ViperEkura
c8b1e40f71
- Rename assets/ to docs/, split into guides/ and developer/ - Add get-started.md: installation + 5-step quickstart - Add guides/evaluation.md: 7 eval scripts with CLI args - Add guides/distributed.md: DDP/FSDP, gradient accumulation, NCCL - Add developer/internals.md: loss formulas, RoPE, KV cache math - Add developer/cuda_kernels.md: build system, benchmarks, file layout - Fix storage_format doc in preprocessing.md - Update cross-references in README.md, README-zh-CN.md, Dockerfile
133 lines
6.9 KiB
Markdown
133 lines
6.9 KiB
Markdown
# Data Flow
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This document describes the data pipeline: from raw text to model input tensors. For creating preprocessing configs, see [Preprocessing Guide](../guides/preprocessing.md).
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## Contents
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- [Overview](#overview)
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- [Data Preparation](#data-preparation) — tokenization, format detection, backends
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- [Data Keys by Training Type](#data-keys-by-training-type)
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- [Dataset Architecture](#dataset-architecture)
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- [Sampler](#sampler)
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- [DataLoader](#dataloader)
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## Overview
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```
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JSONL Lines → Pipeline (mask builder) → Tokenized Tensors
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↓
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.h5 or .bin storage
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↓
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Store.load()
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↓
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Store.fetch(begin, end, keys)
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↓
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BaseDataset.__getitem__(idx)
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↓
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Sampler → DataLoader → Training / Inference
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```
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## Data Preparation
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Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`) or binary (`.bin` + `meta.json`) files with keyed tensor groups.
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### Tokenization
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The `Pipeline` reads JSONL lines, applies the mask builder (see [Preprocessing](../guides/preprocessing.md)), and produces flat token sequences:
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```python
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# Per JSONL line: messages → chat template → token IDs + loss mask
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tokens = tokenizer.encode(rendered_text) # List[int]
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loss_mask = [0, 0, 0, 1, 1, 1, 1, 1, 1] # 0=masked, 1=train
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# Stored as flat tensors, packed with other lines by packing strategy
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```
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The output `meta.json` records the storage format, key names, dtype, total token count, and tensor shapes for each shard.
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### Format Detection
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`detect_format(load_path)` inspects the path:
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- If `load_path` is a file: checks suffix — `.h5`/`.hdf5` → `"h5"`, `.jsonl` → `"jsonl"`, unknown suffix raises `ValueError`
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- If `load_path` is a directory: recursively globs for `*.h5`/`*.hdf5` files → `"h5"`, `*.bin` + `**/meta.json` → `"bin"`, or `*.jsonl` + `dataset_config.json` → `"jsonl"`
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### Store Backends
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Storage format is auto-detected by `detect_format()`; backends are dispatched via registry:
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```
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StoreFactory.create("h5") → H5Store
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StoreFactory.create("bin") → MmapStore
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StoreFactory.create("jsonl") → JsonlStore
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```
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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.
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**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.
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**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).
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**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).
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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.
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## Data Keys by Training Type
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| Type | Storage Keys | Access Mode |
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|------|-------------|-------------|
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| `seq` | `sequence` (→ input_ids, target_ids via offset-by-1) | stream (`fetch`) |
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| `sft` | `sequence`, `loss_mask`, `position_ids` | stream (`fetch`) |
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| `dpo` | `chosen`, `rejected`, `chosen_mask`, `rejected_mask` | record (`fetch_record`) |
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| `grpo` | `prompts`, `responses`, `masks`, `rewards` | record (`fetch_record`) |
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## Dataset Architecture
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```
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DatasetFactory.load(
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train_type, load_path=None, window_size=0, stride=None,
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storage_type=None, tokenizer_path=None,
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max_len=2048, store=None
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)
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→ BaseDataset.load(load_path, storage_type=None)
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→ detect_format(load_path)
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→ StoreFactory.create(storage_type)
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→ Store.load(load_path)
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→ _normalize(raw) # base Store, shared by both backends
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→ Store._data[Dict[str, List[Tensor]]]
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+ _cum[Dict[str, List[int]]] (stream mode)
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+ _offsets[Dict[str, List[int]]] (record mode)
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Stream datasets (SEQ/SFT):
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BaseDataset.__getitem__(idx)
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→ get_index(idx) → [begin, end)
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→ Store.fetch(begin, end, keys) → Tensor / Dict[str, Tensor]
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Record datasets (DPO/GRPO via RecordDataset):
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RecordDataset.__getitem__(idx)
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→ Store.fetch_record(idx, keys) → Tensor / Dict[str, Tensor]
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```
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Class hierarchy: `BaseDataset` ← `SEQDataset` / `SFTDataset` (stream); `BaseDataset` ← `RecordDataset` ← `DPODataset` / `GRPODataset` (record).
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`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`).
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`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.
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`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()`.
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`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).
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## Sampler
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`ResumableDistributedSampler` supports checkpoint-aware distributed sampling:
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- Tracks `start_epoch` / `start_iter` for resume
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- Shuffle via `torch.Generator(seed + epoch)`
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- Per-replica index slicing for DDP
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## DataLoader
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Standard PyTorch `DataLoader` with configurable `batch_size`, `num_workers`, `pin_memory`, `prefetch_factor`. Sampler produces indices; dataloader fetches tensor batches via `__getitem__`.
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> Document Update Time: 2026-07-19
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