dataset implementations for training.
+
+Composition over inheritance — every dataset is a thin wrapper that
+binds a :class:`Store` to a particular train-type's key mapping.  All
+sample-id → token/record indexing lives on the Store; datasets never
+know about window/stride math or segment layouts.
+
+Class hierarchy:
+
+    BaseDataset (ABC)         — holds a Store, exposes __len__/keys,
+                                 overrides __getitem__
+    ├── SEQDataset            — next-token prediction (stream)
+    ├── SFTDataset            — loss-mask + position_ids (stream)
+    ├── DPODataset            — chosen/rejected pairs (record)
+    └── GRPODataset           — prompt + response group (record)
+
+``DatasetFactory.load(train_type, load_path, window_size, stride, …)``
+builds the Store (auto-detecting format) before constructing the
+matching dataset.  Passing ``store=`` skips Store construction.
+
+When a record dataset (DPO) reads from raw JSONL, a *processor*
+function (pure ``record -> Dict[str, Tensor]``) is forwarded to
+:class:`JsonlStore` so tokenisation happens on the fly.
+"""
 
 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
+from functools import partial
+from typing import Callable, Dict, List, Optional
 
 import torch
 from torch import Tensor
@@ -13,6 +37,147 @@ from astrai.dataset.storage import (
     detect_format,
 )
 from astrai.factory import BaseFactory
+from astrai.tokenize import AutoTokenizer
+
+
+def dpo_tokenize(
+    record: dict,
+    tokenizer,
+    max_len: int = 2048,
+) -> Optional[dict]:
+    """Tokenize one DPO record into chosen/rejected + masks.
+
+    Applies the tokenizer's chat template so token sequences match the
+    SFT checkpoint's format.  Prompt is rendered with
+    ``add_generation_prompt=True``; chosen/rejected are appended as a
+    single assistant turn.
+
+    Accepts:
+
+    - Flat:   ``{"prompt": str, "chosen": str, "rejected": str}``
+    - Conv:   ``{"prompt": [{role, content}, ...], "chosen": [...], ...}``
+    - Legacy: ``{"input": str, "chosen": str, "rejected": str}``
+
+    No packing, no ``position_ids`` — DPO sequences are independent.
+    """
+    prompt = record.get("prompt") or record.get("input")
+    chosen = record.get("chosen")
+    rejected = record.get("rejected")
+    if prompt is None or chosen is None or rejected is None:
+        return None
+
+    prompt_messages = _to_messages(prompt)
+    chosen_text = _extract_text(chosen)
+    rejected_text = _extract_text(rejected)
+    if chosen_text is None or rejected_text is None:
+        return None
+    chosen_messages = prompt_messages + [{"role": "assistant", "content": chosen_text}]
+    rejected_messages = prompt_messages + [
+        {"role": "assistant", "content": rejected_text}
+    ]
+
+    prompt_ids = tokenizer.apply_chat_template(
+        prompt_messages, tokenize=True, add_generation_prompt=True
+    )
+    ch_ids = tokenizer.apply_chat_template(
+        chosen_messages, tokenize=True, add_generation_prompt=False
+    )
+    re_ids = tokenizer.apply_chat_template(
+        rejected_messages, tokenize=True, add_generation_prompt=False
+    )
+
+    full_ch = ch_ids[:max_len]
+    full_re = re_ids[:max_len]
+
+    prompt_len = min(len(prompt_ids), max_len)
+    ch_mask = [0] * prompt_len + [1] * max(0, len(full_ch) - prompt_len)
+    ch_mask = ch_mask[:max_len]
+    re_mask = [0] * prompt_len + [1] * max(0, len(full_re) - prompt_len)
+    re_mask = re_mask[:max_len]
+
+    return {
+        "chosen": full_ch,
+        "rejected": full_re,
+        "chosen_mask": ch_mask,
+        "rejected_mask": re_mask,
+    }
+
+
+def _to_messages(value) -> list:
+    """Accept str or conversation list; return message list."""
+    if isinstance(value, str):
+        return [{"role": "user", "content": value}]
+    if isinstance(value, list):
+        return value
+    return [{"role": "user", "content": str(value)}]
+
+
+def _extract_text(value) -> Optional[str]:
+    """Accept str or conversation list; return plain text."""
+    if value is None:
+        return None
+    if isinstance(value, str):
+        return value
+    if isinstance(value, list):
+        return "".join(m.get("content", "") for m in value if isinstance(m, dict))
+    return None
+
+
+def dpo_processor(
+    record: dict,
+    tokenizer,
+    max_len: int = 2048,
+) -> Dict[str, Tensor]:
+    """DPO processor: wraps :func:`dpo_tokenize` and returns tensors."""
+    result = dpo_tokenize(record, tokenizer, max_len=max_len)
+    if result is None:
+        raise ValueError(f"Malformed DPO record: {list(record.keys())}")
+    return {
+        "chosen": torch.tensor(result["chosen"], dtype=torch.int32),
+        "rejected": torch.tensor(result["rejected"], dtype=torch.int32),
+        "chosen_mask": torch.tensor(result["chosen_mask"], dtype=torch.bool),
+        "rejected_mask": torch.tensor(result["rejected_mask"], dtype=torch.bool),
+    }
+
+
+def dpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
+    """Collate variable-length DPO samples into padded 2-D tensors.
+
+    Input: list of dicts, each with:
+      - chosen:        [C_i]
+      - rejected:      [R_i]
+      - chosen_mask:   [C_i]
+      - rejected_mask: [R_i]
+
+    Output (padded to the max length across chosen/rejected within the batch):
+      - chosen:        [B, S_max]
+      - rejected:      [B, S_max]
+      - chosen_mask:   [B, S_max]
+      - rejected_mask: [B, S_max]
+    """
+    B = len(batch)
+    S_max = max(b["chosen"].size(0) for b in batch)
+    S_max = max(S_max, max(b["rejected"].size(0) for b in batch))
+
+    chosen = torch.zeros(B, S_max, dtype=torch.long)
+    rejected = torch.zeros(B, S_max, dtype=torch.long)
+    chosen_mask = torch.zeros(B, S_max, dtype=torch.bool)
+    rejected_mask = torch.zeros(B, S_max, dtype=torch.bool)
+
+    for i, b in enumerate(batch):
+        c_len = b["chosen"].size(0)
+        r_len = b["rejected"].size(0)
+        chosen[i, :c_len] = b["chosen"]
+        rejected[i, :r_len] = b["rejected"]
+        chosen_mask[i, :c_len] = b["chosen_mask"]
+        rejected_mask[i, :r_len] = b["rejected_mask"]
+
+    return {
+        "chosen": chosen,
+        "rejected": rejected,
+        "chosen_mask": chosen_mask,
+        "rejected_mask": rejected_mask,
+    }
 
 
 def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
@@ -58,200 +223,203 @@ def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
     }
 
 
-class BaseDataset(Dataset, ABC):
-    """Abstract base class for all dataset types.
+def validate_keys(store: Store, required: List[str]) -> None:
+    """Raise ``KeyError`` if *store* is missing any *required* key."""
+    if not required:
+        return
+    actual = set(store.keys)
+    missing = [k for k in required if k not in actual]
+    if missing:
+        raise KeyError(
+            f"Store at {getattr(store, '_load_path', '?')} is missing required "
+            f"keys {missing}; available keys are {sorted(actual)}."
+        )
 
-    Implements common functionality for window-based data fetching.
-    Uses a storage abstraction for format-agnostic data loading.
+
+class BaseDataset(Dataset, ABC):
+    """Abstract base class for dataset types.
+
+    Holds a :class:`Store`.  All sample-id indexing is delegated to the
+    store — this class exposes ``__len__`` as ``len(store)`` and the
+    ``keys`` property as ``store.keys``.  Subclasses implement
+    ``__getitem__`` with the train-type-specific key mapping and any
+    training-only index arithmetic (e.g. the next-token ``+1`` shift).
     """
 
-    def __init__(self, window_size: int, stride: int):
+    required_keys: List[str] = []
+
+    def __init__(self, store: Store):
         super().__init__()
-        self.window_size = window_size
-        self.stride = stride
-        self.storage: Optional[Store] = None
+        self.store: Store = store
+        validate_keys(store, self.required_keys)
 
-    @property
-    def required_keys(self) -> List[str]:
-        """Return required storage keys for this dataset type.
-
-        Subclasses should override to specify expected keys.
-        """
-        return []
-
-    def _validate_keys(self):
-        if not self.required_keys:
-            return
-        actual_keys = set(self.storage.keys)
-        missing = [k for k in self.required_keys if k not in actual_keys]
-        if missing:
-            raise KeyError(
-                f"Dataset {type(self).__name__} requires keys {self.required_keys}, "
-                f"but storage at {self._load_path} only has {sorted(actual_keys)}. "
-                f"Missing: {missing}"
-            )
-
-    def load(self, load_path: str, storage_type: Optional[str] = None, **kwargs):
-        """Load dataset from the given path.
-
-        Auto-detects the storage format if not specified.
-
-        Args:
-            load_path: Path to the data directory or file
-            storage_type: Force a specific storage type ("h5", "bin", "jsonl"),
-                          or None for auto-detection
-            **kwargs: Extra arguments forwarded to the store constructor and
-                      to ``store.load()``.
-
-        Raises:
-            KeyError: If the loaded storage is missing required keys.
-        """
-        if storage_type is None:
-            storage_type = detect_format(load_path)
-        self.storage = StoreFactory.create(storage_type, **kwargs)
-        self._load_path = load_path
-        self.storage.load(load_path, **kwargs)
-        self._validate_keys()
-
-    @property
-    def count(self) -> int:
-        """Return the total number of raw elements (tokens) in the dataset."""
-        if self.storage is None:
-            return 0
-        return len(self.storage)
+    def __len__(self) -> int:
+        return len(self.store)
 
     @property
     def keys(self) -> List[str]:
-        """Return the available data keys."""
-        if self.storage is None:
-            return []
-        return self.storage.keys
+        return self.store.keys
 
-    def get_index(self, index: int) -> tuple:
-        """Calculate begin and end indices for a sample.
-
-        Args:
-            index: Sample index
-
-        Returns:
-            Tuple of (begin_idx, end_idx)
-        """
-        if self.storage is None:
-            raise RuntimeError("Dataset not loaded, call load() first")
-        total = len(self.storage)
-        if total <= self.window_size:
-            raise ValueError(
-                f"Data too short: {total} tokens <= window_size {self.window_size}"
-            )
-
-        begin_idx = min(index * self.stride, total - 1 - self.window_size)
-        end_idx = min(begin_idx + self.window_size, total - 1)
-
-        return begin_idx, end_idx
+    @property
+    def token_count(self) -> int:
+        return self.store.token_count
 
     @abstractmethod
     def __getitem__(self, index: int) -> Dict[str, Tensor]:
-        """Get a single sample by index.
-
-        Must be implemented by subclasses.
-        """
         raise NotImplementedError
 
-    def __len__(self) -> int:
-        if self.storage is None:
-            return 0
-        total = len(self.storage)
-        if total <= self.window_size:
-            return 0
-        return (total - 1 - self.window_size) // self.stride + 1
-
 
 class DatasetFactory(BaseFactory["BaseDataset"]):
-    """Factory class for creating dataset instances.
+    """Factory for creating dataset instances by train-type.
 
-    Supports decorator-based registration for extensible dataset types.
-    All default dataset types (seq, sft, dpo, grpo) are registered automatically
-    when their classes are defined with the decorator.
-
-    Example usage:
-        @DatasetFactory.register("custom")
-        class CustomDataset(BaseDataset):
-            ...
-
-        dataset = DatasetFactory.create("custom", window_size, stride)
+    Use :meth:`DatasetFactory.register("custom")` to register new
+    dataset classes; they must inherit from :class:`BaseDataset`.
     """
 
     @classmethod
     def load(
         cls,
         train_type: str,
-        load_path: str,
-        window_size: int,
+        load_path: Optional[str] = None,
+        window_size: int = 0,
         stride: Optional[int] = None,
         storage_type: Optional[str] = None,
+        tokenizer_path: Optional[str] = None,
+        max_len: int = 2048,
+        store: Optional[Store] = None,
         **kwargs,
     ) -> "BaseDataset":
         """Create and load a dataset in one step.
 
+        Two entry points:
+
+        - **store given**: bind it directly — the caller fully controls
+          Store construction and processor setup.  *load_path*,
+          *storage_type*, *tokenizer_path*, *window_size*, *stride* are
+          ignored.
+        - **store is None**: build a Store from *load_path*, auto-detecting
+          format and constructing a processor when *tokenizer_path* is
+          given for a record dataset on JSONL.
+
         Args:
-            train_type: Type of training dataset
-            load_path: Path to the data file
-            window_size: Window size for data sampling
-            stride: Stride between consecutive samples (default: same as window_size)
-            storage_type: Storage type ("h5", "bin", "jsonl") or None for auto-detection
-            **kwargs: Extra arguments forwarded to ``dataset.load()``.
+            train_type: Registered dataset name ("seq", "sft", "dpo",
+                "grpo", …).
+            load_path: Path to the data file or directory (ignored if
+                *store* is given).
+            window_size: Stream window length — only meaningful for
+                stream datasets (SEQ/SFT).  Record datasets ignore it.
+            stride: Stride between consecutive stream samples
+                (default: same as *window_size*).
+            storage_type: Storage backend ("h5", "bin", "jsonl") or
+                None for auto-detection.
+            tokenizer_path: Path to tokenizer for lazy JSONL
+                tokenisation (record datasets only).
+            max_len: Max sequence length forwarded to processors.
+            store: Pre-built, already-loaded Store instance.
+            **kwargs: Extra arguments forwarded to ``store.load()``.
 
         Returns:
-            Loaded dataset instance
+            Loaded dataset instance.
         """
+        if store is not None:
+            return cls.create(train_type, store=store)
+
+        if load_path is None:
+            raise ValueError("Either load_path or store must be provided")
+
+        if storage_type is None:
+            storage_type = detect_format(load_path)
+
         if stride is None:
             stride = window_size
 
-        dataset = cls.create(train_type, window_size, stride)
-        dataset.load(load_path, storage_type=storage_type, **kwargs)
+        processor = cls._maybe_build_processor(
+            train_type, storage_type, tokenizer_path, max_len
+        )
 
-        return dataset
+        store_window = cls._store_window_for(train_type, window_size)
+        store = StoreFactory.create(
+            storage_type,
+            window_size=store_window,
+            stride=stride if stride else store_window,
+        )
+        if processor is not None:
+            store.load(load_path, processor=processor, **kwargs)
+        else:
+            store.load(load_path, **kwargs)
+
+        return cls.create(train_type, store=store)
+
+    @staticmethod
+    def _store_window_for(train_type: str, window_size: int) -> int:
+        """Stream datasets consume ``window_size``; record datasets ignore it.
+
+        Record datasets (dpo/grpo) treat each record as an independent
+        training unit and never window, so the store is built with
+        ``window_size=0`` and ``len(store)`` returns the record count.
+        """
+        if train_type in ("seq", "sft"):
+            return window_size
+        return 0
+
+    @staticmethod
+    def _maybe_build_processor(
+        train_type: str,
+        storage_type: str,
+        tokenizer_path: Optional[str],
+        max_len: int,
+    ) -> Optional[Callable[[dict], Dict[str, Tensor]]]:
+        """Build an on-the-fly tokenisation processor if applicable.
+
+        Only raw JSONL + record datasets (DPO/GRPO) need a processor;
+        pre-tokenised backends (H5/bin) and stream datasets (SEQ/SFT)
+        return ``None`` so no tokenizer is loaded.
+        """
+        if tokenizer_path is None or storage_type != "jsonl":
+            return None
+        if train_type == "dpo":
+            tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+            return partial(dpo_processor, tokenizer=tokenizer, max_len=max_len)
+        return None
 
 
 @DatasetFactory.register("seq")
 class SEQDataset(BaseDataset):
-    """Dataset for sequential next-token prediction training."""
+    """Dataset for sequential next-token prediction training.
 
-    @property
-    def required_keys(self) -> List[str]:
-        return ["sequence"]
+    Stream mode: ``store.fetch(begin, end, "sequence")`` returns the
+    input window; the +1 shifted call returns the next-token target.
+    """
 
-    def _fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
-        return self.storage.fetch(begin_idx, end_idx, "sequence")
+    required_keys = ["sequence"]
 
-    def __getitem__(self, index):
-        begin_idx, end_idx = self.get_index(index)
-
-        x = self._fetch_data(begin_idx, end_idx).to(dtype=torch.long)
-        y = self._fetch_data(begin_idx + 1, end_idx + 1).to(dtype=torch.long)
-
-        return {"input_ids": x, "target_ids": y}
+    def __getitem__(self, index: int):
+        begin, end = self.store.sample_window(index)
+        x = self.store.fetch(begin, end, "sequence")
+        y = self.store.fetch(begin + 1, end + 1, "sequence")
+        return {
+            "input_ids": x.to(dtype=torch.long),
+            "target_ids": y.to(dtype=torch.long),
+        }
 
 
 @DatasetFactory.register("sft")
 class SFTDataset(BaseDataset):
-    """Dataset for supervised fine-tuning with loss masking."""
+    """Dataset for supervised fine-tuning with loss masking.
 
