Nemo Mbridge Perf Sequence Packing

Sequence Packing Skill

For stable background and recommendation level, see:

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

Enablement

Offline packed SFT for LLM finetuning:

import math
from megatron.bridge.data.datasets.packed_sequence import PackedSequenceSpecs
cfg.train.micro_batch_size = 1cfg.train.global_batch_size = 8cfg.dataset.seq_length = 8192cfg.model.seq_length = 8192cfg.dataset.enable_offline_packing = True
cp_size = cfg.model.context_parallel_sizetp_size = cfg.model.tensor_model_parallel_sizecp_multiple = 2 * cp_size if cp_size > 1 else 1sp_multiple = cp_size * tp_size if cfg.model.sequence_parallel and tp_size > 1 else 1cfg.dataset.offline_packing_specs = PackedSequenceSpecs(    packed_sequence_size=8192,    pad_seq_to_mult=math.lcm(cp_multiple, sp_multiple),)

Choose the offline pack length

For text-only LLM SFT and PEFT verification, start with an 8192-token offline pack when the model context limit, memory, and model-family support allow it. Benchmark pack lengths at equal token slots per optimizer step:

token_slots_per_step = packed_sequence_size * global_batch_size

For example, 2K/GBS32, 4K/GBS16, and 8K/GBS8 each expose 65,536 token slots per step. Longer packs aggregate more source examples into each physical MBS1 row and can reduce gradient accumulation and per-step overhead. They also increase activation memory and may expose kernel-width constraints, so select the largest measured configuration that fits rather than assuming longer is always faster.

Offline packing requires MBS1. Require global_batch_size % data_parallel_size == 0 and global_batch_size >= data_parallel_size; an 8K/GBS8 workload therefore needs DP no larger than 8. Keep model.seq_length, dataset.seq_length, and packed_sequence_size equal, use a fresh packed-data output root after changing any of them, and inspect the resolved post-setup configuration.

Equal token slots do not make different pack lengths numerically identical: the longer target changes truncation and pack membership. Rerun finite-loss, no-skip/NaN, and convergence sentinels before replacing verified evidence.

For finetuning with CP enabled:

cfg.model.context_parallel_size = 2cfg.model.calculate_per_token_loss = Truecfg.ddp.average_in_collective = False

Use the same alignment formula for SFT and PEFT. It produces 1 for TP1/CP1 with SP disabled and 4 for TP4/CP1 with SP enabled. Offline packing does not derive the value automatically, so pin it explicitly and rebuild packed data after a topology change.

If a dispatcher or kernel requires a fixed final token width:

cfg.dataset.dataset_kwargs = {    **(cfg.dataset.dataset_kwargs or {}),    "pad_to_max_length": True,}

Choose packed_sequence_size to satisfy the kernel multiple. For example, HybridEP with a 128-token combine chunk requires a width divisible by 128. This is separate from pad_seq_to_mult, which aligns each constituent sequence for CP/SP.

If CUDA graphs are enabled for this packed path, fixed token width is required and packed metadata must also have a static shape:

cfg.dataset.offline_packing_specs.pad_cu_seqlens = Truecfg.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 GPT SFT and supported VLM finetuning:

cfg.dataset.enable_in_batch_packing = Truecfg.dataset.dataloader_type = "single"cfg.train.micro_batch_size = 4

For local or materialized GPT-SFT JSONL, this keeps the existing mmap-backed dataset and performs tokenization lazily. Both prompt/completion (GPTSFTDataset) and chat (GPTSFTChatDataset) preserve their loss-mask semantics. Use dataloader_type="single" or "cyclic" so every DataLoader yield is one logical microbatch; GPT-SFT in-batch packing does not support the global-batch "batch" dataloader.

Energon online packing for Qwen-VL uses Energon's per-worker candidate buffer instead of limiting selection to one collator micro batch:

cfg.dataset.packing_buffer_size = 16cfg.dataset.micro_batch_size = 1cfg.train.micro_batch_size = 1cfg.model.calculate_per_token_loss = Truecfg.ddp.average_in_collective = False

packing_buffer_size is the sole native-packing selector; leave the legacy collator- and step-owned packing flags at their defaults. Use vlm_step. The buffer size counts prepared candidate samples per worker, not bytes or packed tokens. Since prepared image/video patch tensors remain in host memory until selection, start at 8-16 for high-resolution or video data and measure worker RSS, first-batch latency, and bin fill before increasing it. This path does not write offline packs; the source WebDataset shards remain unchanged. It supports eager Qwen-VL with MBS1 and rejects MTP, CUDA graphs, Qwen3-VL DistTrain, and PP. Requested MoE expert-parallel communication overlap is disabled with a warning. Standard eager alltoall EP has functional coverage for Qwen3.6-35B-A3B at TP1/PP1/EP8 with overlap disabled; this is not performance evidence. Other EP dispatchers are accepted with fixed-width native packs but do not yet have equivalent runtime evidence. The Qwen-VL model derives a MoE padding mask from logical and physical THD boundaries so fixed-width gaps do not enter auxiliary-loss, z-loss, or expert-bias statistics. Current MCore may still dispatch padded positions; expert-capacity/token-dropping configurations lack native-packing runtime coverage.

