Sequence Packing Skill

For stable background and recommendation level, see:

@docs/training/packed-sequences.md
@skills/perf-sequence-packing/card.yaml

Enablement

Offline packed SFT for LLM finetuning:

python

from megatron.bridge.data.datasets.packed_sequence import PackedSequenceSpecs

cfg.train.micro_batch_size = 1
cfg.dataset.seq_length = 4096
cfg.model.seq_length = 4096
cfg.dataset.dataset_kwargs = {"pad_to_max_length": True}
cfg.dataset.packed_sequence_specs = PackedSequenceSpecs(
    packed_sequence_size=4096,
    pad_seq_to_mult=1,
)

If CP is enabled:

python

cfg.model.context_parallel_size = 2
cfg.model.calculate_per_token_loss = True
cfg.ddp.average_in_collective = False
cfg.dataset.packed_sequence_specs.pad_seq_to_mult = cfg.model.context_parallel_size * 2

# If sequence_parallel is also enabled, use lcm(2*CP, CP*TP):
# import math
# cfg.dataset.packed_sequence_specs.pad_seq_to_mult = math.lcm(2 * CP, CP * TP)
# See src/megatron/bridge/training/vlm_step.py for reference logic.

If CUDA graphs are enabled for this packed path:

python

cfg.dataset.packed_sequence_specs.pad_cu_seqlens = True
cfg.dataset.dataset_kwargs["pad_to_max_length"] = True

Note:

pad_cu_seqlens = True

also requires a metadata JSON file alongside the packed dataset (asserted in

src/megatron/bridge/data/datasets/sft.py

). Custom packed datasets that omit the metadata file will hit an assertion at dataset initialization.

In-batch packing for VLM finetuning:

python

cfg.dataset.pack_sequences_in_batch = True
cfg.train.micro_batch_size = 2

Long-context baseline:

python

cfg.model.seq_length = 16384
cfg.dataset.seq_length = 16384
cfg.model.context_parallel_size = 2

Code Anchors

LLM packed SFT config surface:

if packed_sequence:
    dataset_kwargs = {"pad_to_max_length": True}
    packed_sequence_specs = PackedSequenceSpecs(packed_sequence_size=seq_length, pad_seq_to_mult=pad_seq_to_mult)
else:
    dataset_kwargs = {}
    packed_sequence_specs = None

Bridge validation:

1617

if self.model.context_parallel_size > 1:
    assert self.model.seq_length % (self.model.context_parallel_size * 2) == 0, ...
    if isinstance(self.dataset, FinetuningDatasetConfig):
        assert self.model.calculate_per_token_loss, ...
        assert not self.ddp.average_in_collective, ...
...
if ... packed_sequence_size > 0 and self.train.micro_batch_size > 1:
    raise ValueError(...)
...
if getattr(self.dataset, "pack_sequences_in_batch", False) and self.train.micro_batch_size == 1:
    raise ValueError(...)

VLM in-batch runtime:

308

if enable_packing:
    ...
    ) = pack_batch_sequences(
        ...
        pad_token_id=0,
        pad_to_multiple_of=cp_size * 2 if cp_size > 1 else 1,
    )

Packed THD runtime constraint:

if cu_seqlens.dim() > 1 and cu_seqlens.size(0) != 1:
    raise ValueError("Packed THD batches expect micro-batch size 1 for context-parallel slicing (THD layout)")

Pitfalls

Offline packed SFT and VLM in-batch packing are different features with opposite micro-batch rules.
When CP is enabled, packed sequence lengths must respect
```
2 * context_parallel_size
```
divisibility.

For finetuning with CP,

calculate_per_token_loss=True

and

ddp.average_in_collective=False

are required.

pad_cu_seqlens=True

also requires

pad_to_max_length=True

Packing support is model-family-specific.
```
Qwen3-Next
```
,
```
GLM-4.5
```
, and
```
Qwen3.5-VL
```
contain explicit opt-outs in different paths.
MTP finetuning is documented as incompatible with packed sequences.

Verification

Use the checked-in unit coverage:

bash

uv run python -m pytest tests/unit_tests/training/utils/test_packed_seq_utils.py -v && \
uv run python -m pytest tests/unit_tests/training/test_config.py -k "packed_sequence or pack_sequences_in_batch or context_parallel_seq_length_divisibility or context_parallel_finetuning_validations" -v && \
uv run python -m pytest tests/unit_tests/training/test_vlm_step.py -k "enable_packing" -v

Success criteria:

first command reports
```
8 passed
```
second command reports
```
14 passed
```
third command reports
```
2 passed
```

perf-sequence-packing

NPX Install

Tags

SKILL.md Content

Sequence Packing Skill

Enablement

Code Anchors

Pitfalls

Verification