-    @property
-    def required_keys(self) -> List[str]:
-        return ["sequence", "loss_mask", "position_ids"]
+    Stream mode: ``sequence``/``loss_mask``/``position_ids`` are sliced
+    to the window.  ``loss_mask`` and ``target_ids`` use the +1 shifted
+    slice so they align with the predicted positions.
+    """
 
-    def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
-        return self.storage.fetch(begin_idx, end_idx, key)
-
-    def __getitem__(self, index):
-        begin_idx, end_idx = self.get_index(index)
-
-        x = self._fetch_data(begin_idx, end_idx, "sequence")
-        y = self._fetch_data(begin_idx + 1, end_idx + 1, "sequence")
-        position_ids = self._fetch_data(begin_idx, end_idx, "position_ids")
-        loss_mask = self._fetch_data(begin_idx + 1, end_idx + 1, "loss_mask")
+    required_keys = ["sequence", "loss_mask", "position_ids"]
 
+    def __getitem__(self, index: int):
+        begin, end = self.store.sample_window(index)
+        x = self.store.fetch(begin, end, "sequence")
+        y = self.store.fetch(begin + 1, end + 1, "sequence")
+        position_ids = self.store.fetch(begin, end, "position_ids")
+        loss_mask = self.store.fetch(begin + 1, end + 1, "loss_mask")
         return {
             "input_ids": x.to(dtype=torch.long),
             "target_ids": y.to(dtype=torch.long),
@@ -262,32 +430,37 @@ class SFTDataset(BaseDataset):
 
 @DatasetFactory.register("dpo")
 class DPODataset(BaseDataset):
-    """Dataset for Direct Preference Optimization training."""
+    """Record-structured dataset for Direct Preference Optimization.
 
-    @property
-    def required_keys(self) -> List[str]:
-        return ["chosen", "rejected", "chosen_mask", "rejected_mask"]
+    Each sample is one preference pair (chosen + rejected) and is an
+    independent training unit — no windowing, stride, or cross-record
+    concatenation.  This keeps each sequence self-contained so attention
+    never leaks across preference pairs.
 
-    def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
-        return self.storage.fetch(begin_idx, end_idx, key)
+    Two loading paths (handled by :class:`DatasetFactory`):
 
-    def __getitem__(self, index: int):
-        begin_idx, end_idx = self.get_index(index)
+    - **Pre-tokenized** (H5/bin): ``store.load(path)`` reads per-record
+      tensors; ``__getitem__`` returns them directly.
+    - **Raw JSONL** (``tokenizer_path=...``): builds a lazy processor
+      via :func:`dpo_processor` that tokenises on the fly — no packing,
+      no ``position_ids``.
+    """
 
-        chosen = self._fetch_data(begin_idx, end_idx, "chosen").to(dtype=torch.long)
-        rejected = self._fetch_data(begin_idx, end_idx, "rejected").to(dtype=torch.long)
-        chosen_mask = self._fetch_data(begin_idx, end_idx, "chosen_mask").to(
-            dtype=torch.bool
-        )
-        rejected_mask = self._fetch_data(begin_idx, end_idx, "rejected_mask").to(
-            dtype=torch.bool
-        )
+    required_keys = ["chosen", "rejected", "chosen_mask", "rejected_mask"]
 
+    def make_processor(self, tokenizer, max_len: int):
+        return partial(dpo_processor, tokenizer=tokenizer, max_len=max_len)
+
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
         return {
-            "chosen": chosen,
-            "rejected": rejected,
-            "chosen_mask": chosen_mask,
-            "rejected_mask": rejected_mask,
+            "chosen": self.store.fetch_record(index, "chosen").to(dtype=torch.long),
+            "rejected": self.store.fetch_record(index, "rejected").to(dtype=torch.long),
+            "chosen_mask": self.store.fetch_record(index, "chosen_mask").to(
+                dtype=torch.bool
+            ),
+            "rejected_mask": self.store.fetch_record(index, "rejected_mask").to(
+                dtype=torch.bool
+            ),
         }
 
 
@@ -295,10 +468,8 @@ class DPODataset(BaseDataset):
 class GRPODataset(BaseDataset):
     """Dataset for offline Group Relative Policy Optimization.
 
-    Unlike the window-based datasets (SEQ/SFT/DPO), GRPO data is
-    record-structured: each sample is one prompt with its group of
-    responses and scalar rewards.  There is no windowing or stride —
-    every record is an independent training unit.
+    Each sample is one prompt with its group of responses and scalar
+    rewards — an independent training unit with no windowing or stride.
 
     Expected storage layout (produced by JsonlStore or pre-tokenized):
 
@@ -308,70 +479,16 @@ class GRPODataset(BaseDataset):
     - ``rewards``:   List[Tensor]  — one 1-D float tensor (len G) per record
     """
 
-    def __init__(self, window_size: int = 0, stride: int = 0, **kwargs):
-        super().__init__(window_size=window_size, stride=stride or window_size)
-        self._records: List[dict] = []
-
-    @property
-    def required_keys(self) -> List[str]:
-        return ["prompts", "responses", "masks", "rewards"]
-
-    def load(self, load_path: str, storage_type: Optional[str] = None, **kwargs):
-        if storage_type is None:
-            storage_type = detect_format(load_path)
-        self.storage = StoreFactory.create(storage_type, **kwargs)
-        self._load_path = load_path
-        self.storage.load(load_path, **kwargs)
-        self._validate_keys()
-        self._build_records()
-
-    def _validate_keys(self):
-        actual_keys = set(self.storage.keys)
-        missing = [k for k in self.required_keys if k not in actual_keys]
-        if missing:
-            raise KeyError(
-                f"GRPODataset requires keys {self.required_keys}, "
-                f"but storage only has {sorted(actual_keys)}. Missing: {missing}"
-            )
-
-    def _build_records(self):
-        """Unfold segmented storage into per-record lists.
-
-        ``prompts`` is a flat list of 1-D tensors (one per record).
-        ``responses`` / ``masks`` are nested lists (G tensors per record).
-        ``rewards`` is a flat list of 1-D tensors (len G per record).
-        """
-        prompt_segs = self.storage._data.get("prompts", [])
-        response_segs = self.storage._data.get("responses", [])
-        mask_segs = self.storage._data.get("masks", [])
-        reward_segs = self.storage._data.get("rewards", [])
-
-        n_records = len(prompt_segs)
-        self._records = []
-        for i in range(n_records):
-            self._records.append(
-                {
-                    "prompts": prompt_segs[i],
-                    "responses": response_segs[i] if i < len(response_segs) else [],
-                    "masks": mask_segs[i] if i < len(mask_segs) else [],
-                    "rewards": reward_segs[i]
-                    if i < len(reward_segs)
-                    else torch.tensor([]),
-                }
-            )
-
-    @property
-    def count(self) -> int:
-        return len(self._records)
-
-    def __len__(self) -> int:
-        return len(self._records)
+    required_keys = ["prompts", "responses", "masks", "rewards"]
 
     def __getitem__(self, index: int) -> Dict[str, Tensor]:
-        rec = self._records[index]
+        prompts = self.store.fetch_record(index, "prompts")
+        responses = self.store.fetch_record(index, "responses")
+        masks = self.store.fetch_record(index, "masks")
+        rewards = self.store.fetch_record(index, "rewards")
         return {
-            "prompts": rec["prompts"].to(dtype=torch.long),
-            "responses": [r.to(dtype=torch.long) for r in rec["responses"]],
-            "masks": [m.to(dtype=torch.bool) for m in rec["masks"]],
-            "rewards": rec["rewards"].to(dtype=torch.float32),
+            "prompts": prompts.to(dtype=torch.long),
+            "responses": [r.to(dtype=torch.long) for r in responses],
+            "masks": [m.to(dtype=torch.bool) for m in masks],
+            "rewards": rewards.to(dtype=torch.float32),
         }

astrai/dataset/sampler.py

@@ -5,7 +5,15 @@ import torch.distributed as dist
 from torch.utils.data import Dataset, Sampler
 
 
-class ResumableDistributedSampler(Sampler[int]):
+class RDSampler(Sampler[int]):
+    """Resumable Distributed Sampler.
+
+    A distributed sampler that supports checkpoint-based resume: iteration
+    state (epoch, position) is tracked so training can continue from the
+    exact sample after a restart.  Shards the dataset across
+    ``dist.world_size`` replicas with optional shuffling.
+    """
+
     def __init__(
         self,
         data_source: Dataset,

astrai/dataset/storage.py

@@ -1,20 +1,48 @@
 """Storage backends for different data formats.
 
-Layers:
-  - I/O layer:       save_* / load_* functions, read/write raw files (HDF5/bin)
-                      return Dict[str, List[Tensor]] — format-specific, no state
-  - Store (ABC):     central abstraction, normalizes multi-segment into
-                      Dict[str, List[Tensor]] per key via _normalize(),
-                      fetch() uses bisect across segments — no forced concat
-  - Dataset layer:   BaseDataset owns a Store, only calls store.fetch(begin, end, key)
+Architecture (composition over inheritance):
 
-Key properties:
-  - Multi-segment:   segments kept as-is, no forced concatenation — safe for
-                      datasets larger than RAM
-  - Explicit length: _length = min(total elements across keys), set at load,
-                      __len__ returns O(1)
-  - Zero-copy mmap:  MmapStore wraps np.memmap(mode="r"), all DataLoader
-                       workers share OS page-cache pages
+    Store (ABC)               — owns _data/_cum/_offsets bookkeeping
+                                  + window_size/stride for sample-id
+                                  indexing.  __getitem__/__len__ produce
+                                  the smallest iterable unit so Dataset
+                                  classes are pure delegators.
+    Streamable (mixin)        — raw token slice  fetch(begin, end, keys)
+    Recordable (mixin)        — raw record slice fetch_record(idx, keys)
+
+    H5Store(Store, Streamable, Recordable)
+    MmapStore(Store, Streamable, Recordable)
+    JsonlStore(Store, Streamable, Recordable)
+
+Each mixin is a stateless trait that relies on ``self._data`` etc.
+provided by :class:`Store`.  Concrete stores mix in whichever access
+primitives they support — ``Store`` is the sole base class, so there is
+no diamond inheritance or MRO ambiguity.
+
+Sample-id indexing lives on :class:`Store`, not on the dataset:
+
+- **Stream mode** (``window_size > 0``):  ``len(store)`` returns the number
+  of ``(window_size, stride)`` windows that fit in the token river;
+  ``store[i]`` returns the *i*-th window as a dict of per-key tensors;
+  ``store.sample_window(i)`` exposes the underlying ``(begin, end)``
+  token slice for callers (e.g. next-token trainers) that need a +1
+  shifted companion window.
+- **Record mode** (``num_records > 0``):  ``len(store)`` returns the
+  record count; ``store[i]`` returns the *i*-th record dict.
+
+Raw token/record access via :meth:`fetch` / :meth:`fetch_record`
+remains available for low-level callers that want explicit index
+control.  ``store.token_count`` is the total stream token count (what
+``len(store)`` used to mean in the legacy stream-only API).
+
+``segments_are_records`` (class attribute on each Store subclass)
+tells ``_normalize`` whether segments are inherently per-record (H5/
+JSONL) or opaque shards (bin).  Record access for bin relies on
+``_offsets`` instead.
+
+:class:`JsonlStore` supports a lazy mode (``processor=fn``) that keeps
+raw records and defers tokenisation to ``fetch_record`` — used by DPO
+to train directly from a ``.jsonl`` file without a pre-tokenised copy.
 """
 
 import bisect
@@ -23,20 +51,18 @@ import json
 import logging
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Dict, List, Union
+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.builder import MaskBuilderFactory
-from astrai.preprocessing.position_id import PositionIdStrategyFactory
+from astrai.preprocessing.transform import TokenizeTransform
 from astrai.serialization import (
     load_bin,
+    load_bin_offsets,
     load_h5,
 )
-from astrai.tokenize import AutoTokenizer
 
 logger = logging.getLogger(__name__)
 
@@ -48,7 +74,7 @@ def detect_format(load_path: str) -> str:
         load_path: Directory or file path
 
     Returns:
-        Format string ("h5", "bin", or "jsonl")
+        Format string ("h5", "bin", "jsonl", or "processed")
 
     Raises:
         FileNotFoundError: If no supported data files are found
@@ -81,84 +107,173 @@ def detect_format(load_path: str) -> str:
     ]
     if jsonl_files:
         return "jsonl"
-    json_files = [
-        Path(p) for p in glob.glob(str(root / "**" / "*.json"), recursive=True)
-    ]
-    if json_files:
-        return "jsonl"
     raise FileNotFoundError(f"No supported data files found at {load_path}")
 
 
 class Store(ABC):
-    """String keys -> segmented tensors with ``fetch(begin, end, keys)``.
+    """Common base for all storage backends.
 
-    Each key maps to one or more tensor segments (no forced concatenation).
-    ``len(store)`` returns ``self._length`` (explicit, O(1)), the minimum
-    total element count across all keys.
+    A Store owns both its data layout AND its sample-id → token/record
+    index translation.  Datasets are thin wrappers that bind a Store
+    to a particular train-type's key mapping; they never know about
+    window/stride math.
 
-    Subclasses fill ``self._data`` and ``self._cum`` during ``load()``
-    via ``_normalize()``.
+    Two iteration modes:
+
+    - **Stream** (``window_size > 0``): data is treated as one long
+      token river.  ``len(store)`` returns the number of windows;
+      ``store[i]`` slices every stream-compatible key to window ``i``;
+      ``store.sample_window(i)`` returns the ``(begin, end)`` token
+      slice for callers needing a +1 shifted companion window.
+    - **Record** (``num_records > 0``): data is per-record.
+      ``len(store)`` returns ``num_records``; ``store[i]`` returns
+      the *i*-th record as a dict.
+
+    Raw token slicing is still available via :meth:`fetch` (mixed in
+    by :class:`Streamable`) when a store has stream support configured.
+    Raw record slicing via :meth:`fetch_record` (mixed in by
+    :class:`Recordable`) when a store has record support.
+
+    ``token_count`` exposes the raw total stream length — this is what
+    ``len(store)`` returned in the legacy stream-only API and what
+    stream-bound ``fetch`` uses for its bounds check.
     """
 
-    def __init__(self):
+    segments_are_records: bool = False
+
+    def __init__(
+        self,
+        window_size: int = 0,
+        stride: Optional[int] = None,
+    ):
         self._data: Dict[str, List[Tensor]] = {}
         self._cum: Dict[str, List[int]] = {}
+        self._offsets: Dict[str, List[int]] = {}
         self._length: int = 0
+        self._num_records: int = 0
+        self._window_size: int = int(window_size)
+        self._stride: int = int(stride) if stride is not None else int(window_size)
 
     @abstractmethod
-    def load(self, path: str) -> None:
+    def load(self, path: str, **kwargs) -> None:
         raise NotImplementedError
 
     @property
     def keys(self) -> List[str]:
         return list(self._data.keys())
 
-    def __len__(self) -> int:
+    @property
+    def window_size(self) -> int:
+        return self._window_size
+
+    @property
+    def stride(self) -> int:
+        return self._stride
+
+    @property
+    def token_count(self) -> int:
+        """Total tokens across all stream segments.
+
+        Useful for the bounds-checked raw :meth:`fetch` and as the
+        legacy ``len(store)`` value.
+        """
         return self._length
 
-    def fetch(
-        self,
-        begin: int,
-        end: int,
-        keys: Union[str, List[str]],
-    ):
-        if not self._data:
-            raise RuntimeError("Store not loaded")
-        if not (0 <= begin < self._length and 0 <= end <= self._length):
-            raise ValueError(
-                f"Index out of bounds: begin={begin}, end={end}, length={self._length}"
+    @property
+    def num_records(self) -> int:
+        """Number of records available via :meth:`fetch_record`.
+
+        Non-zero only when the backing layout provides per-record
+        indexing (H5/JSONL segments or bin ``_offsets``).
+        """
+        return self._num_records
+
+    @property
+    def num_samples(self) -> int:
+        """Number of items produced by ``__getitem__``.
+
+        Stream-mode wins when ``window_size > 0`` and there are tokens
+        to slice; otherwise falls back to ``num_records``.
+        """
+        if self._window_size > 0 and self._length > 0:
+            total = self._length
+            w = self._window_size
+            if total <= w:
+                return 0
+            return (total - 1 - w) // self._stride + 1
+        return self._num_records
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        if index < 0:
+            index += self.num_samples
+        if not 0 <= index < self.num_samples:
+            raise IndexError(
+                f"Store index out of range: {index}, num_samples={self.num_samples}"
             )
-        if isinstance(keys, str):
-            return self._fetch_key(keys, begin, end)
-        return {k: self._fetch_key(k, begin, end) for k in keys}
+        if self._window_size > 0 and self._length > 0:
+            begin, end = self.sample_window(index)
+            keys = self._stream_keys()
+            return {k: self.fetch(begin, end, k) for k in keys}
+        return self.fetch_record(index, self._record_keys())
 
-    def _fetch_key(self, key: str, begin: int, end: int) -> Tensor:
-        """Fetch slice [begin, end) across potentially multiple segments."""
-        segments = self._data[key]
-        cum = self._cum[key]
-        seg_start = bisect.bisect_right(cum, begin)
-        seg_end = bisect.bisect_left(cum, end)
+    def sample_window(self, index: int) -> Tuple[int, int]:
+        """Return ``(begin, end)`` token positions for stream sample *index*.
 