Long-context baseline:

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

Code Anchors

LLM packed SFT config surface:

dataset_kwargs = {}offline_packing_specs = Noneif enable_offline_packing:    dataset_kwargs["pad_to_max_length"] = True    offline_packing_specs = PackedSequenceSpecs(packed_sequence_size=seq_length, pad_seq_to_mult=pad_seq_to_mult)
return _text_hf_dataset_config(    source=HFDatasetSourceConfig(dataset_name="squad"),    preprocessing=PromptCompletionSFTPreprocessingConfig(separator=" "),    seq_length=seq_length,    enable_offline_packing=enable_offline_packing,    offline_packing_specs=offline_packing_specs,    dataset_kwargs=dataset_kwargs,    val_proportion=0.1,    num_workers=1,)

The shared text-dataset helper currently opts into fixed-width packs. Treat that as a helper default, not a universal offline-packing runtime requirement; preserve it when the selected dispatcher, kernel, or CUDA-graph path requires static width.

Bridge validation:

enable_in_batch_packing = getattr(self.dataset, "enable_in_batch_packing", False)enable_offline_packing = getattr(self.dataset, "enable_offline_packing", False)offline_packing_specs = getattr(self.dataset, "offline_packing_specs", None)
if enable_offline_packing and enable_in_batch_packing:    raise ValueError("enable_offline_packing and enable_in_batch_packing are mutually exclusive.")if enable_offline_packing and offline_packing_specs is None:    raise ValueError("offline_packing_specs must be set when enable_offline_packing=True.")...if enable_in_batch_packing:    ...    cp_multiple = 2 * cp_size if cp_size > 1 else 1    sp_multiple = cp_size * tp_size if has_sp and tp_size > 1 else 1    self.dataset.in_batch_packing_pad_to_multiple_of = math.lcm(cp_multiple, sp_multiple)

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 enable_offline_packing and self.train.micro_batch_size > 1:    raise ValueError(...)...if enable_in_batch_packing and self.train.micro_batch_size == 1:    raise ValueError(...)

Collate-time in-batch runtime used by VLM providers:

def prepare_padded_or_packed_sequence_batch(    batch,    *,    sequence_length,    ...    enable_in_batch_packing=False,    in_batch_packing_pad_to_multiple_of=1,    ...):    ...    if enable_in_batch_packing:        pack_right_padded_sequence_batch_to_mcore_thd(            batch,            sequence_length=sequence_length,            pad_to_multiple_of=in_batch_packing_pad_to_multiple_of,            ...        )        return

GPT-SFT direct-row packing:

def _collate_in_batch(self, batch):    ...    return build_mcore_thd_sequence_batch_from_rows(...)

Packed THD runtime constraint:

if batch.get("cu_seqlens_q") is not None:    cu_seqlens = batch.get("cu_seqlens_q_padded")    if cu_seqlens is None:        cu_seqlens = batch["cu_seqlens_q"]    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)")    return cu_seqlens.squeeze()
cu_seqlens = batch["cu_seqlens"]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

1. Offline packed SFT, runtime in-batch packing, and Energon online packing are different features. Offline and Energon packing use physical MBS1; runtime in-batch packing uses MBS greater than one.
2. GPT-SFT in-batch packing requires dataloader_type="single" or "cyclic"; it does not support "batch".
3. When CP is enabled, packed sequence lengths must respect 2 * context_parallel_size divisibility.
4. For finetuning with CP, calculate_per_token_loss=True and ddp.average_in_collective=False are required.
5. pad_cu_seqlens=True also requires pad_to_max_length=True.
6. Packing support is model-family-specific. Qwen3-Next, GLM-4.5, and Qwen3.5-VL contain explicit opt-outs in different paths.
7. MTP finetuning is documented as incompatible with packed sequences.
8. Synthetic padding rows, including negative indices remapped through samples_mapping, must retain an all-zero loss mask.
9. global_batch_size must be divisible by and no smaller than data parallel size when offline packing uses MBS1.
10. Derive pad_seq_to_mult from CP/TP/SP for both SFT and PEFT; do not hardcode different values by workload type.
11. pad_to_max_length controls final pack width and is conditional on fixed-shape execution requirements.
12. Energon packing_buffer_size is per worker and also affects validation; global/eval batch counts refer to physical packs rather than source conversations.
13. Exact Energon loader resume requires unchanged shards/splits, DP world size, worker counts, shuffle settings/seed, processor, sequence length, topology, and packing-buffer size.

Verification

Use the checked-in unit coverage:

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 enable_in_batch_packing or offline_and_in_batch_packing_are_mutually_exclusive or context_parallel_seq_length_divisibility or context_parallel_finetuning_validations" -v && \uv run python -m pytest tests/unit_tests/data/packing/test_in_batch.py -v && \uv run python -m pytest tests/unit_tests/data/datasets/test_gpt_sft.py -k "in_batch_packing" -v && \uv run python -m pytest tests/unit_tests/data/builders/test_gpt_sft_config.py -v && \uv run python -m pytest tests/unit_tests/training/test_vlm_step.py -k "deferred_in_batch_packing or packed_metadata" -v && \uv run python -m pytest tests/unit_tests/models/qwen_vl/data/test_energon.py tests/unit_tests/data/builders/test_energon_builder.py -v && \uv run python -m pytest tests/unit_tests/tutorials/test_multimodal_data_tutorials.py -k "native_packing_loader" -v && \uv run python -m pytest tests/unit_tests/data/datasets/test_packed_parquet.py -k "negative_index_zeroes_loss_mask" -v && \uv run python -m pytest tests/unit_tests/data/datasets/test_sft.py -k "mapped_padding_rows_do_not_contribute_to_loss" -v

Success criteria:

• all selected tests pass
• offline and in-batch configuration validation remains mutually exclusive
• packed metadata reaches the training step in MCore THD form
• GPT-SFT in-batch packing rejects the global-batch "batch" dataloader
• native Energon packing restores pending groups exactly and flushes finite partial buffers without dropping samples
• mapped padding rows do not contribute to loss