nemo-mbridge-perf-hierarchical-context-parallel

What this skill does

# 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:

```python
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 = False
```

Required constraints:

- `prod(hierarchical_context_parallel_sizes) == context_parallel_size`
- `seq_length % (2 * context_parallel_size) == 0`
- Transformer Engine `>= 1.12.0`

## Code Anchors

Upstream config and validation:

```45:54:3rdparty/Megatron-LM/megatron/core/model_parallel_config.py
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.
"""
```

```428:433:3rdparty/Megatron-LM/megatron/training/arguments.py
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:

```613:648:src/megatron/bridge/training/initialize.py
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:

```503:524:src/megatron/bridge/training/initialize.py
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

1. **Bridge HCP is MPU-only today**: If `use_decentralized_pg=True`, Bridge initializes flat CP groups and leaves HCP unset.
2. **No checked-in Bridge recipe** currently exercises HCP directly.
3. **Single-GPU load helpers** clear `hierarchical_context_parallel_sizes`.
4. **Silent broken training on old stacks**: If you use `a2a+p2p` without setting `hierarchical_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.
5. **Product must match**: `prod(hierarchical_context_parallel_sizes)` must exactly equal `context_parallel_size`. A mismatch triggers an assertion.
6. **Verify in logs**: Look for the process group initialization output. You should see `HIERARCHICAL_CONTEXT_PARALLEL_GROUPS` being created. If you only see `CONTEXT_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:

```bash
uv run python -m pytest tests/unit_tests/training/test_decentralized_pg.py -q
```

For 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]`:

```bash
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=2
```

Success criteria:

- Logs show `HIERARCHICAL_CONTEXT_PARALLEL_GROUPS` being created
- Training completes at least one step without error
- If you only see `CONTEXT_PARALLEL_GROUP`, HCP is not active