-        results = []
-        for i in range(seg_start, seg_end + 1):
-            prev = cum[i - 1] if i > 0 else 0
-            s = max(begin - prev, 0)
-            e = min(end - prev, segments[i].shape[0])
-            results.append(segments[i][s:e])
+        The clipped tail keeps the last reachable window inside the
+        token river instead of overshooting.  Caller is responsible
+        for staying within :attr:`num_samples`: an out-of-range index
+        raises ``IndexError``.
+        """
+        if self._window_size <= 0:
+            raise RuntimeError("sample_window() requires window_size > 0 (stream mode)")
+        if self._window_size <= 0 or self._length <= self._window_size:
+            raise IndexError(
+                f"Data too short for window: token_count={self._length}, "
+                f"window_size={self._window_size}"
+            )
+        if not 0 <= index < self.num_samples:
+            raise IndexError(
+                f"Sample index out of range: {index}, num_samples={self.num_samples}"
+            )
+        total = self._length
+        begin = min(index * self._stride, total - 1 - self._window_size)
+        end = min(begin + self._window_size, total - 1)
+        return begin, end
 
-        return results[0] if len(results) == 1 else torch.cat(results, dim=0)
+    def _stream_keys(self) -> List[str]:
+        out: List[str] = []
+        for k, tensors in self._data.items():
+            if tensors and isinstance(tensors[0], list):
+                continue
+            out.append(k)
+        return out
 
-    def _normalize(self, raw: Dict[str, list]):
-        """Register segments and pre-compute cumulative lengths.
+    def _record_keys(self) -> List[str]:
+        return list(self._data.keys())
 
-        Does NOT concatenate — segments are kept as-is to avoid OOM on
-        large datasets.  Sets ``self._length`` to the minimum total
-        element count across all flat-tensor keys.
+    def _normalize(
+        self,
+        raw: Dict[str, list],
+        offsets: Optional[Dict[str, List[int]]] = None,
+    ):
+        """Register segments and pre-compute indices for both access modes.
 
-        For GRPO multi-response keys, values may be ``List[List[Tensor]]``
-        (one list of G tensors per record).  These are stored as-is and
-        excluded from the cumulative-length bookkeeping since they are
-        accessed record-by-record via ``_data`` rather than via ``fetch``.
+        Stream mode: ``_cum[key]`` accumulates per-segment lengths so
+        ``Streamable._fetch_stream_key`` can bisect across segments
+        without concatenation.
+
+        Record mode: if *offsets* is provided (bin layout),
+        ``_offsets[key]`` stores cumulative per-record offsets into the
+        single concatenated segment.  Otherwise, when
+        ``segments_are_records`` is True (H5/JSONL), ``_data[key]`` is
+        a per-record list and ``fetch_record`` indexes it directly.
+
+        Nested keys (GRPO ``responses``/``masks`` as
+        ``List[List[Tensor]]``) are stored as-is and excluded from both
+        cumulative bookkeepings — they are only accessed record-by-record.
         """
         flat_lengths = []
         for key, tensors in raw.items():
@@ -167,7 +282,6 @@ class Store(ABC):
                 self._cum[key] = []
                 flat_lengths.append(0)
                 continue
-            # Skip nested lists (GRPO responses/masks) — record-level access
             if isinstance(tensors[0], list):
                 self._cum[key] = []
                 continue
@@ -180,166 +294,343 @@ class Store(ABC):
             flat_lengths.append(cum[-1] if cum else 0)
         self._length = min(flat_lengths) if flat_lengths else 0
 
+        valid_offsets: Dict[str, List[int]] = {}
+        if offsets:
+            for key, off in offsets.items():
+                segs = self._data.get(key, [])
+                if len(segs) == 1 and len(off) > 1:
+                    valid_offsets[key] = off
+                elif len(segs) > 1:
+                    logger.warning(
+                        "Key '%s' has %d segments with offsets — record mode "
+                        "disabled for this key (multi-shard bin+offsets not "
+                        "supported). Merge shards or use H5/JSONL.",
+                        key,
+                        len(segs),
+                    )
+        self._offsets = valid_offsets
+        if valid_offsets:
+            record_counts = [len(v) - 1 for v in valid_offsets.values()]
+            self._num_records = min(record_counts) if record_counts else 0
+        elif self.segments_are_records:
+            per_record_counts = []
+            for key, tensors in self._data.items():
+                if tensors and isinstance(tensors[0], list):
+                    continue
+                per_record_counts.append(len(tensors))
+            self._num_records = min(per_record_counts) if per_record_counts else 0
+        else:
+            self._num_records = 0
+
+
+class Streamable:
+    """Mixin granting raw token-stream access via :meth:`fetch`.
+
+    Stateless trait relying on ``self._data``, ``self._cum``,
+    ``self._length`` maintained by :class:`Store`.  Stream mode is
+    active when the owning store has ``window_size > 0``; for stores
+    that can also serve record access (H5/JSONL/bin+offsets), the
+    ``fetch_record`` API from :class:`Recordable` is used instead.
+    """
+
+    def fetch(
+        self,
+        begin: int,
+        end: int,
+        keys: Union[str, List[str]],
+    ):
+        return _stream_fetch(self, begin, end, keys)
+
+
+def _stream_fetch(self, begin: int, end: int, keys: Union[str, List[str]]):
+    if not getattr(self, "_data", None):
+        raise RuntimeError("Store not loaded")
+    if not (0 <= begin < self._length and 0 <= end <= self._length):
+        raise ValueError(
+            f"Index out of bounds: begin={begin}, end={end}, length={self._length}"
+        )
+    if isinstance(keys, str):
+        return _fetch_stream_key(self, keys, begin, end)
+    return {k: _fetch_stream_key(self, k, begin, end) for k in keys}
+
+
+def _fetch_stream_key(self, key: str, begin: int, end: int) -> Tensor:
+    segments = self._data[key]
+    cum = self._cum[key]
+    seg_start = bisect.bisect_right(cum, begin)
+    seg_end = bisect.bisect_left(cum, end)
+
+    results = []
+    for i in range(seg_start, seg_end + 1):
+        prev = cum[i - 1] if i > 0 else 0
+        s = max(begin - prev, 0)
+        e = min(end - prev, segments[i].shape[0])
+        results.append(segments[i][s:e])
+
+    return results[0] if len(results) == 1 else torch.cat(results, dim=0)
+
+
+class Recordable:
+    """Mixin granting raw record access via :meth:`fetch_record`.
+
+    Stateless trait relying on ``self._data``, ``self._offsets``,
+    ``self._num_records`` maintained by :class:`Store`.
+    """
+
+    def fetch_record(
+        self,
+        index: int,
+        keys: Union[str, List[str]],
+    ):
+        return _record_fetch(self, index, keys)
+
+
+def _record_fetch(self, index: int, keys: Union[str, List[str]]):
+    if not getattr(self, "_data", None) and self._num_records == 0:
+        raise RuntimeError("Store not loaded")
+    if not 0 <= index < self._num_records:
+        raise ValueError(
+            f"Record index out of bounds: {index}, num_records={self._num_records}"
+        )
+    if isinstance(keys, str):
+        return _fetch_record_key(self, keys, index)
+    return {k: _fetch_record_key(self, k, index) for k in keys}
+
+
+def _fetch_record_key(self, key: str, index: int):
+    offsets = self._offsets.get(key)
+    if offsets:
+        start = offsets[index]
+        end = (
+            offsets[index + 1]
+            if index + 1 < len(offsets)
+            else self._data[key][0].shape[0]
+        )
+        return self._data[key][0][start:end]
+    return self._data[key][index]
+
 
 class StoreFactory(BaseFactory["Store"]):
-    """Factory for creating Store instances by type name.
-
-    Example::
-
-        @StoreFactory.register("custom")
-        class CustomStore(Store):
-            ...
-    """
+    """Factory for creating Store instances by type name."""
 
 
 @StoreFactory.register("h5")
-class H5Store(Store):
-    """HDF5-based storage backend (pre-tokenized data)."""
+class H5Store(Store, Streamable, Recordable):
+    """HDF5-based storage backend (pre-tokenized data).
 
-    def load(self, path: str):
+    Each key is stored as a group of per-record datasets (``data_0``,
+    ``data_1``, …).  Supports both access modes:
+
+    - **Stream**: ``fetch(begin, end, key)`` and ``store[i]`` slice
+      across concatenated records via ``_cum`` — used by SEQ/SFT.
+    - **Record**: ``fetch_record(i, key)`` and ``store[i]`` (when
+      ``window_size == 0``) index ``_data[key]`` directly — used by
+      DPO/GRPO.
+    """
+
+    segments_are_records = True
+
+    def __init__(
+        self,
+        window_size: int = 0,
+        stride: Optional[int] = None,
+    ):
+        super().__init__(window_size=window_size, stride=stride)
+
+    def load(self, path: str, **kwargs):
         self._normalize(load_h5(path))
 
 
 @StoreFactory.register("bin")
-class MmapStore(Store):
+class MmapStore(Store, Streamable, Recordable):
     """Memory-mapped binary storage backend.
 
     Each key is a single .bin file backed by ``np.memmap(mode="r")``.
     No per-process memory duplication — all DataLoader workers share the
     same OS page-cache pages.
 
-    Format on disk::
+    Supports both access modes:
 
-        data_root/
-          meta.json          # {key: {shape, dtype}, ...}
-          <key>.bin          # raw numpy array, one per key
+    - **Stream**: always available via :meth:`fetch`.
+    - **Record** (``fetch_record(i, key)``): only when ``meta.json``
+      contains per-record ``offsets`` (written via
+      ``save_bin(..., record_keys=...)``).  Legacy bin files without
+      offsets have ``num_records == 0`` and ``len(store)`` reflects the
+      windowed sample count when ``window_size > 0``.
+
+    ``segments_are_records`` is ``False`` here (bin segments are
+    contiguous streams, not per-record) — record access is driven
+    purely by ``_offsets``.
     """
 
-    def load(self, path: str):
+    segments_are_records = False
+
+    def __init__(
+        self,
+        window_size: int = 0,
+        stride: Optional[int] = None,
+    ):
+        super().__init__(window_size=window_size, stride=stride)
+        self._mmap_refs: List[Tensor] = []
+
+    def load(self, path: str, **kwargs):
         self._mmap_refs = []
         root = Path(path)
         all_raw: Dict[str, List[Tensor]] = {}
+        all_offsets: Dict[str, List[int]] = {}
         meta_paths = [
             Path(p) for p in glob.glob(str(root / "**" / "meta.json"), recursive=True)
         ]
         for meta_path in meta_paths:
             raw = load_bin(str(meta_path.parent))
+            off = load_bin_offsets(str(meta_path.parent))
             for key, tensors in raw.items():
                 if key not in all_raw:
                     all_raw[key] = []
                 all_raw[key].extend(tensors)
+            for key, o in off.items():
+                if key not in all_offsets:
+                    all_offsets[key] = []
+                all_offsets[key].extend(o)
         if not meta_paths:
             raise FileNotFoundError(f"No meta.json found under {path}")
-        self._normalize(all_raw)
+        self._normalize(all_raw, offsets=all_offsets or None)
         for tensors in self._data.values():
             self._mmap_refs.extend(tensors)
 
 
-@StoreFactory.register("jsonl")
-class JsonlStore(Store):
-    """On-the-fly tokenization store for raw JSONL files.
+class JsonlSource:
+    """Read raw JSON records from a ``.jsonl`` file or directory.
 
-    A JSONL dataset directory contains ``*.jsonl`` files plus a
-    ``dataset_config.json`` file that follows the same schema as
-    :class:`PipelineConfig` with an additional ``tokenizer_path`` field.
-    Records are tokenized when the store is loaded and concatenated into
-    segmented tensors matching the key layout expected by the dataset
-    classes (``sequence``, ``loss_mask``, ``position_ids``, ...).
+    A thin reader used by :class:`JsonlStore` in processor mode — holds
+    no tokenizer, performs no tokenisation, just yields dicts.
+    """
+
+    def __init__(self, path: str):
+        self.path = Path(path)
+        self._records: Optional[List[dict]] = None
+
+    def load(self) -> List[dict]:
+        if self._records is None:
+            self._records = self._read(self.path)
+        return self._records
+
+    @staticmethod
+    def _read(root: Path) -> List[dict]:
+        if root.is_file():
+            return JsonlSource._read_file(root)
+        return JsonlSource._read_dir(root)
+
+    @staticmethod
+    def _read_file(path: Path) -> List[dict]:
+        records: List[dict] = []
+        with open(path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    records.append(json.loads(line))
+                except json.JSONDecodeError:
+                    logger.warning("Failed to parse JSON line in %s, skipping", path)
+        return records
+
+    @staticmethod
+    def _read_dir(root: Path) -> List[dict]:
+        records: List[dict] = []
+        for jsonl_path in sorted(root.glob("*.jsonl")):
+            records.extend(JsonlSource._read_file(jsonl_path))
+        return records
+
+
+@StoreFactory.register("jsonl")
+class JsonlStore(Store, Streamable, Recordable):
+    """JSONL reader with two tokenisation modes.
+
+    A JSONL dataset is a ``.jsonl`` file or a directory of ``*.jsonl``
+    files plus (optionally) a ``dataset_config.json`` describing the
+    tokenization pipeline.
+
+    Two modes, selected at :meth:`load` time:
+
+    - **Eager** (default): applies a :class:`TokenizeTransform` to every
+      record at load time and registers per-key tensors via
+      ``_normalize``.  Both ``fetch`` (stream) and ``fetch_record``
+      (record) work.
+    - **Lazy** (``processor=fn`` passed): keeps raw records and defers
+      tokenisation to ``fetch_record``.  Only record access works —
+      ``len(store)`` returns ``num_records``; stream primitives raise.
     """
 
     CONFIG_NAME = "dataset_config.json"
+    segments_are_records = True
 
-    def load(self, path: str):
-        root = Path(path)
-        config_path = root / self.CONFIG_NAME
-        if not config_path.exists():
-            raise FileNotFoundError(
-                f"JSONL dataset config not found: {config_path}. "
-                f"Expected {self.CONFIG_NAME} alongside *.jsonl files."
+    def __init__(
+        self,
+        window_size: int = 0,
+        stride: Optional[int] = None,
+    ):
+        super().__init__(window_size=window_size, stride=stride)
+        self._source: Optional[JsonlSource] = None
+        self._processor: Optional[Callable[[dict], Dict[str, Tensor]]] = None
+        self._keys_cache: Optional[List[str]] = None
+
+    def load(self, path: str, transform=None, processor=None, **kwargs):
+        self._source = JsonlSource(path)
+        records = self._source.load()
+
+        if processor is not None:
+            self._processor = processor
+            self._num_records = len(records)
+            return
+
+        if transform is None:
+            root = Path(path)
+            config_path = root / self.CONFIG_NAME if root.is_dir() else None
+            if config_path is None or not config_path.exists():
+                raise FileNotFoundError(
+                    f"JSONL dataset config not found. Expected "
+                    f"{self.CONFIG_NAME} alongside *.jsonl files, pass an "
+                    f"explicit transform, or pass processor= for lazy "
+                    f"on-the-fly tokenisation."
+                )
+            transform = TokenizeTransform.from_config_file(str(config_path))
+
+        transformed = transform.apply(records)
+        self._normalize(transformed)
+
+    @property
+    def keys(self) -> List[str]:
+        if self._processor is not None:
+            if self._keys_cache is None and self._num_records > 0:
+                sample = self._processor(self._source.load()[0])
+                self._keys_cache = list(sample.keys())
+            return self._keys_cache or []
+        return list(self._data.keys())
+
+    def fetch_record(self, index: int, keys: Union[str, List[str]]):
+        if self._processor is not None:
+            if not 0 <= index < self._num_records:
+                raise ValueError(
+                    f"Record index out of bounds: {index}, "
+                    f"num_records={self._num_records}"
+                )
+            record = self._source.load()[index]
+            data = self._processor(record)
+            if isinstance(keys, str):
+                return data[keys]
+            return {k: data[k] for k in keys}
+        return _record_fetch(self, index, keys)
+
+    def fetch(self, begin: int, end: int, keys: Union[str, List[str]]):
+        if self._processor is not None:
+            raise RuntimeError(
+                "JsonlStore in lazy (processor) mode does not support "
+                "stream fetch(); use fetch_record() instead."
             )
+        return _stream_fetch(self, begin, end, keys)
 
-        with open(config_path, "r", encoding="utf-8") as f:
-            raw_config = json.load(f)
-
-        tokenizer_path = raw_config.pop("tokenizer_path", None) or str(root)
-        self.config = PipelineConfig.from_dict(raw_config)
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-        mask_builder = MaskBuilderFactory.create("sectioned")
-        position_strategy = PositionIdStrategyFactory.create(
-            self.config.output.position_ids_mode
-        )
-
-        raw: Dict[str, List[Tensor]] = {}
-        doc_sequences: List[List[int]] = []
-
-        def _process_item(item: dict) -> None:
-            nonlocal raw, doc_sequences
-            result = mask_builder.build(item, self.config, tokenizer)
-            if result is None:
-                return
-            result.pop("domain", None)
-            primary_ids = self._primary_ids(result)
-            if not primary_ids:
-                return
-            doc_sequences.append(primary_ids)
-            for key, ids in result.items():
-                if key not in raw:
-                    raw[key] = []
-                if ids and isinstance(ids[0], list):
-                    # GRPO multi-response: List[List[int]] → List[Tensor]
-                    raw[key].append(
-                        [torch.tensor(sub, dtype=self._infer_dtype(sub)) for sub in ids]
-                    )
-                else:
-                    raw[key].append(torch.tensor(ids, dtype=self._infer_dtype(ids)))
-
-        for jsonl_path in sorted(root.glob("*.jsonl")):
-            with open(jsonl_path, "r", encoding="utf-8") as f:
-                for line in f:
-                    line = line.strip()
-                    if not line:
-                        continue
-                    try:
-                        item = json.loads(line)
-                    except json.JSONDecodeError:
-                        logger.warning(
-                            "Failed to parse JSON line in %s, skipping", jsonl_path
-                        )
-                        continue
-                    _process_item(item)
-
-        for json_path in sorted(root.glob("*.json")):
-            if json_path.name == self.CONFIG_NAME:
-                continue
-            with open(json_path, "r", encoding="utf-8") as f:
-                try:
-                    data = json.load(f)
-                except json.JSONDecodeError:
-                    logger.warning("Failed to parse JSON file %s, skipping", json_path)
-                    continue
-            if isinstance(data, list):
-                for item in data:
-                    _process_item(item)
-            elif isinstance(data, dict):
-                _process_item(data)
-
-        pos_ids = position_strategy.generate(doc_sequences)
-        if pos_ids:
-            raw["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
-
-        self._normalize(raw)
-
-    @staticmethod
-    def _primary_ids(result: dict) -> List[int]:
-        """Return the first flat integer list in *result* as the primary id sequence."""
-        for val in result.values():
-            if isinstance(val, list) and val and isinstance(val[0], int):
-                return val
-        return []
-
-    @staticmethod
-    def _infer_dtype(ids: List) -> torch.dtype:
-        """Infer tensor dtype from the first element of a token/value list."""
-        if ids and isinstance(ids[0], float):
-            return torch.float32
-        return torch.int32
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        if self._processor is not None:
+            return self.fetch_record(index, self._record_keys())
+        return super().__getitem__(index)

astrai/inference/api/tool_parser.py

@@ -7,6 +7,7 @@ Subclasses may optionally consume ``token_ids`` for token-level parsing
 (e.g. Harmony / VLM-style parsers).
 """
 
