[Frontend][Core] Add sparse NCCL weight transfer support for in-place updates

[bedeks]

Purpose

Implements an MVP sparse NCCL weight transfer path for online RL weight sync. Instead of resending full dense tensors, the trainer can send (indices, values) patches that are applied in-place to existing runtime GPU parameters.

This addresses #39451.

Current scope:

• NCCL backend only
• sparse updates use kernel-format/runtime parameter names
• not composable with packed=True
• not composable with is_checkpoint_format=True
• restricted to TP=1, PP=1

Why this is not duplicating an existing PR:

• I checked issue #39451 and open PRs for the same area; no open PR was already implementing this sparse NCCL MVP.

Test Plan

.venv/bin/python -m pytest tests/distributed/test_weight_transfer.py -v -k 'valid_sparse_update_info or sparse_update_requires_nnz_list or sparse_update_rejects_checkpoint_format or sparse_update_rejects_packed or sparse_update_rejects_non_int32_indices or dense_update_rejects_sparse_metadata or nccl_receive_sparse_weights_without_init_raises or nccl_sparse_weight_transfer_between_processes or sparse_update_kind_rejected'

.venv/bin/python -m pytest tests/entrypoints/weight_transfer/test_weight_transfer_llm.py -v -k 'test_update_weights_passes_sparse_metadata'

.venv/bin/python -m pytest tests/v1/worker/test_gpu_model_runner.py -v -k 'apply_sparse_weight_patches'

.venv/bin/python -m pytest tests/v1/worker/test_gpu_worker_weight_transfer.py -v -k 'sparse_dispatches or sparse_rejects_tp_or_pp'

Test Result

• tests/distributed/test_weight_transfer.py

  • 9 passed, 27 deselected in 13.59s
  • includes test_nccl_sparse_weight_transfer_between_processes on a 2-GPU pod

• tests/entrypoints/weight_transfer/test_weight_transfer_llm.py

  • 1 passed, 5 deselected in 28.24s

• tests/v1/worker/test_gpu_model_runner.py

  • 3 passed, 25 deselected in 2.40s

• tests/v1/worker/test_gpu_worker_weight_transfer.py

  • 2 passed in 2.17s

• examples/rl/rlhf_sparse_nccl.py on a 2-GPU pod:

  • baseline_equal = True
  • patch_digest_equal = True
  • after_equal = True
  • any_output_changed = True
  • dense payload 942.29 MB vs sparse payload 0.16 MB; dense send 192.02 ms vs sparse send 0.40 ms

Additional Validation

Outside this PR branch, I also ran temporary repro/debug harnesses on a 2-GPU
pod to validate dense-vs-sparse equivalence for the same deterministic patch on
Qwen/Qwen3-1.7B.

Observed results:

• trainer patch digests matched
• full server-side parameter digest maps matched
• controlled max_tokens=1, greedy outputs matched between dense and sparse updates

Performance validation:

• for a patch affecting ~0.3% of model elements on Qwen/Qwen3-1.7B, the sparse payload was ~30.97 MB versus ~3.44 GB for the dense full-model resend path
• in one pod validation run, trainer-side send time decreased from ~175 ms for dense resend to ~4 ms for sparse patch transfer

This validation is supplementary and is not part of the submitted branch.

AI assistance was used to help develop and validate this change.

---

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• The test plan, such as providing test command.
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[gemini-code-assist]

Code Review

This pull request introduces support for sparse weight updates via the NCCL weight transfer backend. It adds a new sparse_flat update kind, allowing 1D flattened patches to be applied directly to model parameters using index_copy_. The implementation includes updates to the NCCL engine, GPU worker, and model runner, along with comprehensive unit and integration tests. Feedback highlights a performance concern in apply_sparse_weight_patches where GPU-CPU synchronization occurs during index validation, which could degrade performance in high-frequency update scenarios.

[gemini-code-assist]

The calls to .item() on patch.indices.max() and patch.indices.min() cause a GPU-CPU synchronization for every parameter being updated. In an online RL setting where many parameters might be updated frequently, this will significantly degrade performance by introducing hundreds or thousands of sync points per weight update step. Since this is an internal API for weight transfer, it is better to trust the trainer's correctness or use a single non-blocking kernel check if validation is strictly required.

[bedeks]

This was defensive validation added in the initial implementation. I have removed it and will rely on the trainer/internal API contract

[hao-aaron]

great work, thanks for this major contribution! Left some organizational comments, and it could be nice to have an example in examples/rl with a real model, to illustrate the stuff mentioned above in the "Additional Validation" section?

[hao-aaron]

could be ignorance on my part, but its not immediately obvious what nnz means?

[bedeks]

I'll change it to num_updates_list

[hao-aaron]

is there a reason we want the user to be able to specify a dtype for their indices? Might be easier just to force it to be a fixed dtype.

[bedeks]

I added indices_dtype_name initially to keep the sparse wire format as self describing and easier to extend in the future. For this MVP, though, sparse indices are always int32, so I agree the field is unnecessary and we should hardcode int32 for now.

[hao-aaron]

I think what we can do is move all of this new sparse related fields and field validation into the base class. I can see future engines making use of sparse updates, not just nccl. We can add all the new functions like receive_sparse_weights and trainer_send_sparse_weights to base as well, and have ipc override them with not implemented errors

[hao-aaron]

if we move all the sparse function to the base class and implement in ipc as described above we can simplify this. I also think what we can do the if statements a bit more concise like:

if checkpoint format:
    # checkpoint format stuff
else:
    if sparse:
        #sparse stuff
    else:
        #normal kernel

The more checks we can put on the transfer engine side instead of in gpu_worker.py the better IMO?

[mergify]

Documentation preview: https://vllm--40096.org.readthedocs.build/en/40096/

[aoshen02]

cc @qgallouedec

[qgallouedec]

@AmineDiro

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @bedeks.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[bedeks]

Failing test is not related to the code changes.