Nemo Mbridge Perf Sequence Packing

NVIDIAOfficialPublished by NVIDIA

Skill
Tool integration
Software development
Apache-2.0
Medium risk

From the author

Validate and use packed sequences and long-context training in Megatron-Bridge, including offline LLM packing, collate-time VLM packing, Energon online packing, and CP constraints.

— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides the configuration patterns and validation requirements for implementing sequence packing in Megatron-Bridge. It covers three distinct packing methodologies: offline LLM packing, collate-time in-batch packing, and Energon online packing for VLMs. The guide details how to align packing specifications with context-parallel (CP), tensor-parallel (TP), and sequence-parallel (SP) topologies, ensuring that memory usage and kernel constraints are managed effectively during long-context training.

What it does

Configures offline packed SFT for LLM finetuning with specific sequence length and padding requirements.
Enables in-batch packing for GPT-SFT and VLM workloads to optimize data throughput.
Implements Energon online packing for Qwen-VL models using per-worker candidate buffers.
Provides alignment formulas for CP/TP/SP topologies to ensure valid sequence lengths.
Enforces configuration constraints to prevent invalid combinations of packing methods and dataloaders.
Includes a comprehensive suite of unit tests to verify packing metadata and loss mask integrity.

When to use it

When optimizing training throughput for long-context LLM or VLM finetuning.
When memory constraints require efficient packing of multiple sequences into single micro-batches.
When deploying models with context-parallelism that require specific sequence length divisibility.
When using Energon-based data loading for high-resolution multimodal models like Qwen-VL.

Advantages

Reduces gradient accumulation overhead by aggregating source examples into physical micro-batches.
Optimizes memory utilization for long-context training through structured sequence packing.
Provides clear validation logic to prevent common configuration errors in complex parallel topologies.
Supports diverse packing strategies tailored to specific model families and data loading requirements.

Usage examples

“How do I configure offline packing for an 8192-token LLM finetuning job?”: The agent provides a Python configuration snippet setting enable_offline_packing to True, defining PackedSequenceSpecs with appropriate padding multiples, and ensuring micro-batch size is set to 1.
“What are the requirements for enabling in-batch packing in GPT-SFT?”: The agent explains that enable_in_batch_packing must be True, dataloader_type must be set to single or cyclic, and micro-batch size must be greater than 1.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/nemo-mbridge-perf-sequence-packing/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip nemo-mbridge-perf-sequence-packing.zip -d .agents/skills

Documentation by NVIDIA

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 = 1
cfg.train.global_batch_size = 8
cfg.dataset.seq_length = 8192
cfg.model.seq_length = 8192
cfg.dataset.enable_offline_packing = True

cp_size = cfg.model.context_parallel_size
tp_size = cfg.model.tensor_model_parallel_size
cp_multiple = 2 * cp_size if cp_size > 1 else 1
sp_multiple = cp_size * tp_size if cfg.model.sequence_parallel and tp_size > 1 else 1
cfg.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 = 2
cfg.model.calculate_per_token_loss = True
cfg.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 = 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 GPT SFT and supported VLM finetuning:

cfg.dataset.enable_in_batch_packing = True
cfg.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 = 16
cfg.dataset.micro_batch_size = 1
cfg.train.micro_batch_size = 1
cfg.model.calculate_per_token_loss = True
cfg.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 = 16384
cfg.dataset.seq_length = 16384
cfg.model.context_parallel_size = 2

Code Anchors

LLM packed SFT config surface:

dataset_kwargs = {}
offline_packing_specs = None
if 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

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.
GPT-SFT in-batch packing requires dataloader_type="single" or "cyclic"; it does not support "batch".
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.
Synthetic padding rows, including negative indices remapped through samples_mapping, must retain an all-zero loss mask.
global_batch_size must be divisible by and no smaller than data parallel size when offline packing uses MBS1.
Derive pad_seq_to_mult from CP/TP/SP for both SFT and PEFT; do not hardcode different values by workload type.
pad_to_max_length controls final pack width and is conditional on fixed-shape execution requirements.
Energon packing_buffer_size is per worker and also affects validation; global/eval batch counts refer to physical packs rather than source conversations.
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

Files

ATTRIBUTION.mdAdded by AutoGineer867 B
BENCHMARK.md4.8 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md13.0 KiB
card.yaml7.2 KiB
evals/evals.json2.5 KiB
skill-card.md4.1 KiB
skill.oms.sig4.7 KiB

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