+import json
 import re
 import uuid
 from abc import ABC, abstractmethod
@@ -117,6 +118,29 @@ def _parse_tool_call_json(json_str: str, complete: bool):
 
     Returns ``(name, args, valid)``.
     """
+    if complete:
+        try:
+            obj = json.loads(json_str)
+        except json.JSONDecodeError:
+            return None, "", False
+        name = obj.get("name")
+        if not isinstance(name, str) or not name:
+            return None, "", False
+        args = obj.get("arguments")
+        if isinstance(args, dict):
+            if not args:
+                args = ""
+            else:
+                args = json.dumps(args, ensure_ascii=False)
+                args = args[1:-1].rstrip()
+        elif isinstance(args, list):
+            args = json.dumps(args, ensure_ascii=False) if args else ""
+        elif isinstance(args, str):
+            pass
+        else:
+            args = str(args) if args is not None else ""
+        return name, args, True
+
     name_match = re.search(r'"name"\s*:\s*"([^"]*)"', json_str)
     if not name_match:
         return None, "", False
@@ -127,8 +151,6 @@ def _parse_tool_call_json(json_str: str, complete: bool):
         return name, "", True
 
     raw = args_match.group(1).rstrip()
-    if complete and raw.endswith("}"):
-        raw = raw[:-1].rstrip()
     if raw.startswith("{"):
         inner = raw[1:].rstrip()
         if inner.endswith("}"):
@@ -156,9 +178,6 @@ def _find_tool_calls(text: str, start_pos: int = 0):
             break
 
         json_str = text[brace:end]
-        if not _TOOL_CALL_HEAD_RE.search(json_str):
-            pos = end
-            continue
 
         name, args, valid = _parse_tool_call_json(json_str, complete=True)
         if not valid or name is None:
@@ -186,7 +205,7 @@ def _find_partial_tool_call(text: str, start_pos: int = 0):
         return None
 
     json_str = text[brace:]
-    if not _TOOL_CALL_HEAD_RE.search(json_str):
+    if '"name"' not in json_str:
         return None
 
     name, args, valid = _parse_tool_call_json(json_str, complete=False)

astrai/inference/core/cache.py

@@ -300,7 +300,11 @@ class KVCache(ABC):
 
     @abstractmethod
     def bind_tasks(
-        self, task_ids: List[str], total_len: int, device: torch.device
+        self,
+        task_ids: List[str],
+        total_len: int,
+        device: torch.device,
+        write_positions: Optional[Tensor] = None,
     ) -> CacheView: ...
 
     def task_cached(self, task_id: str) -> int:
@@ -399,7 +403,11 @@ class PageCache(KVCache):
             self._pool.record(page_table[i], prompt_ids, i)
 
     def bind_tasks(
-        self, task_ids: List[str], total_len: int, device: torch.device
+        self,
+        task_ids: List[str],
+        total_len: int,
+        device: torch.device,
+        write_positions: Optional[Tensor] = None,
     ) -> PageCacheView:
         page_table = self._table.table_tensor(task_ids, device)
         return PageCacheView(self._storage, page_table, total_len)
@@ -409,23 +417,37 @@ class ContiguousCacheView(CacheView):
     """Contiguous KV-cache view for attention layers."""
 
     def __init__(
-        self, cache: "ContiguousCache", batch_indices: Tensor, total_len: int = 0
+        self,
+        cache: "ContiguousCache",
+        batch_indices: Tensor,
+        total_len: int = 0,
+        write_positions: Optional[Tensor] = None,
     ):
         self._cache = cache
         self._batch_indices = batch_indices
         self._total_len = total_len
+        self._write_positions = write_positions
 
     def write(self, layer_id: int, k: Tensor, v: Tensor):
         seq_len = k.size(1)
-        start_pos = self._total_len - seq_len
         indices = self._batch_indices
-        self._cache.k[layer_id, indices, start_pos : start_pos + seq_len] = k
-        self._cache.v[layer_id, indices, start_pos : start_pos + seq_len] = v
-        new_len = start_pos + seq_len
-        for s in indices.tolist():
-            cur = self._cache._slot_len.get(s, 0)
-            if new_len > cur:
-                self._cache._slot_len[s] = new_len
+        if self._write_positions is not None and seq_len == 1:
+            pos = self._write_positions
+            self._cache.k[layer_id, indices, pos] = k.squeeze(1)
+            self._cache.v[layer_id, indices, pos] = v.squeeze(1)
+            for s, p in zip(indices.tolist(), pos.tolist()):
+                cur = self._cache._slot_len.get(s, 0)
+                if p + 1 > cur:
+                    self._cache._slot_len[s] = p + 1
+        else:
+            start_pos = self._total_len - seq_len
+            self._cache.k[layer_id, indices, start_pos : start_pos + seq_len] = k
+            self._cache.v[layer_id, indices, start_pos : start_pos + seq_len] = v
+            new_len = start_pos + seq_len
+            for s in indices.tolist():
+                cur = self._cache._slot_len.get(s, 0)
+                if new_len > cur:
+                    self._cache._slot_len[s] = new_len
 
     def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]:
         max_len = max(
@@ -491,9 +513,21 @@ class ContiguousCache(KVCache):
     def task_extend(self, task_id: str, pos: int) -> bool:
         return pos < self.max_seq_len
 
+    def task_cached(self, task_id: str) -> int:
+        slot = self._task_slot.get(task_id)
+        if slot is None:
+            return 0
+        return self._slot_len.get(slot, 0)
+
     def bind_tasks(
-        self, task_ids: List[str], total_len: int, device: torch.device
+        self,
+        task_ids: List[str],
+        total_len: int,
+        device: torch.device,
+        write_positions: Optional[Tensor] = None,
     ) -> ContiguousCacheView:
         slots = [self._task_slot[tid] for tid in task_ids]
         batch_indices = torch.tensor(slots, dtype=torch.long, device=device)
-        return ContiguousCacheView(self, batch_indices, total_len)
+        return ContiguousCacheView(
+            self, batch_indices, total_len, write_positions=write_positions
+        )

astrai/inference/core/executor.py

@@ -106,7 +106,12 @@ class Executor:
         with torch.inference_mode():
             outputs = self.model(
                 input_ids.unsqueeze(1),
-                paged_cache=self.kv_cache.bind_tasks(task_ids, total_len, self.device),
+                paged_cache=self.kv_cache.bind_tasks(
+                    task_ids,
+                    total_len,
+                    self.device,
+                    write_positions=position_ids,
+                ),
                 position_ids=position_ids.unsqueeze(1),
             )
             logits = outputs["logits"][:, -1, :]

astrai/inference/core/scheduler.py

@@ -138,36 +138,33 @@ class InferenceScheduler:
                                 t.task_id, t.prompt_ids, start_logical_page
                             )
 
-                pos_groups: Dict[int, List[Task]] = {}
-                for t in self._task_mgr.get_active_tasks():
-                    pos_groups.setdefault(t.next_pos, []).append(t)
+                decode_tasks = self._task_mgr.get_active_tasks()
 
-                for next_pos in sorted(pos_groups.keys()):
-                    group = sorted(pos_groups[next_pos], key=lambda t: t.task_id)
+                valid: List[Task] = []
+                for t in sorted(decode_tasks, key=lambda t: t.task_id):
+                    if cache.task_extend(t.task_id, t.next_pos):
+                        valid.append(t)
+                    else:
+                        t.status = TaskStatus.ABORTED
+                        self._task_mgr.invoke_callback(t.task_id, STOP)
 
-                    valid: List[Task] = []
-                    for t in group:
-                        if cache.task_extend(t.task_id, t.next_pos):
-                            valid.append(t)
-                        else:
-                            t.status = TaskStatus.ABORTED
+                if valid:
+                    next_tokens = self._executor.execute_decode(valid)
+
+                    for t, ntok in zip(valid, next_tokens):
+                        t.output_ids.append(ntok)
+                        t.output_tokens += 1
+                        new_text = t.decode_new_token(self._task_mgr.tokenizer)
+                        if new_text:
+                            self._task_mgr.invoke_callback(t.task_id, new_text)
+
+                    for t in valid:
+                        if t.is_finished(stop_ids):
+                            remaining = t.flush_remaining(self._task_mgr.tokenizer)
+                            if remaining:
+                                self._task_mgr.invoke_callback(t.task_id, remaining)
                             self._task_mgr.invoke_callback(t.task_id, STOP)
 
-                    if valid:
-                        next_tokens = self._executor.execute_decode(valid)
-
-                        for t, ntok in zip(valid, next_tokens):
-                            t.output_ids.append(ntok)
-                            t.output_tokens += 1
-                            self._task_mgr.invoke_callback(
-                                t.task_id,
-                                self._task_mgr.tokenizer.decode([ntok]),
-                            )
-
-                        for t in valid:
-                            if t.is_finished(stop_ids):
-                                self._task_mgr.invoke_callback(t.task_id, STOP)
-
         except Exception as e:
             self._stop_event.set()
             logger.error(f"Scheduler loop crashed: {e}", exc_info=True)

astrai/inference/core/task.py

@@ -13,6 +13,40 @@ logger = logging.getLogger(__name__)
 STOP = object()
 
 
+class StreamDecoder:
+    """Incremental decoder for byte-level BPE streaming.
+
+    Byte-level BPE may split a single Unicode character (e.g. em-dash,
+    smart quotes) across multiple tokens. Decoding such a token in
+    isolation produces U+FFFD (replacement char). This decoder
+    accumulates token IDs and only emits text once the trailing
+    characters are complete, buffering incomplete multi-byte sequences
+    until the next token arrives.
+    """
+
+    __slots__ = ("_tokenizer", "_ids", "_emitted")
+
+    def __init__(self, tokenizer: AutoTokenizer):
+        self._tokenizer = tokenizer
+        self._ids: List[int] = []
+        self._emitted: str = ""
+
+    def push(self, token_id: int) -> str:
+        """Append a token ID and return newly completed text.
+
+        Returns "" while a multi-byte character is still incomplete.
+        """
+        self._ids.append(token_id)
+        full = self._tokenizer.decode(self._ids, skip_special_tokens=True)
+        if full.endswith("\ufffd"):
+            return ""
+        if len(full) > len(self._emitted):
+            diff = full[len(self._emitted) :]
+            self._emitted = full
+            return diff
+        return ""
+
+
 class TaskStatus(Enum):
     """Task lifecycle states."""
 
@@ -51,6 +85,34 @@ class Task:
         self.output_tokens: int = 0
         self.arrival_time = time.time()
         self.finish_time: Optional[float] = None
+        self._decoder: Optional[StreamDecoder] = None
+
+    def decode_new_token(self, tokenizer: AutoTokenizer) -> str:
+        """Decode the last appended output token, buffering incomplete
+        multi-byte sequences across calls.
+
+        Lazily creates a :class:`StreamDecoder` on first use.
+        """
+        if self._decoder is None:
+            self._decoder = StreamDecoder(tokenizer)
+        return self._decoder.push(self.output_ids[-1])
+
+    def flush_remaining(self, tokenizer: AutoTokenizer) -> str:
+        """Emit any text still buffered in the decoder.
+
+        Called when generation terminates (max_tokens reached, stop
+        sequence, or external removal) to avoid dropping a final
+        incomplete-looking fragment that is actually complete when
+        adjacent to the stop token.
+        """
+        if self._decoder is None or not self.output_ids:
+            return ""
+        full = tokenizer.decode(self.output_ids, skip_special_tokens=True)
+        if len(full) > len(self._decoder._emitted):
+            diff = full[len(self._decoder._emitted) :]
+            self._decoder._emitted = full
+            return diff
+        return ""
 
     @property
     def next_pos(self) -> int:

astrai/inference/engine.py

@@ -82,8 +82,8 @@ class GenerationRequest:
             raise ValueError("top_k must be a non-negative integer")
         if not (0.0 <= top_p <= 1.0):
             raise ValueError("top_p must be a float between 0.0 and 1.0")
-        if not (isinstance(temperature, (int, float)) and temperature > 0):
-            raise ValueError("temperature must be a positive number")
+        if not (isinstance(temperature, (int, float)) and temperature >= 0):
+            raise ValueError("temperature must be a non-negative number")
         if not (
             isinstance(frequency_penalty, (int, float))
             and -2.0 <= frequency_penalty <= 2.0

astrai/inference/sample.py

@@ -263,6 +263,12 @@ class SamplingPipeline(BaseSamplingStrategy):
             logits = strategy.apply(logits, filter_value, input_ids, input_mask)
         return logits
 
+    @staticmethod
+    def _is_greedy(temperature: Union[float, Tensor]) -> bool:
+        if isinstance(temperature, Tensor):
+            return temperature.numel() == 1 and temperature.item() == 0
+        return temperature == 0
+
     @torch.inference_mode()
     def sample(
         self,
@@ -273,6 +279,9 @@ class SamplingPipeline(BaseSamplingStrategy):
     ) -> Tensor:
         """Apply strategies then sample (softmax + multinomial).
 
+        Short-circuits to ``argmax`` when temperature is exactly 0
+        (deterministic / greedy decode).
+
         Args:
             logits: Raw logits ``[batch, vocab_size]``.
             input_ids: Previously generated token IDs ``[batch, seq_len]``.
@@ -281,6 +290,11 @@ class SamplingPipeline(BaseSamplingStrategy):
         Returns:
             Sampled token IDs ``[batch]``.
         """
+        for s in self.strategies:
+            if isinstance(s, TemperatureStrategy) and self._is_greedy(s.temperature):
+                return logits.argmax(dim=-1)
+            break
+
         return torch.multinomial(
             torch.softmax(
                 self.apply(logits, filter_value, input_ids, input_mask), dim=-1
@@ -304,6 +318,9 @@ def sample(
 
     Shortcut for ``SamplingPipeline(...).sample(logits)``.
 
