Nemo Mbridge Perf Hierarchical Context Parallel
NVIDIAOfficialPublished by NVIDIA
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides the necessary configuration steps, code references, and verification methods for enabling hierarchical context parallelism (HCP) within the Megatron-Bridge framework. It focuses on the a2a+p2p communication type, detailing the requirements for hierarchical group sizing, dependency versions, and integration with Transformer Engine. The guide also highlights common pitfalls, such as compatibility issues with decentralized process groups and the importance of matching product sizes to total context parallel size.
What it does
- Defines configuration knobs for hierarchical context parallelism.
- Maps code anchors for MCore and Bridge initialization paths.
- Provides validation logic for CP communication types.
- Outlines requirements for Transformer Engine integration.
- Lists common pitfalls and troubleshooting steps for HCP deployment.
- Offers verification procedures via log inspection and manual smoke tests.
When to use it
- When scaling context parallelism beyond standard KV head limits.
- When investigating OOM errors or performance regressions related to CP configuration changes.
- When implementing a2a+p2p communication strategies in Megatron-Bridge.
- When debugging silent failures or broken gradients in older training stacks.
Advantages
- Enables advanced scaling of sequence dimensions across GPU ranks.
- Provides clear constraints to prevent configuration mismatches.
- Includes specific log verification steps to confirm group initialization.
- Offers a structured approach to testing hierarchical configurations.
Usage examples
- “How do I enable hierarchical context parallelism with a size of 4 using a2a+p2p?”
- The user receives the specific configuration overrides for model.context_parallel_size, model.cp_comm_type, and model.hierarchical_context_parallel_sizes.
- “How can I verify that hierarchical context parallelism is active in my training logs?”
- The user is instructed to check logs for the creation of HIERARCHICAL_CONTEXT_PARALLEL_GROUPS rather than just the standard CONTEXT_PARALLEL_GROUP.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nemo-mbridge-perf-hierarchical-context-parallel/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nemo-mbridge-perf-hierarchical-context-parallel.zip -d .agents/skills
Documentation by NVIDIA
Hierarchical Context Parallel Skill
This skill covers hierarchical context parallelism: nested context-parallel process
groups used by cp_comm_type="a2a+p2p" and configured with
hierarchical_context_parallel_sizes.
For what hierarchical CP is, when to use it, and the decision tree
(a2a+p2p vs pure a2a vs p2p), see:
@docs/training/hierarchical-context-parallel.md
@skills/nemo-mbridge-perf-hierarchical-context-parallel/card.yaml
Enablement
Minimal Bridge override:
cfg.model.context_parallel_size = 4
cfg.model.cp_comm_type = "a2a+p2p"
cfg.model.hierarchical_context_parallel_sizes = [2, 2]
cfg.dist.use_decentralized_pg = FalseRequired constraints:
prod(hierarchical_context_parallel_sizes) == context_parallel_sizeseq_length % (2 * context_parallel_size) == 0Transformer Engine
>= 1.12.0
Code Anchors
Upstream config and validation:
context_parallel_size: int = 1
"""Splits network input along sequence dimension across GPU ranks."""
hierarchical_context_parallel_sizes: Optional[list[int]] = None
"""Degrees of the hierarchical context parallelism. Users should provide a list to specify
the sizes for different levels. Taking the a2a+p2p cp comm type as example, it contains
groups of two levels, so the first value of the list indicates the group size of the a2a
communication type, and the second value indicates the group size of the p2p communication
type.
"""if args.hierarchical_context_parallel_sizes:
from numpy import prod
assert args.context_parallel_size == prod(args.hierarchical_context_parallel_sizes)
if "a2a+p2p" in args.cp_comm_type:
assert args.hierarchical_context_parallel_sizes is not None, \
"--hierarchical-context-parallel-sizes must be set when a2a+p2p is used in cp comm"Bridge MPU path:
parallel_state.initialize_model_parallel(
...
context_parallel_size=model_config.context_parallel_size,
hierarchical_context_parallel_sizes=model_config.hierarchical_context_parallel_sizes,
...
)
...
return ProcessGroupCollection.use_mpu_process_groups()Bridge decentralized-PG path:
pg_collection = ProcessGroupCollection(
...
cp=cp_pg,
tp_cp=tp_cp_pg,
hcp=None,
ep=ep_pg,
...
)Implementation Map
The code anchors above show the config declarations and argument validation.
Validation (MCore)
TransformerConfig.__post_init__ enforces that a2a+p2p requires HCP sizes and the product matches CP.
Process group creation
parallel_state.initialize_model_parallel creates hierarchical CP sub-groups
when HCP sizes are provided via create_hierarchical_groups. Bridge currently
gets those groups through the MPU-backed ProcessGroupCollection.
TE integration
TEDotProductAttention passes the hierarchical groups to Transformer Engine
when a2a+p2p is used. Requires Transformer Engine >= 1.12.0.
Pitfalls
Bridge HCP is MPU-only today: If
use_decentralized_pg=True, Bridge initializes flat CP groups and leaves HCP unset.No checked-in Bridge recipe currently exercises HCP directly.
Single-GPU load helpers clear
hierarchical_context_parallel_sizes.Silent broken training on old stacks: If you use
a2a+p2pwithout settinghierarchical_context_parallel_sizes, MCore now asserts. Older versions would silently disable CP communication, so each rank attended only to its local chunk and produced artificially high throughput with broken gradients.Product must match:
prod(hierarchical_context_parallel_sizes)must exactly equalcontext_parallel_size. A mismatch triggers an assertion.Verify in logs: Look for the process group initialization output. You should see
HIERARCHICAL_CONTEXT_PARALLEL_GROUPSbeing created. If you only seeCONTEXT_PARALLEL_GROUP, HCP is not active.
Verification
No dedicated Bridge end-to-end test exists yet for HCP (see @skills/nemo-mbridge-perf-hierarchical-context-parallel/card.yaml
follow_up_validation). Use the existing unit tests and log inspection instead.
Run the decentralized-PG unit test to confirm the flat-CP behavior is preserved:
uv run python -m pytest tests/unit_tests/training/test_decentralized_pg.py -qFor a manual smoke check, launch a 4-GPU run with a small recipe and
cp_comm_type=a2a+p2p plus hierarchical_context_parallel_sizes=[2,2]:
CUDA_VISIBLE_DEVICES=0,1,2,3 uv run python -m torch.distributed.run --nproc_per_node=4 \
scripts/training/run_recipe.py \
--recipe llama32_1b_pretrain_config \
model.context_parallel_size=4 \
model.cp_comm_type=a2a+p2p \
"model.hierarchical_context_parallel_sizes=[2,2]" \
train.train_iters=2Success criteria:
Logs show
HIERARCHICAL_CONTEXT_PARALLEL_GROUPSbeing createdTraining completes at least one step without error
If you only see
CONTEXT_PARALLEL_GROUP, HCP is not active
Files
- ATTRIBUTION.mdAdded by AutoGineer893 B
- BENCHMARK.md4.4 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.5 KiB
- card.yaml2.7 KiB
- evals/evals.json1.5 KiB
- skill-card.md3.9 KiB
- skill.oms.sig4.7 KiB
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- NVIDIA/skills · skills/nemo-mbridge-perf-hierarchical-context-parallel · commit fce3cca
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- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
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