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Ayush Chaurasia
242ade8017
## How packing works
Consider four tokenized documents:
[1]
[2]
[10, 11, 12, 13, 14, 15, 16, 17]
[20]
With:
```
StreamingDataset(
table,
shuffle=False,
columns=["tokens"],
num_splits=2,
pack_sequences=5,
eos_id=9,
pad_id=0,
blocks_per_epoch=6,
)
```
the documents are assigned to two fixed logical splits. Each split
maintains an independent token buffer, appends eos_id after every
document, and emits blocks of five tokens.
Because blocks_per_epoch=6, each split emits exactly three blocks:
Cycl/e 1:
Split 0: [1, 9, 2, 9, 0] # 9 is eos, 0 is padding
Split 1: [10, 11, 12, 13, 14]
Cycle 2:
Split 0: [0, 0, 0, 0, 0]
Split 1: [15, 16, 17, 9, 20]
Cycle 3:
Split 0: [0, 0, 0, 0, 0]
Split 1: [9, 0, 0, 0, 0]
If a split runs out of tokens early, it emits padded blocks through the
fixed budget. This prevents one rank from finishing before another.
Logical splits are independent of rank and worker ownership. A
checkpoint records each split’s consumed-document count, emitted-block
count, remaining tokens, and document boundaries. Merging
those per-split states allows the same packed stream to resume after the
topology changes.
doc_ids identifies document segments, including continuations across
block boundaries. It is not a padding mask: padding retains the
preceding document ID, so callers must mask padding using a
reserved pad_id.
blocks_per_epoch="auto" is also available. It estimates the budget from
a deterministic bounded sample and warns that the result is approximate.
WIP pre-training tests:
```
┌────────────────────────────────────┬────────────────────────┬─────────────────────────────────────┐
│ │ GPT-2 124M │ GPT-2 medium 354M │
├────────────────────────────────────┼────────────────────────┼─────────────────────────────────────┤
│ Corpus │ 2.4M docs / 12GB table │ 9.67M docs / 45GB table │
│ Tokens (Chinchilla) │ 2.43B │ 7.0B │
├────────────────────────────────────┼────────────────────────┼─────────────────────────────────────┤
│ Data prep (ingest→curate→tokenize) │ ~12 min │ ~51 min │
├────────────────────────────────────┼────────────────────────┼─────────────────────────────────────┤
│ Training wall time │ ~50 min │ 3h 06m │
├────────────────────────────────────┼────────────────────────┼─────────────────────────────────────┤
│ Throughput / MFU │ 1.60M tok/s / 35% │ 684k tok/s / 42.0%, │
├────────────────────────────────────┼────────────────────────┼─────────────────────────────────────┤
│ Final val loss │ 3.230 │ 2.840 │
└────────────────────────────────────┴────────────────────────┴─────────────────────────────────────┘
```
---------
Co-authored-by: OpenAI Codex <codex@openai.com>
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