+    When **temperature** is exactly 0 (scalar or single-element tensor)
+    the function short-circuits to ``argmax`` for deterministic decode.
+
     Args:
         logits: Raw logits ``[batch, vocab_size]``.
         frequency_penalty: Penalty per occurrence for repeated tokens
@@ -314,6 +331,8 @@ def sample(
     Returns:
         Sampled token IDs ``[batch]``.
     """
+    if SamplingPipeline._is_greedy(temperature):
+        return logits.argmax(dim=-1)
     return SamplingPipeline(
         [
             TemperatureStrategy(temperature),

astrai/parallel/executor.py

@@ -148,7 +148,14 @@ class BaseExecutor:
     def grad_accum_steps(self) -> int:
         return self.gradient_state.num_steps
 
-    def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
+    def clip_grad_norm(self, model: nn.Module, max_norm: Optional[float]) -> float:
+        if max_norm is None:
+            total_norm = torch.norm(
+                torch.stack(
+                    [p.grad.norm(2) for p in model.parameters() if p.grad is not None]
+                )
+            )
+            return total_norm.item()
         total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
         if isinstance(total_norm, torch.Tensor):
             return total_norm.item()
@@ -224,13 +231,6 @@ class DDPExecutor(BaseExecutor):
             return model.module.state_dict()
         return model.state_dict()
 
-    def _gather_state_dict(self, model: nn.Module):
-        if not self.use_distributed:
-            return self.unwrap_model(model)
-        if get_rank() != 0:
-            return None
-        return self.unwrap_model(model)
-
 
 @ExecutorFactory.register("fsdp")
 class FSDPExecutor(BaseExecutor):
@@ -289,7 +289,9 @@ class FSDPExecutor(BaseExecutor):
             return model.no_sync()
         return contextlib.nullcontext()
 
-    def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
+    def clip_grad_norm(self, model: nn.Module, max_norm: Optional[float]) -> float:
+        if max_norm is None:
+            return super().clip_grad_norm(model, max_norm)
         if isinstance(model, FSDP) and self.use_distributed:
             total_norm = model.clip_grad_norm_(max_norm)
             if isinstance(total_norm, torch.Tensor):

astrai/preprocessing/__init__.py

@@ -8,12 +8,14 @@ from astrai.preprocessing.builder import (
 from astrai.preprocessing.packing import (
     PackingStrategy,
     PackingStrategyFactory,
+    plan_bfd,
 )
 from astrai.preprocessing.pipeline import Pipeline, filter_by_length
 from astrai.preprocessing.position_id import (
     PositionIdStrategy,
     PositionIdStrategyFactory,
 )
+from astrai.preprocessing.transform import TokenizeTransform
 from astrai.preprocessing.writer import (
     StoreWriter,
     StoreWriterFactory,
@@ -32,5 +34,7 @@ __all__ = [
     "SingleOutputMaskBuilder",
     "StoreWriter",
     "StoreWriterFactory",
+    "TokenizeTransform",
     "filter_by_length",
+    "plan_bfd",
 ]

astrai/preprocessing/core.py

@@ -0,0 +1,124 @@
+"""Shared preprocessing kernel used by both :class:`Pipeline` and
+:class:`TokenizeTransform`.
+
+The two entry points previously duplicated ~60 % of their logic:
+record iteration, mask-builder invocation, primary-id extraction,
+per-key accumulation, dtype inference and position-id generation.
+This module factors out the common core as pure functions so that
+the online (``TokenizeTransform``) and offline (``Pipeline``) paths
+stay in lockstep.
+"""
+
+from itertools import chain
+from typing import Dict, Iterator, List, Optional
+
+import torch
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.preprocessing.builder import MaskBuilderFactory
+from astrai.preprocessing.position_id import PositionIdStrategyFactory
+from astrai.tokenize import AutoTokenizer
+
+
+def build_preprocessing_components(config: PipelineConfig, tokenizer_path: str):
+    """Load tokenizer, mask builder and position-id strategy together.
+
+    Both ``Pipeline`` and ``TokenizeTransform`` need the same triple;
+    centralising the construction avoids drift (e.g. one path forgetting
+    to create the position-id strategy).
+    """
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+    mask_builder = MaskBuilderFactory.create("sectioned")
+    position_strategy = PositionIdStrategyFactory.create(
+        config.output.position_ids_mode
+    )
+    return tokenizer, mask_builder, position_strategy
+
+
+def primary_ids(result: dict) -> List[int]:
+    """Return the first flat int-list value in *result*.
+
+    Used for token counting and position-id generation when the
+    primary key name is not known (DPO uses ``chosen``, GRPO uses
+    ``prompts``, SFT uses ``sequence``).
+    """
+    for val in result.values():
+        if isinstance(val, list) and val and isinstance(val[0], int):
+            return val
+    return []
+
+
+def infer_dtype(ids: List) -> torch.dtype:
+    """Float values become float32, everything else int32."""
+    if ids and isinstance(ids[0], float):
+        return torch.float32
+    return torch.int32
+
+
+def iter_raw_records(
+    records: List[dict],
+    mask_builder,
+    config: PipelineConfig,
+    tokenizer,
+) -> Iterator[dict]:
+    """Yield mask-builder output dicts for each record, skipping failures.
+
+    Drops ``domain`` from the result (callers that need it should read
+    it before calling this).  Each yielded dict maps a key
+    (``sequence``, ``chosen``, ``responses``…) to either a flat
+    ``List[int]`` or a nested ``List[List[int]]`` (GRPO responses/masks).
+    """
+    for item in records:
+        result = mask_builder.build(item, config, tokenizer)
+        if result is None:
+            continue
+        result.pop("domain", None)
+        if not primary_ids(result):
+            continue
+        yield result
+
+
+def to_per_record_tensors(
+    raw: Dict[str, list],
+) -> Dict[str, List[torch.Tensor]]:
+    """Convert an accumulated ``{key: [per-record ids]}`` dict to tensors.
+
+    Handles three shapes transparently:
+
+    - ``List[int]`` per record (``sequence``, ``chosen``…) → one tensor per record.
+    - ``List[List[int]]`` per record (GRPO ``responses``/``masks``) → one
+      ``List[Tensor]`` per record (nested), preserving the per-response
+      boundary so downstream code can index responses individually.
+    - ``List[int]`` for the whole shard (pre-packed keys) → single tensor.
+
+    The detection mirrors the previous inline logic in
+    ``Pipeline._flush`` and ``TokenizeTransform.apply``.
+    """
+    tensors: Dict[str, List[torch.Tensor]] = {}
+    for key, ids_list in raw.items():
+        if ids_list and isinstance(ids_list[0], list):
+            tensors[key] = [
+                [torch.tensor(sub, dtype=infer_dtype(sub)) for sub in ids]
+                if ids and isinstance(ids[0], list)
+                else torch.tensor(ids, dtype=infer_dtype(ids))
+                for ids in ids_list
+            ]
+        else:
+            tensors[key] = [
+                torch.tensor(list(chain.from_iterable(ids_list)), dtype=torch.int32)
+            ]
+    return tensors
+
+
+def build_position_ids(
+    sequences: List[List[int]],
+    strategy,
+) -> Optional[List[int]]:
+    """Generate position ids for *sequences* using *strategy*.
+
+    Returns ``None`` when the strategy produces no ids (e.g. ``none``
+    mode), so callers can skip attaching the key instead of storing
+    an empty list.
+    """
+    pos_ids = strategy.generate(sequences)
+    return pos_ids or None

astrai/preprocessing/packing.py

@@ -19,6 +19,43 @@ def _truncate(seq: List[int], max_len: int, mode: str) -> List[int]:
     return seq[:max_len]
 
 
+def plan_bfd(
+    sequences: List[List[int]], max_packed_len: int, truncation_mode: str = "keep_start"
+) -> List[List[int]]:
+    """Best-Fit Decreasing bin packing of *sequences* into bins.
+
+    Returns a list of bins, each bin a list of original indices into
+    *sequences*.  Bin capacities are respected on the *truncated*
+    length of each sequence (so a sequence longer than
+    *max_packed_len* counts at *max_packed_len*).
+
+    Pure index-based so callers can apply the same plan to any
+    aligned key (``loss_mask``, ``position_ids``…).
+    """
+    n = len(sequences)
+    order = sorted(range(n), key=lambda i: len(sequences[i]), reverse=True)
+    bins: List[List[int]] = []
+    bin_lengths: List[int] = []
+
+    for orig_idx in order:
+        seq_len = len(_truncate(sequences[orig_idx], max_packed_len, truncation_mode))
+        best_bin = None
+        best_remain = max_packed_len + 1
+        for i, bl in enumerate(bin_lengths):
+            remain = max_packed_len - bl
+            if seq_len <= remain < best_remain:
+                best_remain = remain
+                best_bin = i
+        if best_bin is not None:
+            bins[best_bin].append(orig_idx)
+            bin_lengths[best_bin] += seq_len
+        else:
+            bins.append([orig_idx])
+            bin_lengths.append(seq_len)
+
+    return bins
+
+
 class PackingStrategy(ABC):
     """Reorder and truncate sequences within a shard."""
 
@@ -70,7 +107,7 @@ class BFDPacking(PackingStrategy):
         sequences = keys.get("sequence", [])
         if not sequences:
             return keys
-        bins = self._plan(sequences, max_packed_len, truncation_mode)
+        bins = plan_bfd(sequences, max_packed_len, truncation_mode)
 
         packed: Dict[str, List[List[int]]] = {}
         for k, vals in keys.items():
@@ -91,35 +128,6 @@ class BFDPacking(PackingStrategy):
             result.extend(vals[i])
         return result
 
-    @staticmethod
-    def _plan(
-        sequences: List[List[int]], max_packed_len: int, truncation_mode: str
-    ) -> List[List[int]]:
-        n = len(sequences)
-        order = sorted(range(n), key=lambda i: len(sequences[i]), reverse=True)
-        bins: List[List[int]] = []
-        bin_lengths: List[int] = []
-
-        for orig_idx in order:
-            seq_len = len(
-                _truncate(sequences[orig_idx], max_packed_len, truncation_mode)
-            )
-            best_bin = None
-            best_remain = max_packed_len + 1
-            for i, bl in enumerate(bin_lengths):
-                remain = max_packed_len - bl
-                if seq_len <= remain < best_remain:
-                    best_remain = remain
-                    best_bin = i
-            if best_bin is not None:
-                bins[best_bin].append(orig_idx)
-                bin_lengths[best_bin] += seq_len
-            else:
-                bins.append([orig_idx])
-                bin_lengths.append(seq_len)
-
-        return bins
-
 
 @PackingStrategyFactory.register("bfd_split")
 class BFDSplitPacking(BFDPacking):

astrai/preprocessing/pipeline.py

@@ -4,6 +4,10 @@ Composes a :class:`BaseMaskBuilder` (selected by ``input.type``) with
 sharding and flush to ``.h5`` / ``.bin`` storage.  Packing, position-id
 generation and storage writing are each delegated to pluggable strategies,
 dispatched by configuration keys.
+
+Record iteration, mask building, primary-id extraction and per-key
+accumulation are shared with :class:`TokenizeTransform` via the
+:mod:`astrai.preprocessing.core` helpers.
 """
 
 import json
@@ -17,11 +21,13 @@ import torch
 import tqdm
 
 from astrai.config.preprocess_config import PipelineConfig
-from astrai.preprocessing.builder import MaskBuilderFactory
+from astrai.preprocessing.core import (
+    build_preprocessing_components,
+    iter_raw_records,
+    primary_ids,
+)
 from astrai.preprocessing.packing import PackingStrategyFactory
-from astrai.preprocessing.position_id import PositionIdStrategyFactory
 from astrai.preprocessing.writer import StoreWriterFactory
-from astrai.tokenize import AutoTokenizer
 
 logger = logging.getLogger(__name__)
 
@@ -64,20 +70,18 @@ class Pipeline:
         self.output_dir = output_dir
         self.tokenizer_path = tokenizer_path
 
-        self.mask_builder = MaskBuilderFactory.create("sectioned")
+        self.tokenizer, self.mask_builder, self._position_id = (
+            build_preprocessing_components(config, tokenizer_path)
+        )
         self._packer = PackingStrategyFactory.create(
             config.preprocessing.packing_strategy
         )
-        self._position_id = PositionIdStrategyFactory.create(
-            config.output.position_ids_mode
-        )
         self._writer = StoreWriterFactory.create(config.output.storage_format)
 
     def transform(self, item: dict) -> Optional[dict]:
-        return self.mask_builder.build(item, self.config, self._tokenizer)
+        return self.mask_builder.build(item, self.config, self.tokenizer)
 
     def run(self):
-        self._tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_path)
         domains: dict = defaultdict(lambda: defaultdict(list))
         total_tokens = 0
         shard_idx: dict[str, int] = defaultdict(int)
@@ -102,14 +106,7 @@ class Pipeline:
                 continue
 
             domain = result.pop("domain", "__default__")
-
-            is_multi = bool(getattr(self.config.input, "sources", None))
-            if is_multi:
-                ids = self._primary_ids(result)
-            else:
-                ids = result.pop("sequence")
-                result["sequence"] = ids
-
+            ids = primary_ids(result)
             if not ids:
                 continue
 
@@ -129,15 +126,6 @@ class Pipeline:
         if total_tokens > 0:
             self._flush(domains, shard_idx)
 
-    @staticmethod
-    def _primary_ids(result: dict) -> list:
-        """Return the first list-valued entry in *result* as the primary id
-        sequence for token counting."""
-        for val in result.values():
-            if isinstance(val, list) and val and isinstance(val[0], int):
-                return val
-        return []
-
     @staticmethod
     def _align_bucket(bucket: dict, result: dict, ids: list):
         """Pad previously-accumulated keys that are missing from *result*."""
@@ -170,39 +158,12 @@ class Pipeline:
             original_sequences = keys.get("sequence", [])
             mode = self.config.output.position_ids_mode
 
-            if mode == "doc_reset" and original_sequences:
-                keys["position_ids"] = [list(range(len(s))) for s in original_sequences]
-
+            keys = self._inject_doc_reset_position_ids(keys, mode, original_sequences)
             keys = self._packer.apply(dict(keys), pp.max_packed_len, pp.truncation_mode)
-
-            tensors: Dict[str, List[torch.Tensor]] = {}
-            for key, ids_list in keys.items():
-                dt = _STR_TO_DTYPE.get(
-                    self.config.output.dtype.get(key, "int32"), torch.int32
-                )
-                # GRPO multi-response keys store List[List[int]] per record
-                # (responses/masks).  Rewards store List[float] per record.
-                # Both produce List[Tensor] (one tensor per record), but
-                # responses need inner flattening while rewards do not.
-                if ids_list and isinstance(ids_list[0], list):
-                    tensors[key] = [
-                        torch.tensor(
-                            list(chain.from_iterable(ids))
-                            if ids and isinstance(ids[0], list)
-                            else ids,
-                            dtype=dt,
-                        )
-                        for ids in ids_list
-                    ]
-                else:
-                    tensors[key] = [
-                        torch.tensor(list(chain.from_iterable(ids_list)), dtype=dt)
-                    ]
-
-            if mode == "continuous" and original_sequences:
-                pos_ids = self._position_id.generate(keys.get("sequence", []))
-                if pos_ids:
-                    tensors["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
+            tensors = self._to_tensors(keys)
+            tensors = self._inject_continuous_position_ids(
+                tensors, mode, keys.get("sequence", [])
+            )
 
             self._writer.save(self.output_dir, domain, idx, tensors)
             shard_idx[domain] = idx + 1
@@ -212,3 +173,76 @@ class Pipeline:
                 f"  saved {domain}/shard_{idx:04d}  "
                 f"({tensors[first_key][0].numel():,} tokens)"
             )
+
+    def _inject_doc_reset_position_ids(
+        self,
+        keys: Dict[str, list],
+        mode: str,
+        original_sequences: List[List[int]],
+    ) -> Dict[str, list]:
+        """Attach per-document position_ids before packing (``doc_reset``).
+
+        ``doc_reset`` position ids must enter the packer so that each
+        packed bin concatenates the per-doc ranges in bin order.  The
+        per-record structure ``[range(len(s)) for s in seqs]`` is required
+        by the packer (it concatenates per-record lists per bin); the
+        ``PositionIdStrategy.generate`` flattens, so it cannot be used
+        directly here — it is only consulted for the ``continuous``
+        post-packing path.
+        """
+        if mode != "doc_reset" or not original_sequences:
+            return keys
+        keys["position_ids"] = [list(range(len(s))) for s in original_sequences]
+        return keys
+
+    def _inject_continuous_position_ids(
+        self,
+        tensors: Dict[str, List[torch.Tensor]],
+        mode: str,
+        packed_sequences: List[List[int]],
+    ) -> Dict[str, List[torch.Tensor]]:
+        """Attach a single continuous position_ids tensor after packing.
+
+        ``continuous`` mode spans the whole shard (post-packing), so it
+        cannot participate in bin packing — it is computed from the
+        packed sequences and appended directly to the tensor dict.
+        """
+        if mode != "continuous" or not packed_sequences:
+            return tensors
+        pos_ids = self._position_id.generate(packed_sequences)
+        if pos_ids:
+            tensors["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
+        return tensors
+
+    def _to_tensors(self, keys: Dict[str, list]) -> Dict[str, List[torch.Tensor]]:
+        """Convert packed per-key id lists to tensors.
+
+        Honours ``config.output.dtype`` overrides per key; falls back to
+        ``int32``.  Handles three shapes (see
+        :func:`astrai.preprocessing.core.to_per_record_tensors` for the
+        equivalent online-path helper):
+        - ``List[int]`` per record → one tensor per record.
+        - ``List[List[int]]`` per record (GRPO responses/masks) → one tensor
+          per record, inner lists flattened.
+        - ``List[int]`` for the whole shard (pre-packed keys) → single tensor.
+        """
+        tensors: Dict[str, List[torch.Tensor]] = {}
+        for key, ids_list in keys.items():
+            dt = _STR_TO_DTYPE.get(
+                self.config.output.dtype.get(key, "int32"), torch.int32
+            )
+            if ids_list and isinstance(ids_list[0], list):
+                tensors[key] = [
+                    torch.tensor(
+                        list(chain.from_iterable(ids))
+                        if ids and isinstance(ids[0], list)
+                        else ids,
+                        dtype=dt,
+                    )
+                    for ids in ids_list
+                ]
+            else:
+                tensors[key] = [
+                    torch.tensor(list(chain.from_iterable(ids_list)), dtype=dt)
+                ]
+        return tensors

astrai/preprocessing/transform.py

@@ -0,0 +1,92 @@
+"""Tokenization transform for JSONL record streams.
+
+Bridges the Reader layer (``JsonlStore`` reads raw JSON records) and the
+Dataset layer (expects per-record tensors).  Holds the tokenizer,
+mask-builder and position-id strategy together so that I/O code stays
+free of model dependencies.
+
+The record-processing core (mask building, primary-id extraction,
+per-key tensorisation, position-id generation) is shared with
+:class:`astrai.preprocessing.pipeline.Pipeline` via the
+:mod:`astrai.preprocessing.core` helpers.
+"""
+
+import json
+from pathlib import Path
+from typing import Dict, List
+
+import torch
+
+from astrai.config.preprocess_config import PipelineConfig
+from astrai.preprocessing.core import (
+    build_position_ids,
+    build_preprocessing_components,
+    iter_raw_records,
+    to_per_record_tensors,
+)
+
+
+class TokenizeTransform:
+    """Tokenize raw JSONL record dicts into per-key tensor lists.
+
+    Owns the three preprocessing concerns that were previously inlined in
+    ``JsonlStore``: tokenization, loss-mask construction and position-id
+    generation.  Constructing it loads the tokenizer, so it is intentionally
+    cheap to pass around once built.
+
+    Args:
+        config: Pipeline config describing sections / masks / position mode.
+        tokenizer_path: Path passed to ``AutoTokenizer.from_pretrained``.
+    """
+
+    def __init__(self, config: PipelineConfig, tokenizer_path: str):
+        self.config = config
+        self.tokenizer, self.mask_builder, self.position_strategy = (
+            build_preprocessing_components(config, tokenizer_path)
+        )
+
+    @classmethod
+    def from_config_file(cls, config_path: str) -> "TokenizeTransform":
+        """Build from a ``dataset_config.json`` file path.
+
+        The config file follows :class:`PipelineConfig` schema with an
+        extra ``tokenizer_path`` field.  When omitted, the config's
+        parent directory is used as the tokenizer path.
+        """
+        root = Path(config_path).parent
+        with open(config_path, "r", encoding="utf-8") as f:
+            raw_config = json.load(f)
+        tokenizer_path = raw_config.pop("tokenizer_path", None) or str(root)
+        config = PipelineConfig.from_dict(raw_config)
+        return cls(config, tokenizer_path)
+
+    def apply(self, records: List[dict]) -> Dict[str, list]:
+        """Tokenize a list of raw record dicts.
+
+        Returns a dict mapping key (``sequence``, ``chosen``, ``responses``,
+        …) to a list of per-record tensors (or nested tensor lists for
+        multi-response keys such as GRPO ``responses``).
+        """
+        raw: Dict[str, list] = {}
+        doc_sequences: List[List[int]] = []
+
+        for result in iter_raw_records(
+            records, self.mask_builder, self.config, self.tokenizer
+        ):
+            primary = None
+            for val in result.values():
+                if isinstance(val, list) and val and isinstance(val[0], int):
+                    primary = val
+                    break
+            if primary is not None:
+                doc_sequences.append(primary)
+            for key, ids in result.items():
+                raw.setdefault(key, []).append(ids)
+
+        tensors = to_per_record_tensors(raw)
+
+        pos_ids = build_position_ids(doc_sequences, self.position_strategy)
+        if pos_ids is not None:
+            tensors["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
+
+        return tensors

astrai/serialization/__init__.py

@@ -19,6 +19,7 @@ from astrai.serialization.checkpoint import (
 )
 from astrai.serialization.dataset import (
     load_bin,
+    load_bin_offsets,
     load_h5,
     save_bin,
     save_h5,
@@ -37,6 +38,7 @@ __all__ = [
     "save_safetensors",
     "save_torch",
     "load_bin",
+    "load_bin_offsets",
     "load_h5",
     "save_bin",
     "save_h5",

astrai/serialization/dataset.py

@@ -3,7 +3,7 @@
 import json
 import os
 from pathlib import Path
-from typing import Dict, List
+from typing import Any, Dict, List, Optional
 
 import h5py
 import numpy as np
@@ -50,12 +50,43 @@ def load_h5(file_path: str, share_memory=True) -> Dict[str, List[Tensor]]:
     return tensor_group
 
