[QUESTION]Splitting large document and bucketing

[shafiqabedin]

I asked this question in the discussion section but did not receive any response so asking here with a little bit more details. I am trying to figure out if bucketization is done (or can be done) for model training. By "bucketization" I am referring to batching based on similar sequence length (https://torchtext.readthedocs.io/en/latest/data.html#bucketiterator).. The motivation is that I have documents with very large text and I want pick a splitting schema (which may create a lot of samples with small number of tokens and bucketize them). That brings me to the second question which is - is splitting supported in Megatron?

Any answer would be much appreciated - Thanks.

[Advait251206]

Yes — this is something Megatron-LM can handle, but not in the same “out-of-the-box BucketIterator + automatic splitting” way that torchtext does. You need to understand how Megatron’s data pipeline is designed.

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1. Does Megatron-LM support bucketization?

✔ Yes — but indirectly

Megatron-LM achieves “bucket-like behavior” through:

• dynamic sequence packing
• length-based batching (optional depending on dataset builder)
• token stream construction instead of fixed samples

However:

It does NOT use a PyTorch-style BucketIterator abstraction.

Instead, it builds a contiguous token dataset and then chunks it.

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2. How Megatron actually batches data

Megatron training data pipeline is:

Documents → Tokenization → Concatenation → Fixed-length chunking → Batches

So batching is based on:

• fixed seq_length
• continuous token stream slicing

NOT:

• grouping similar-length documents at runtime

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3. Is explicit bucketization supported?

❌ Not natively like torchtext

Megatron does not:

• group samples by length dynamically
• maintain multiple buckets per batch iterator
• reorder dataset online based on sequence length

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4. What Megatron uses instead

✔ Token packing (closest equivalent)

If enabled in dataset preprocessing:

• short documents are concatenated
• long documents are split
• sequences are filled up to seq_length

This achieves:

high GPU utilization without explicit bucketing

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5. Your use case: large documents + splitting

You said:

“split large documents into many small samples”

This is already how Megatron expects data to be structured.

✔ You should:

Preprocess offline:

• split long docs into chunks (e.g., 1K–4K tokens)
• optionally overlap chunks (sliding window)
• store as flat token stream or document list

Then Megatron will:

• pack them into fixed-length sequences automatically

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6. Can Megatron do splitting automatically?

❌ Not intelligently

It does NOT:

• detect semantic boundaries
• split documents by meaning
• optimize chunking per-document

You must do this before training.

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7. Why true bucketization is not used

Bucketization (like torchtext) improves:

• padding efficiency
• per-batch uniformity

But Megatron prefers:

✔ global token streaming

Because it enables:

• deterministic training
• efficient tensor parallel execution
• simple distributed sharding
• stable memory layout

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8. What about “length-aware batching”?

Some Megatron variants can:

• reduce padding waste via packing
• optionally sort by length (offline preprocessing step)

But this is:

preprocessing-time sorting, not runtime bucketing

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9. Recommended approach (best practice)

For your case:

✔ Step 1: split documents offline

Example:

• max chunk size = 2048 or 4096 tokens
• overlap = 128–256 tokens (optional)

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✔ Step 2: shuffle chunks globally

• avoid ordering bias
• ensures randomness

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✔ Step 3: feed into Megatron as token stream

Let Megatron handle:

• batching
• packing
• distributed partitioning

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10. Key distinction

Feature	torchtext BucketIterator	Megatron-LM
Dynamic length batching	✔	❌
Runtime bucketing	✔	❌
Offline packing	optional	✔ core design
Token stream training	❌	✔ primary model

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11. Bottom line

• ❌ Megatron does not support runtime bucketization like torchtext
• ✔ It uses token-stream packing instead
• ✔ Splitting large documents must be done in preprocessing
• ✔ Efficiency is achieved via fixed-length chunking, not dynamic batching

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GitHub: https://github.com/Advait251206