 
-def save_bin(file_path: str, tensor_group: Dict[str, List[Tensor]]):
+def save_bin(
+    file_path: str,
+    tensor_group: Dict[str, List[Tensor]],
+    record_keys: Optional[List[str]] = None,
+):
+    """Save tensors as memory-mapped binary files.
+
+    When *record_keys* is provided, those keys are written with per-record
+    cumulative offsets in ``meta.json`` so that ``MmapStore.fetch_record``
+    can slice individual records from the concatenated binary without
+    cross-record concatenation.  Keys not in *record_keys* (e.g. SEQ
+    ``sequence``) are written as a single contiguous stream without
+    offsets, preserving backward compatibility.
+
+    Nested keys (``List[List[Tensor]]`` such as GRPO ``responses``) are
+    not supported in bin format — use H5 for those.
+    """
     os.makedirs(file_path, exist_ok=True)
+    record_keys = set(record_keys or [])
     meta = {}
     for key, tensors in tensor_group.items():
+        if tensors and isinstance(tensors[0], list):
+            raise ValueError(
+                f"Nested key '{key}' (List[List[Tensor]]) is not supported "
+                f"in bin format. Use H5 or JSONL storage instead."
+            )
         cat = torch.cat(tensors, dim=0)
-        meta[key] = {"shape": list(cat.shape), "dtype": str(cat.dtype).split(".")[-1]}
+        entry: Dict[str, Any] = {
+            "shape": list(cat.shape),
+            "dtype": str(cat.dtype).split(".")[-1],
+        }
+        if key in record_keys:
+            offsets = [0]
+            for t in tensors:
+                offsets.append(offsets[-1] + t.shape[0])
+            entry["offsets"] = offsets
+        meta[key] = entry
         np.asarray(cat.cpu().numpy()).tofile(os.path.join(file_path, f"{key}.bin"))
     with open(os.path.join(file_path, "meta.json"), "w") as f:
         json.dump(meta, f)
@@ -69,8 +100,24 @@ def load_bin(file_path: str) -> Dict[str, List[Tensor]]:
         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)]
     return segments
+
+
+def load_bin_offsets(file_path: str) -> Dict[str, List[int]]:
+    """Read per-record cumulative offsets from ``meta.json``.
+
+    Returns an empty dict when no key has offsets (legacy bin files),
+    in which case record-mode access falls back to per-record segment
+    indexing (H5/JSONL layout).
+    """
+    with open(os.path.join(file_path, "meta.json"), "r") as f:
+        meta = json.load(f)
+    offsets: Dict[str, List[int]] = {}
+    for key, info in meta.items():
+        if "offsets" in info:
+            offsets[key] = info["offsets"]
+    return offsets

astrai/tokenize/chat_template.py

@@ -1,3 +1,4 @@
+from functools import cached_property
 from typing import Any, Dict, List, Optional
 
 from jinja2 import Template
@@ -29,7 +30,19 @@ class ChatTemplate:
         self.description = description
         self.default_variables = default_variables or {}
         self.special_tokens = special_tokens or {}
-        self._compiled: Template = Template(template_str)
+
+    @cached_property
+    def _compiled(self) -> Template:
+        """Lazy-compiled Jinja2 template, cached on first access.
+
+        The compiled :class:`~jinja2.Template` holds a dynamically-generated
+        ``root`` render function whose ``__module__`` is ``None``; under
+        ``pickle`` it falls back to ``__main__`` and breaks ``spawn``-based
+        multiprocessing.  By deferring compilation to first access, the
+        default pickle protocol serialises only ``template_str``; each
+        worker rebuilds the cache on first render.
+        """
+        return Template(self.template_str)
 
     @classmethod
     def from_string(

astrai/tokenize/tokenizer.py

@@ -164,7 +164,14 @@ class AutoTokenizer:
           - tokenizer.bos_token   → returns string
           - tokenizer.bos_token_id → returns corresponding integer ID
           - tokenizer.stop_ids → returns list of corresponding integer IDs for all special tokens
+
+        Internal/private attrs are not intercepted: during unpickle
+        ``__dict__`` is empty, so probing ``self._special_token_map``
+        would recurse infinitely.
         """
+        if key.startswith("_"):
+            raise AttributeError(key)
+
         # Handle stop_ids - return IDs for all special tokens
         if key == "stop_ids":
             stop_ids = []

astrai/trainer/strategy.py

@@ -98,7 +98,6 @@ class BaseStrategy(ABC):
         self.model = model
         self.device = device
         self.executor = kwargs.pop("executor", None)
-        self.model_fn = kwargs.pop("model_fn", None)
         self.extra_kwargs = kwargs
 
     @abstractmethod
@@ -225,7 +224,7 @@ class DPOStrategy(BaseStrategy):
         device: str,
         ref_model: nn.Module,
         beta: float = 0.1,
-        reduction: str = "mean",
+        reduction: str = "sum",
         **kwargs,
     ):
         super().__init__(model, device, **kwargs)

astrai/trainer/train_context.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 from torch.utils.data import DataLoader, random_split
 
 from astrai.config.train_config import TrainConfig
-from astrai.dataset import ResumableDistributedSampler
+from astrai.dataset import RDSampler
 from astrai.model.components.lora import inject_lora
 from astrai.parallel.executor import BaseExecutor, ExecutorFactory
 from astrai.parallel.setup import get_current_device, get_rank, get_world_size
@@ -141,7 +141,7 @@ class TrainContextBuilder:
             )
 
         sampler_offset = context.consumed_samples // context.world_size
-        sampler = ResumableDistributedSampler(
+        sampler = RDSampler(
             data_source=train_dataset,
             start_epoch=context.epoch,
             start_iter=sampler_offset,
@@ -154,10 +154,11 @@ class TrainContextBuilder:
             num_workers=cfg.num_workers,
             pin_memory=cfg.pin_memory,
             prefetch_factor=cfg.prefetch_factor,
+            collate_fn=cfg.collate_fn,
         )
 
         if val_dataset is not None:
-            val_sampler = ResumableDistributedSampler(
+            val_sampler = RDSampler(
                 data_source=val_dataset,
                 start_epoch=0,
                 start_iter=0,
@@ -171,6 +172,7 @@ class TrainContextBuilder:
                 num_workers=cfg.num_workers,
                 pin_memory=cfg.pin_memory,
                 prefetch_factor=cfg.prefetch_factor,
+                collate_fn=cfg.collate_fn,
             )
 
         context.model, context.optimizer, context.dataloader, context.scheduler = (
@@ -209,7 +211,6 @@ class TrainContextBuilder:
             model=context.model,
             device=device,
             executor=executor,
-            model_fn=cfg.model_fn,
             **strategy_kwargs,
         )
 

scripts/demo/download.py

@@ -5,7 +5,7 @@ from huggingface_hub import snapshot_download
 
 PROJECT_ROOT = Path(__file__).resolve().parents[2]
 DEFAULT_LOCAL_DIR = Path(PROJECT_ROOT, "params")
-DEFAULT_REPO_ID = "ViperEk/KHAOSZ"
+DEFAULT_REPO_ID = "ViperEkura/AstrAI-V1-instruct"
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(

scripts/demo/generate_batch.py

@@ -26,11 +26,9 @@ def batch_generate():
 
     prompts = [
         tokenizer.apply_chat_template(
-            [
-                {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": q},
-            ],
+            [{"role": "user", "content": q}],
             tokenize=False,
+            add_generation_prompt=True,
         )
         for q in inputs
     ]

scripts/demo/stream_chat.py

@@ -58,8 +58,8 @@ def parse_args():
     parser.add_argument(
         "--system_prompt",
         type=str,
-        default="You are a helpful assistant.",
-        help="Optional system prompt",
+        default="",
+        help="Optional system prompt (default: empty, model not SFT-trained on system role)",
     )
     return parser.parse_args()
 
@@ -73,18 +73,20 @@ def chat():
     model.to(device="cuda", dtype=torch.bfloat16)
     engine = InferenceEngine(model=model, tokenizer=tokenizer)
 
-    messages = [{"role": "system", "content": args.system_prompt}]
-
     while True:
         query = input(">> ")
         if query == "!exit":
             break
 
-        messages.append({"role": "user", "content": query})
+        msgs = []
+        if args.system_prompt:
+            msgs.append({"role": "system", "content": args.system_prompt})
+        msgs.append({"role": "user", "content": query})
+        prompt = tokenizer.apply_chat_template(
+            msgs, tokenize=False, add_generation_prompt=True
+        )
 
         full_response = ""
-        prompt = tokenizer.apply_chat_template(messages, tokenize=False)
-
         for token in engine.generate(
             prompt=prompt,
             stream=True,
@@ -99,7 +101,6 @@ def chat():
             full_response += token
 
         print()
-        messages.append({"role": "assistant", "content": full_response.strip()})
 
 
 if __name__ == "__main__":

scripts/eval/evaluate_humaneval.py

@@ -20,6 +20,7 @@ from typing import Dict, Iterator, List, Optional, Sequence, Tuple
 import numpy as np
 import torch
 import tqdm
+from datasets import load_dataset
 
 from astrai.inference import InferenceEngine
 from astrai.model import AutoModel
@@ -29,9 +30,7 @@ from astrai.tokenize import AutoTokenizer
 # Config
 # ---------------------------------------------------------------------------
 
-HUMANEVAL_URL = (
-    "https://github.com/openai/human-eval/raw/master/data/HumanEval.jsonl.gz"
-)
+HUMANEVAL_HF_DATASET = "openai/openai_humaneval"
 
 STOP_SEQUENCES = [
     "\nclass ",
@@ -64,21 +63,16 @@ class EvalConfig:
     problem_indices: Optional[List[int]] = None
 
 
-def download(url: str, path: str):
+def download(path: str):
     if os.path.exists(path):
         return
-    import gzip
-    import urllib.request
-
     os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
-    print(f"Downloading {url} ...")
-    tmp = path + ".tmp"
-    urllib.request.urlretrieve(url, tmp)
-    with gzip.open(tmp, "rb") as f_in:
-        with open(path, "wb") as f_out:
-            f_out.write(f_in.read())
-    os.remove(tmp)
-    print(f"  saved to {path}")
+    print(f"Downloading HumanEval from HuggingFace ({HUMANEVAL_HF_DATASET}) ...")
+    ds = load_dataset(HUMANEVAL_HF_DATASET, split="test")
+    with open(path, "w", encoding="utf-8") as f:
+        for item in ds:
+            f.write(json.dumps(item, ensure_ascii=False) + "\n")
+    print(f"  saved {len(ds)} problems to {path}")
 
 
 def load_jsonl(path: str) -> List[dict]:
@@ -318,7 +312,7 @@ def run_pipeline(cfg: EvalConfig) -> Dict:
         with open(cfg.test_only, encoding="utf-8") as f:
             generated = json.load(f)
     else:
-        download(HUMANEVAL_URL, cfg.data_path)
+        download(cfg.data_path)
 
         problems = load_jsonl(cfg.data_path)
         if cfg.problem_indices:

scripts/eval/evaluate_ifd.py

@@ -26,28 +26,22 @@ import torch.nn.functional as F
 import tqdm
 
 from astrai.model import AutoModel
+from astrai.preprocessing.packing import plan_bfd
 from astrai.tokenize import AutoTokenizer
 
 
 def _pack_bins(pairs, max_len):
-    """BFD bin packing: pack (c+r) into bins of max total length."""
-    indexed = sorted(enumerate(pairs), key=lambda x: -(len(x[1][0]) + len(x[1][1])))
-    bins = []
-    lengths = []
-    for orig_idx, (c, r) in indexed:
-        size = len(c) + len(r)
-        best_bin = -1
-        for bi, rem in enumerate(lengths):
-            if rem >= size:
-                if best_bin < 0 or rem < lengths[best_bin]:
-                    best_bin = bi
-        if best_bin >= 0:
-            bins[best_bin].append((orig_idx, c, r))
-            lengths[best_bin] -= size
-        else:
-            bins.append([(orig_idx, c, r)])
-            lengths.append(max_len - size)
-    return bins
+    """BFD bin packing: pack (c+r) into bins of max total length.
+
+    Reuses :func:`plan_bfd` so the BFD heuristic stays single-sourced.
+    """
+    # Treat each pair as a single sequence of length len(c)+len(r) for
+    # planning purposes; plan_bfd works on pure lengths.
+    fake_sequences = [[0] * (len(c) + len(r)) for c, r in pairs]
+    plan = plan_bfd(fake_sequences, max_len)
+    return [
+        [(i, pairs[i][0], pairs[i][1]) for i in bin_indices] for bin_indices in plan
+    ]
 
 
 def _resolve_sentinel_ids(tokenizer, sentinel_text):

scripts/eval/evaluate_ifeval.py

@@ -14,21 +14,17 @@ import argparse
 import json
 import os
 import re
-import urllib.request
 from typing import Callable, Dict, List, Optional
 
 import torch
 import tqdm
+from datasets import load_dataset
 
 from astrai.inference import InferenceEngine
 from astrai.model import AutoModel
 from astrai.tokenize import AutoTokenizer
 
-IFEVAL_URL = (
-    "https://raw.githubusercontent.com/google-research/"
-    "google-research/master/instruction_following_eval/data/input_data.jsonl"
-)
-
+IFEVAL_HF_DATASET = "google/IFEval"
 CONSTRAINT_VERIFIERS: Dict[str, Callable[[str, dict], bool]] = {}
 
 
@@ -310,15 +306,12 @@ def download_ifeval(data_path: str):
     if os.path.exists(data_path):
         return
     os.makedirs(os.path.dirname(data_path) or ".", exist_ok=True)
-    print(f"Downloading IFEval from {IFEVAL_URL} ...")
-    tmp = data_path + ".tmp"
-    urllib.request.urlretrieve(IFEVAL_URL, tmp)
-    with open(tmp, "rb") as f_in:
-        content = f_in.read()
-    with open(data_path, "wb") as f_out:
-        f_out.write(content)
-    os.remove(tmp)
-    print(f"  saved to {data_path}")
+    print(f"Downloading IFEval from HuggingFace ({IFEVAL_HF_DATASET}) ...")
+    ds = load_dataset(IFEVAL_HF_DATASET, split="train")
+    with open(data_path, "w", encoding="utf-8") as f:
+        for item in ds:
+            f.write(json.dumps(item, ensure_ascii=False) + "\n")
+    print(f"  saved {len(ds)} items to {data_path}")
 
 
 def load_problems(data_path: str) -> List[dict]:

scripts/eval/evaluate_mmlu.py

@@ -4,18 +4,18 @@ import argparse
 import csv
 import json
 import os
-import shutil
-import tarfile
+import random
+from collections import defaultdict
 
-import requests
 import torch
 import torch.nn.functional as F
 import tqdm
+from datasets import load_dataset
 
 from astrai.model import AutoModel
 from astrai.tokenize import AutoTokenizer
 
-MMLU_URL = "https://people.eecs.berkeley.edu/~hendrycks/data.tar"
+MMLU_HF_DATASET = "cais/mmlu"
 MMLU_SUBJECTS = [
     "abstract_algebra",
     "anatomy",
@@ -77,38 +77,40 @@ MMLU_SUBJECTS = [
 ]
 
 
-def _download_and_extract(url: str, data_dir: str):
-    tar_path = os.path.join(data_dir, "data.tar")
-    os.makedirs(data_dir, exist_ok=True)
-    print(f"Downloading MMLU data from {url}...")
-    resp = requests.get(url, stream=True, timeout=300)
-    resp.raise_for_status()
-    total = int(resp.headers.get("content-length", 0))
-    with tqdm.tqdm(total=total, unit="B", unit_scale=True, desc="  Download") as bar:
-        with open(tar_path, "wb") as f:
-            for chunk in resp.iter_content(chunk_size=8192):
-                f.write(chunk)
-                bar.update(len(chunk))
-    print("Extracting...")
-    with tarfile.open(tar_path, "r") as tf:
-        tf.extractall(data_dir)
-    os.remove(tar_path)
+def _write_subject_csv(data_dir: str, split: str, subject: str, rows: list[dict]):
+    split_dir = os.path.join(data_dir, split)
+    os.makedirs(split_dir, exist_ok=True)
+    path = os.path.join(split_dir, f"{subject}_{split}.csv")
+    with open(path, "w", encoding="utf-8", newline="") as f:
+        writer = csv.writer(f)
+        for row in rows:
+            writer.writerow(row)
 
 
 def download_mmlu(data_dir: str):
-    _download_and_extract(MMLU_URL, data_dir)
-    src = os.path.join(data_dir, "data")
-    if os.path.exists(src):
-        for item in os.listdir(src):
-            src_item = os.path.join(src, item)
-            dst_item = os.path.join(data_dir, item)
-            if os.path.exists(dst_item):
-                if os.path.isdir(dst_item):
-                    shutil.rmtree(dst_item)
-                else:
-                    os.remove(dst_item)
-            os.rename(src_item, dst_item)
-        os.rmdir(src)
+    print(f"Downloading MMLU from HuggingFace ({MMLU_HF_DATASET}) ...")
+    letters = ("A", "B", "C", "D")
+    split_map = {"dev": "dev", "val": "validation", "test": "test"}
+    for local_split, hf_split in split_map.items():
+        ds = load_dataset(MMLU_HF_DATASET, "all", split=hf_split)
+        grouped: dict[str, list[dict]] = defaultdict(list)
+        for item in tqdm.tqdm(ds, desc=f"  {local_split}", leave=False):
+            subject = item["subject"]
+            choices = item["choices"]
+            ans_letter = letters[item["answer"]]
+            grouped[subject].append(
+                [
+                    item["question"],
+                    f"A){choices[0]}",
+                    f"B){choices[1]}",
+                    f"C){choices[2]}",
+                    f"D){choices[3]}",
+                    ans_letter,
+                ]
+            )
+        for subject, rows in grouped.items():
+            _write_subject_csv(data_dir, local_split, subject, rows)
+        print(f"  {local_split}: {len(ds)} items, {len(grouped)} subjects")
     print(f"MMLU data saved to {data_dir}")
 
 
@@ -153,19 +155,22 @@ def build_prompt(question: str, choices: dict, subject: str) -> str:
 
 
 def apply_chat(
-    tokenizer, raw_prompt: str, n_shot: int, dev_data: list[dict] | None
+    tokenizer,
+    raw_prompt: str,
+    n_shot: int,
+    dev_data: list[dict] | None,
+    subject: str = "",
 ) -> str:
     """Wrap raw MMLU prompt in the model's chat template format.
 
-    For few-shot, prepend example Q&A pairs as a second user/assistant exchange.
+    For few-shot, prepend example Q&A pairs as user/assistant exchanges.
+    Few-shot examples use the same subject preamble as the test question to
+    keep the format consistent.
     """
     messages = []
     if n_shot > 0 and dev_data:
         for item in dev_data[:n_shot]:
-            q = f"Question: {item['question']}\n"
-            for k in ("A", "B", "C", "D"):
-                q += f"{k}. {item[k]}\n"
-            q += "Answer:"
+            q = build_prompt(item["question"], item, subject)
             messages.append({"role": "user", "content": q})
             messages.append({"role": "assistant", "content": item["answer"]})
     messages.append({"role": "user", "content": raw_prompt})
@@ -201,6 +206,25 @@ def choice_logprob(
     return score
 
 
+def _permute_choices(item: dict, rng: random.Random) -> tuple[dict, str]:
+    """Shuffle the option order of a question.
+
+    Returns ``(permuted_item, new_answer_letter)``. The question text and
+    the *content* of each choice are unchanged; only which letter (A/B/C/D)
+    maps to which content is shuffled. This neutralises the model's
+    positional bias (e.g. always picking B).
+    """
+    letters = ("A", "B", "C", "D")
+    contents = [item[k] for k in letters]
+    perm = list(letters)
+    rng.shuffle(perm)
+    permuted = {"question": item["question"]}
+    for new_letter, orig_letter in zip(letters, perm):
+        permuted[new_letter] = item[orig_letter]
+    new_answer = letters[perm.index(item["answer"])]
+    return permuted, new_answer
+
+
 def evaluate_subject(
     model,
     tokenizer,
@@ -209,18 +233,24 @@ def evaluate_subject(
     dev_data: list[dict] | None,
     device: str,
     n_shot: int,
+    seed: int = 0,
 ) -> tuple[float, int, int]:
+    rng = random.Random(seed) if seed >= 0 else None
     correct = 0
     total = 0
     for item in tqdm.tqdm(test_data, desc=f"{subject:40s}", leave=False):
-        raw_prompt = build_prompt(item["question"], item, subject)
-        context = apply_chat(tokenizer, raw_prompt, n_shot, dev_data or [])
+        if rng is not None:
+            permuted, answer = _permute_choices(item, rng)
+        else:
+            permuted, answer = item, item["answer"]
+        raw_prompt = build_prompt(permuted["question"], permuted, subject)
+        context = apply_chat(tokenizer, raw_prompt, n_shot, dev_data or [], subject)
         context_ids = tokenizer.encode(context)
         scores = {
             c: choice_logprob(model, tokenizer, context_ids, c, device)
             for c in ("A", "B", "C", "D")
         }
-        if max(scores, key=scores.get) == item["answer"]:
+        if max(scores, key=scores.get) == answer:
             correct += 1
         total += 1
     return correct / total, correct, total
@@ -255,6 +285,12 @@ def main():
         default="bfloat16" if torch.cuda.is_available() else "float32",
         help="Torch dtype",
     )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=0,
+        help="Seed for option permutation (0 to enable, -1 to disable)",
+    )
     args = parser.parse_args()
 
     if args.download or not os.path.exists(args.data_dir):
@@ -286,7 +322,14 @@ def main():
         test_data = load_csv(test_path)
 
         acc, corr, tot = evaluate_subject(
-            model, tokenizer, subject, test_data, dev_data, device, args.n_shot
+            model,
+            tokenizer,
+            subject,
+            test_data,
+            dev_data,
+            device,
+            args.n_shot,
+            seed=args.seed,
         )
         results[subject] = {"accuracy": round(acc, 4), "correct": corr, "total": tot}
         total_correct += corr

scripts/tools/generate.py

@@ -1,8 +1,10 @@
 import argparse
 import json
+import time
 from typing import Optional
 
 import torch
+from tqdm import tqdm
 
 from astrai.inference import InferenceEngine
 from astrai.model import AutoModel
@@ -20,55 +22,102 @@ def processor(
     response_key: str,
     max_tokens: Optional[int],
     batch_size: int,
+    num_samples: int = 1,
+    cache_len: int = 2048,
+    frequency_penalty: float = 0.0,
+    rep_window: int = 64,
 ):
-    # Load model and tokenizer
+    print(f"Loading model from {param_path} ...")
+    t0 = time.time()
     model = AutoModel.from_pretrained(param_path)
     tokenizer = AutoTokenizer.from_pretrained(param_path)
     model.to(device="cuda", dtype=torch.bfloat16)
+    print(f"  model loaded in {time.time() - t0:.1f}s")
 
-    # Create inference engine
     engine = InferenceEngine(
-        model=model, tokenizer=tokenizer, max_batch_size=batch_size
+        model=model,
+        tokenizer=tokenizer,
+        max_batch_size=batch_size * num_samples,
+        max_seq_len=cache_len,
+        max_prompt_len=cache_len,
     )
 
+    print(f"Reading {input_json_file} ...")
     with open(input_json_file, "r", encoding="utf-8") as f:
         input_data = [json.loads(line) for line in f]
 
-    # Check input format: chat messages or raw text
     if input_data and "messages" in input_data[0]:
-        # Chat format: [{"messages": [...]}]
         prompts = [
             tokenizer.apply_chat_template(item["messages"], tokenize=False)
             for item in input_data
         ]
     else:
-        # Raw text format: [{"question": "..."}]
         prompts = [item[question_key] for item in input_data]
+    print(f"  {len(prompts)} prompts loaded\n")
 
-    # Use provided max_tokens or default to model config max_len
     if max_tokens is None:
         max_tokens = model.config.max_len
 
-    # Generate responses (batch)
-    responses = engine.generate(
-        prompt=prompts,
-        stream=False,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p,
-        top_k=top_k,
-    )
+    chunk_size = max(1, batch_size)
 
-    # Write results
     with open(output_json_file, "w", encoding="utf-8") as f:
-        for prompt, response in zip(prompts, responses):
-            if input_data and "messages" in input_data[0]:
-                output_item = {"response": response}
+        pbar = tqdm(
+            total=len(prompts) * num_samples,
+            unit="gen",
+            desc=f" Generating ({num_samples}x/prompt)",
+        )
+        for chunk_start in range(0, len(prompts), chunk_size):
+            chunk = prompts[chunk_start : chunk_start + chunk_size]
+
+            if num_samples > 1:
+                chunk_expanded = [p for p in chunk for _ in range(num_samples)]
+                resp_chunk = engine.generate(
+                    prompt=chunk_expanded,
+                    stream=False,
+                    max_tokens=max_tokens,
+                    temperature=temperature,
+                    top_p=top_p,
+                    top_k=top_k,
+                    frequency_penalty=frequency_penalty,
+                    rep_window=rep_window,
+                )
+                resp_chunk = [
+                    resp_chunk[i * num_samples : (i + 1) * num_samples]
+                    for i in range(len(chunk))
+                ]
             else:
-                output_item = {question_key: prompt, response_key: response}
-            f.write(json.dumps(output_item, ensure_ascii=False) + "\n")
+                resp_chunk = engine.generate(
+                    prompt=chunk,
+                    stream=False,
+                    max_tokens=max_tokens,
+                    temperature=temperature,
+                    top_p=top_p,
+                    top_k=top_k,
+                    frequency_penalty=frequency_penalty,
+                    rep_window=rep_window,
+                )
+
+            for i, prompt in enumerate(chunk):
+                if input_data and "messages" in input_data[0]:
+                    orig = input_data[chunk_start + i]
+                    output_item = {**orig, response_key: resp_chunk[i]}
+                else:
+                    output_item = {
+                        question_key: prompt,
+                        response_key: resp_chunk[i],
+                    }
+                f.write(json.dumps(output_item, ensure_ascii=False) + "\n")
+
+            pbar.update(len(chunk) * num_samples)
+
+        pbar.close()
+
+    elapsed = time.time() - t0
+    print(
+        f"\nDone! {len(prompts)} prompts x {num_samples} samples -> {output_json_file}"
+    )
+    print(f"Total time: {elapsed:.1f}s ({elapsed / len(prompts):.2f}s/prompt)")
 
-    # Cleanup
     engine.shutdown()
 
 
@@ -126,12 +175,36 @@ if __name__ == "__main__":
         default=1,
         help="Batch size for generating responses (default: 1).",
     )
+    parser.add_argument(
+        "--num_samples",
+        type=int,
+        default=1,
+        help="Number of responses per prompt (expands batch internally, default: 1).",
+    )
     parser.add_argument(
         "--max_tokens",
         type=int,
         default=None,
         help="Maximum tokens to generate (default: model config max_len).",
     )
+    parser.add_argument(
+        "--cache_len",
+        type=int,
+        default=2048,
+        help="KV cache & prompt truncation length (default: 2048, lower = less memory).",
+    )
+    parser.add_argument(
+        "--frequency_penalty",
+        type=float,
+        default=0.0,
+        help="Frequency penalty to reduce repetition (default: 0.0, try 0.5-1.0).",
+    )
+    parser.add_argument(
+        "--rep_window",
+        type=int,
+        default=64,
+        help="Window size for frequency penalty (default: 64).",
+    )
 
     args = parser.parse_args()
 

scripts/tools/train.py

@@ -8,7 +8,7 @@ import torch.optim as optim
 from torch import Tensor, nn
 
 from astrai.config import AutoRegressiveLMConfig, TrainConfig
-from astrai.dataset import DatasetFactory
+from astrai.dataset import DatasetFactory, dpo_collate_fn, grpo_collate_fn
 from astrai.model import AutoRegressiveLM
 from astrai.model.components.decoder_block import DecoderBlock
 from astrai.trainer import SchedulerFactory, Trainer
@@ -148,8 +148,8 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--max_grad_norm",
         type=float,
-        default=1.0,
-        help="Max gradient norm for clipping.",
+        default=None,
+        help="Max gradient norm for clipping. None disables clipping.",
     )
     parser.add_argument(
         "--weight_decay",
@@ -460,6 +460,7 @@ def train(
         load_path=data_root_path,
         window_size=window_size,
         stride=stride,
+        tokenizer_path=param_path,
     )
 
     optimizer_fn = partial(
@@ -504,6 +505,12 @@ def train(
 
     grad_ckpt_modules = [DecoderBlock] if gradient_checkpointing else []
 
+    collate_fn = None
+    if train_type == "dpo":
+        collate_fn = dpo_collate_fn
+    elif train_type == "grpo":
+        collate_fn = grpo_collate_fn
+
     train_config = TrainConfig(
         model_fn=model_fn,
         strategy=train_type,
@@ -536,6 +543,7 @@ def train(
         executor_kwargs=executor_kwargs,
         extra_kwargs=strategy_kwargs,
         neftune_alpha=neftune_alpha,
+        collate_fn=collate_fn,
     )
 
     trainer = Trainer(train_config)

tests/data/test_dataset.py

@@ -1,14 +1,16 @@
 import json
 import os
+import tempfile
 
 import numpy as np
 import pytest
 import torch
 
 from astrai.config.preprocess_config import PipelineConfig
-from astrai.dataset.dataset import DatasetFactory, SEQDataset
+from astrai.dataset.dataset import DatasetFactory, dpo_tokenize
 from astrai.dataset.storage import (
     H5Store,
+    JsonlStore,
     StoreFactory,
     detect_format,
 )
@@ -56,6 +58,13 @@ def _write_jsonl_dataset(test_dir, tokenizer_path, records, config_overrides=Non
     return data_dir
 
 
+def _fake_fetch_record(self, idx, keys):
+    """FakeStore.fetch_record matching real Store semantics."""
+    if isinstance(keys, str):
+        return self._data[keys][idx]
+    return {k: self._data[k][idx] for k in keys}
+
+
 def _make_seq_dataset(
     test_dir, name="data", seq_length=200, train_type="seq", data=None, **load_kwargs
 ):
@@ -109,7 +118,7 @@ def test_dpo_strategy_with_random_data(base_test_env):
     )
 
     assert dpo_dataset is not None
-    assert dpo_dataset.storage is not None
+    assert dpo_dataset.store is not None
     assert len(dpo_dataset) > 0
 
     # Test that we can get DPO items without errors
@@ -138,7 +147,7 @@ def test_sft_dataset_with_random_data(base_test_env):
     )
 
     assert sft_dataset is not None
-    assert sft_dataset.storage is not None
+    assert sft_dataset.store is not None
     assert len(sft_dataset) > 0
 
     # Test that we can get SFT items without errors
@@ -169,39 +178,37 @@ def test_dataset_with_custom_stride(base_test_env):
     assert len(dataset) > len(default_stride_dataset)
 
 
-def test_dataset_count_property(base_test_env):
+def test_dataset_token_count_property(base_test_env):
+    """dataset.token_count exposes the raw stream token length."""
     test_dir = base_test_env["test_dir"]
     dataset = _make_seq_dataset(test_dir, "count_test_data")
-    assert dataset.count == 200
-    assert dataset.count > len(dataset)
+    assert dataset.token_count == 200
+    assert dataset.token_count > len(dataset)
     assert len(dataset) == (200 - 1 - 64) // 64 + 1
 
 
-def test_empty_dataset_count():
-    """Test count returns 0 when no data is loaded"""
-    dataset = SEQDataset(window_size=64, stride=32)
-    assert dataset.count == 0
-    assert dataset.keys == []
-
-
 def test_dataset_too_short_for_window(base_test_env):
     test_dir = base_test_env["test_dir"]
     dataset = _make_seq_dataset(test_dir, "short", seq_length=30)
     assert len(dataset) == 0
-    assert dataset.count == 30
+    assert dataset.token_count == 30
 
 
-def test_unloaded_dataset_getitem_raises():
-    """__getitem__ without load() should fail clearly"""
-    dataset = SEQDataset(window_size=64, stride=32)
-    with pytest.raises(RuntimeError, match="not loaded"):
-        dataset.get_index(0)
+def test_unloaded_sample_window_raises():
+    """Store.sample_window before load raises RuntimeError."""
+    from astrai.dataset.storage import H5Store
+
+    store = H5Store(window_size=64, stride=64)
+    with pytest.raises(IndexError, match="Data too short"):
+        store.sample_window(0)
 
 
 def test_unloaded_dataset_len():
-    """__len__ without load() returns 0"""
-    dataset = SEQDataset(window_size=64, stride=32)
-    assert len(dataset) == 0
+    """__len__ on a store with no data returns 0."""
+    from astrai.dataset.storage import H5Store
+
+    store = H5Store(window_size=64, stride=64)
+    assert len(store) == 0
 
 
 def test_store_unloaded_len():
@@ -214,7 +221,7 @@ def test_store_unloaded_len():
 def test_store_fetch_begin_equals_end(base_test_env):
     test_dir = base_test_env["test_dir"]
     dataset = _make_seq_dataset(test_dir, "empty_fetch", seq_length=100, window_size=32)
-    result = dataset.storage.fetch(10, 10, "sequence")
+    result = dataset.store.fetch(10, 10, "sequence")
     assert result.numel() == 0
 
 
@@ -264,7 +271,7 @@ def test_store_multi_segment_concat(base_test_env):
 
     store = StoreFactory.create("h5")
     store.load(data_dir)
-    assert len(store) == 9
+    assert store.token_count == 9
     result = store.fetch(2, 7, "sequence")
     assert result.tolist() == [3, 4, 5, 6, 7]
 
@@ -293,7 +300,9 @@ def test_mmap_store_load_and_fetch(base_test_env):
 
     store = StoreFactory.create("bin")
     store.load(test_dir)
-    assert len(store) == 200
+    assert store.token_count == 200
+    assert store.num_records == 0
+    assert len(store) == 0  # no window configured, no records → 0 samples
     assert "sequence" in store.keys
 
     result = store.fetch(10, 20, "sequence")
@@ -306,23 +315,26 @@ def test_mmap_dataset_load(base_test_env):
     save_bin(test_dir, data)
     dataset = DatasetFactory.load("seq", test_dir, window_size=64)
     assert len(dataset) > 0
-    assert dataset.count == 200
+    assert dataset.token_count == 200
     assert dataset[0]["input_ids"].shape[0] == 64
 
 
 def test_normalize_empty_key():
-    """_normalize with empty tensor list does not crash"""
+    """_normalize with empty tensor list does not crash."""
     store = H5Store()
     store._normalize({"sequence": []})
     assert len(store) == 0
+    assert store.num_records == 0  # empty key forces num_records=0
     assert store.keys == ["sequence"]
 
 
 def test_normalize_mixed_empty_key():
-    """_normalize with empty + non-empty keys returns min=0"""
+    """_normalize with empty + non-empty keys returns min=0 records."""
     store = H5Store()
     store._normalize({"sequence": [torch.tensor([1, 2, 3])], "loss_mask": []})
     assert len(store) == 0
+    assert store.num_records == 0
+    assert store.token_count == 0  # min() over keys
     assert set(store.keys) == {"sequence", "loss_mask"}
 
 
@@ -330,14 +342,14 @@ def test_grpo_dataset_dtype(base_test_env):
     """GRPO dataset returns correct dtypes for per-record structured data."""
     from astrai.dataset.dataset import GRPODataset
 
-    test_dir = base_test_env["test_dir"]
     G = 4
-    dataset = GRPODataset()
-    dataset.storage = type(
+    store = type(
         "FakeStore",
         (),
         {
             "keys": ["prompts", "responses", "masks", "rewards"],
+            "num_records": 1,
+            "token_count": 0,
             "_data": {
                 "prompts": [torch.randint(0, 100, (10,), dtype=torch.int32)],
                 "responses": [
@@ -346,9 +358,11 @@ def test_grpo_dataset_dtype(base_test_env):
                 "masks": [[torch.ones(5, dtype=torch.int32) for _ in range(G)]],
                 "rewards": [torch.rand(G, dtype=torch.float32)],
             },
+            "fetch_record": _fake_fetch_record,
+            "__len__": lambda self: self.num_records,
         },
     )()
-    dataset._build_records()
+    dataset = GRPODataset(store=store)
     item = dataset[0]
 
     assert item["prompts"].dtype == torch.long
@@ -361,25 +375,27 @@ def test_grpo_dataset_load(base_test_env):
     """GRPO dataset loads record-structured data with per-response boundaries."""
     from astrai.dataset.dataset import GRPODataset
 
-    test_dir = base_test_env["test_dir"]
     G = 3
     prompt_len = 8
     resp_lens = [5, 7, 4]
-    dataset = GRPODataset()
-    dataset.storage = type(
+    store = type(
         "FakeStore",
         (),
         {
             "keys": ["prompts", "responses", "masks", "rewards"],
+            "num_records": 1,
+            "token_count": 0,
             "_data": {
                 "prompts": [torch.randint(0, 100, (prompt_len,))],
                 "responses": [[torch.randint(0, 100, (rl,)) for rl in resp_lens]],
                 "masks": [[torch.ones(rl, dtype=torch.int64) for rl in resp_lens]],
                 "rewards": [torch.tensor([0.9, 0.3, 0.7], dtype=torch.float32)],
             },
+            "fetch_record": _fake_fetch_record,
+            "__len__": lambda self: self.num_records,
         },
     )()
-    dataset._build_records()
+    dataset = GRPODataset(store=store)
 
     assert len(dataset) == 1
     item = dataset[0]
@@ -447,16 +463,17 @@ def test_dataset_load_explicit_storage_type(base_test_env):
     test_dir = base_test_env["test_dir"]
     dataset = _make_seq_dataset(test_dir, "explicit", storage_type="h5")
     assert len(dataset) > 0
-    assert dataset.count == 200
+    assert dataset.token_count == 200
 
 
 def _write_json_dataset(test_dir, tokenizer_path, records, config_overrides=None):
-    """Write JSON (not JSONL) dataset — array of objects."""
+    """Write JSONL dataset — one JSON object per line."""
     data_dir = os.path.join(test_dir, "json_data")
     os.makedirs(data_dir, exist_ok=True)
 
-    with open(os.path.join(data_dir, "data.json"), "w", encoding="utf-8") as f:
-        json.dump(records, f, ensure_ascii=False)
+    with open(os.path.join(data_dir, "data.jsonl"), "w", encoding="utf-8") as f:
+        for rec in records:
+            f.write(json.dumps(rec, ensure_ascii=False) + "\n")
 
     config = {
         "tokenizer_path": tokenizer_path,
@@ -535,7 +552,7 @@ def test_json_store_no_tokenizer_path(base_test_env):
     # Save tokenizer files directly in the dataset directory
     tokenizer.save_pretrained(data_dir)
 
-    # Write .json data
+    # Write .jsonl data
     records = [
         {
             "messages": [
@@ -544,8 +561,9 @@ def test_json_store_no_tokenizer_path(base_test_env):
             ]
         }
     ]
-    with open(os.path.join(data_dir, "data.json"), "w", encoding="utf-8") as f:
-        json.dump(records, f, ensure_ascii=False)
+    with open(os.path.join(data_dir, "data.jsonl"), "w", encoding="utf-8") as f:
+        for rec in records:
+            f.write(json.dumps(rec, ensure_ascii=False) + "\n")
 
     # dataset_config.json WITHOUT tokenizer_path
     config = {
@@ -840,11 +858,11 @@ def test_grpo_collate_variable_lengths():
     assert result["responses"][0, 0, 0] == 4
     assert result["responses"][0, 0, 1] == 5
     assert result["responses"][0, 0, 2] == 0  # padded
-    assert result["masks"][0, 0, 2] == False  # padded
+    assert not result["masks"][0, 0, 2]  # padded
 
     # Check response content: item 0, response 1 is [6,7,8,9] no padding
     assert result["responses"][0, 1, 3] == 9
-    assert result["masks"][0, 1, 3] == True
+    assert result["masks"][0, 1, 3]
 
 
 def test_grpo_multiple_records(base_test_env):
@@ -858,12 +876,13 @@ def test_grpo_multiple_records(base_test_env):
         [torch.randint(0, 100, (np.random.randint(3, 8),)) for _ in range(G)]
         for _ in range(n_records)
     ]
-    dataset = GRPODataset()
-    dataset.storage = type(
+    store = type(
         "FakeStore",
         (),
         {
             "keys": ["prompts", "responses", "masks", "rewards"],
+            "num_records": n_records,
+            "token_count": 0,
             "_data": {
                 "prompts": [torch.randint(0, 100, (10,)) for _ in range(n_records)],
                 "responses": dummy_responses,
@@ -875,9 +894,11 @@ def test_grpo_multiple_records(base_test_env):
                     torch.rand(G, dtype=torch.float32) for _ in range(n_records)
                 ],
             },
+            "fetch_record": _fake_fetch_record,
+            "__len__": lambda self: self.num_records,
         },
     )()
-    dataset._build_records()
+    dataset = GRPODataset(store=store)
 
     assert len(dataset) == n_records
 
@@ -888,3 +909,194 @@ def test_grpo_multiple_records(base_test_env):
         assert item["rewards"].shape == (G,)
         for g in range(G):
             assert item["responses"][g].shape == item["masks"][g].shape
+
+
+def _write_dpo_jsonl(test_dir, records):
+    """Write a raw DPO JSONL file (no dataset_config.json)."""
+    path = os.path.join(test_dir, "dpo.jsonl")
+    with open(path, "w", encoding="utf-8") as f:
+        for rec in records:
+            f.write(json.dumps(rec, ensure_ascii=False) + "\n")
+    return path
+
+
+def test_dpo_tokenize_pure_function():
+    """dpo_tokenize returns flat lists with correct mask alignment."""
+
+    class FakeTokenizer:
+        def apply_chat_template(
+            self, messages, tokenize=True, add_generation_prompt=True
+        ):
+            ids = []
+            for m in messages:
+                ids.append(len(m["content"]))
+                ids.append(-1)
+            if add_generation_prompt:
+                ids.append(99)
+            return ids
+
+    record = {"prompt": "ab", "chosen": "xyz", "rejected": "w"}
+    result = dpo_tokenize(record, FakeTokenizer(), max_len=64)
+
+    assert set(result.keys()) == {"chosen", "rejected", "chosen_mask", "rejected_mask"}
+    assert len(result["chosen"]) == len(result["chosen_mask"])
+    assert len(result["rejected"]) == len(result["rejected_mask"])
+
+    assert result["chosen_mask"][0] == 0
+    assert any(m == 1 for m in result["chosen_mask"])
+    assert result["rejected_mask"][0] == 0
+
+
+def test_dpo_tokenize_malformed_record():
+    """dpo_tokenize returns None for missing fields."""
+
+    class FakeTokenizer:
+        def apply_chat_template(
+            self, messages, tokenize=True, add_generation_prompt=True
+        ):
+            return [1]
+
+    assert dpo_tokenize({}, FakeTokenizer()) is None
+    assert dpo_tokenize({"prompt": "a"}, FakeTokenizer()) is None
+    assert dpo_tokenize({"prompt": "a", "chosen": "b"}, FakeTokenizer()) is None
+
+
+def test_dpo_jsonl_lazy_load(base_test_env):
+    """DPODataset loads raw JSONL with tokenizer_path → lazy processor."""
+    test_dir = base_test_env["test_dir"]
+    tokenizer_path = _save_test_tokenizer(test_dir, base_test_env["tokenizer"])
+
+    records = [
+        {"input": "Hello", "chosen": "world", "rejected": "earth"},
+        {"input": "Foo", "chosen": "bar", "rejected": "baz"},
+    ]
+    path = _write_dpo_jsonl(test_dir, records)
+
+    ds = DatasetFactory.load(
+        train_type="dpo",
+        load_path=path,
+        window_size=0,
+        tokenizer_path=tokenizer_path,
+    )
+
+    assert len(ds) == 2
+    assert ds.store.num_records == 2
+    assert ds.store._processor is not None
+
+    item = ds[0]
+    assert set(item.keys()) == {"chosen", "rejected", "chosen_mask", "rejected_mask"}
+    assert item["chosen"].dtype == torch.long
+    assert item["chosen_mask"].dtype == torch.bool
+    assert item["chosen"].shape == item["chosen_mask"].shape
+    assert item["chosen"].shape == item["rejected"].shape
+
+
+def test_dpo_jsonl_lazy_no_tokenizer():
+    """DPODataset on jsonl without tokenizer_path falls back to eager
+    (which requires dataset_config.json, so it should raise)."""
+    with tempfile.TemporaryDirectory() as d:
+        path = os.path.join(d, "dpo.jsonl")
+        with open(path, "w") as f:
+            f.write(json.dumps({"input": "a", "chosen": "b", "rejected": "c"}) + "\n")
+
+        with pytest.raises(FileNotFoundError, match="dataset_config.json"):
+            DatasetFactory.load(
+                train_type="dpo",
+                load_path=path,
+                window_size=0,
+            )
+
+
+def test_jsonl_store_lazy_len_returns_record_count(base_test_env):
+    """JsonlStore in lazy mode: len() returns record count, not tokens."""
+    test_dir = base_test_env["test_dir"]
+    records = [{"input": str(i), "chosen": "c", "rejected": "r"} for i in range(5)]
+    path = _write_dpo_jsonl(test_dir, records)
+
+    store = JsonlStore()
+    store.load(path, processor=lambda r: {"chosen": torch.tensor([1, 2])})
+
+    assert len(store) == 5
+    assert store.num_records == 5
+
+
+def test_jsonl_store_eager_len_returns_token_count(base_test_env):
+    """JsonlStore in eager mode: num_records reflects per-record count."""
+    test_dir = base_test_env["test_dir"]
+    tokenizer_path = _save_test_tokenizer(test_dir, base_test_env["tokenizer"])
+    data_dir = _write_jsonl_dataset(
+        test_dir,
+        tokenizer_path,
+        [{"text": "hello world"}, {"text": "foo bar"}],
+        config_overrides={
+            "preprocessing": {"max_seq_len": 128, "min_chars": 0},
+            "output": {"position_ids_mode": "none"},
+        },
+    )
+
+    store = JsonlStore()
+    store.load(data_dir)
+
+    assert store.num_records == 2
+    assert len(store.keys) > 0
+
+
+def test_h5_store_dual_mode(base_test_env):
+    """H5Store supports both fetch (stream) and fetch_record (record).
+
+    No window configured → ``len(store)`` reflects the record count
+    (2).  ``token_count`` retains the legacy stream length (128), and
+    token-stream access via :meth:`fetch` is still available for
+    callers that want explicit begin/end control.
+    """
+    test_dir = base_test_env["test_dir"]
+
+    seq_length = 64
+    dummy_data = {
+        "chosen": [_rand_seq(seq_length), _rand_seq(seq_length)],
+        "rejected": [_rand_seq(seq_length), _rand_seq(seq_length)],
+    }
+    save_h5(test_dir, "dpo_data", dummy_data)
+
+    store = H5Store()
+    store.load(test_dir)
+
+    assert store.token_count == seq_length * 2
+    assert store.num_records == 2
+    assert len(store) == 2  # no window configured → record count
+
+    rec0 = store.fetch_record(0, "chosen")
+    assert rec0.shape == (seq_length,)
+
+    stream = store.fetch(0, 10, "chosen")
+    assert stream.shape == (10,)
+
+    # Window-configured view of the same data uses stream sample count:
+    #   token_count=128, window_size=64 → num_samples = (128-1-64)//64 + 1 = 1
+    stream_view = H5Store(window_size=seq_length, stride=seq_length)
+    stream_view.load(test_dir)
+    assert len(stream_view) == 1
+
+
+def test_mmap_store_stream_only_no_offsets(base_test_env):
+    """MmapStore without offsets: num_records == 0, stream works.
+
+    No window configured → ``len(store)`` is 0 (no iterate units).
+    ``token_count`` remains 128 for raw token slicing, and ``fetch``
+    provides direct token-range access.
+    """
+    test_dir = base_test_env["test_dir"]
+
+    seq_length = 128
+    dummy_data = {"sequence": [_rand_seq(seq_length)]}
+    save_bin(test_dir, dummy_data)
+
+    store = StoreFactory.create("bin")
+    store.load(test_dir)
+
+    assert store.token_count == seq_length
+    assert store.num_records == 0
+    assert len(store) == 0
+
+    chunk = store.fetch(0, 32, "sequence")
+    assert chunk.shape == (32,)

tests/data/test_sampler.py

@@ -1,4 +1,4 @@
-from astrai.dataset import ResumableDistributedSampler
+from astrai.dataset import RDSampler
 
 
 def test_random_sampler_consistency(random_dataset):
@@ -6,8 +6,8 @@ def test_random_sampler_consistency(random_dataset):
     dataset = random_dataset
 
     # Create two samplers with same seed
-    sampler1 = ResumableDistributedSampler(dataset, seed=42)
-    sampler2 = ResumableDistributedSampler(dataset, seed=42)
+    sampler1 = RDSampler(dataset, seed=42)
+    sampler2 = RDSampler(dataset, seed=42)
 
     indices1 = list(iter(sampler1))
     indices2 = list(iter(sampler2))
@@ -20,8 +20,8 @@ def test_random_sampler_different_seeds(random_dataset):
     dataset = random_dataset
 
     # Create two samplers with different seeds
-    sampler1 = ResumableDistributedSampler(dataset, seed=42)
-    sampler2 = ResumableDistributedSampler(dataset, seed=123)
+    sampler1 = RDSampler(dataset, seed=42)
+    sampler2 = RDSampler(dataset, seed=123)
 
     indices1 = list(iter(sampler1))
     indices2 = list(iter(sampler2))
@@ -35,7 +35,7 @@ def test_sampler_across_epochs(random_dataset):
     dataset = random_dataset
     n = len(dataset)
 
-    sampler = ResumableDistributedSampler(dataset, seed=42)
+    sampler = RDSampler(dataset, seed=42)
 
     # Get indices for first epoch
     epoch1_indices = list(iter(sampler))