feat(refit): add FSDP trainer publisher for RL reshard

[tanushriya910]

Add an FSDP/DTensor trainer-engine adapter so FSDP trainers can publish weight versions through MX's reshard backend, alongside Megatron.

What's new

• engines/fsdp/publisher.py extracts each rank's local shards from an FSDP state_dict. Unsharded and replicated tensors publish in full from every rank; sharded DTensors publish their per-dim local box via compute_local_shape_and_global_offset. HF-name conversion stays out of this layer, the receiver owns that mapping.
• engines/fsdp/adapter.py wires that extraction into the trainer contract, with two staging modes:
  • COPY_TO_DEVICE (default): allocates one persistent wire-dtype arena per shard, registers it once, and snapshots the live weights into it each step. Safe against a moving/re-materialized source since the registered address never changes.
  • IN_PLACE: registers the DTensor's local storage directly and serves it with no copy. Requires the source to already be contiguous and in the wire dtype, and fails fast toward COPY_TO_DEVICE if the source storage moves between steps.
• train/adapter.py: NixlMetadataProvider gains register_tensors, since NIXL can only transfer memory that's been registered, and an adapter needs to register its own staging arenas or in-place buffers before publishing.
• rendezvous.build_sources: unsharded/replicated tensors get published redundantly by every rank, so the same box can show up more than once. build_sources now dedups to one shard per distinct box, picked at random, since the copies are byte-identical and randomizing spreads reads across the publishing ranks' NICs.

Known gaps (follow-ups, not blocking)

• Version retirement isn't wired up yet, so source_reuse_ready fails rather than claiming the source is safe to mutate (mirrors the Megatron adapter's current state).
• Arena registration is one dmabuf MR per shard, not a single pooled VmmArena.

Testing

• test_refit_fsdp_publisher.py: shard extraction (skips non-float, handles unsharded/replicated/sharded), manifest building, contiguity and endpoint validation.
• test_refit_fsdp_adapter.py: both staging modes, moved-source rejection for IN_PLACE, staging-mode/tensor-set immutability after initialize, unsupported mode/format rejection.
• test_reshard_refit_rendezvous.py: build_sources dedup behavior for redundant boxes vs. genuinely distinct boxes.

Summary by CodeRabbit

• New Features

  • Added support for publishing and staging FSDP tensor shards for weight transfer.
  • Added manifest generation with shard, tensor, device, and memory metadata.
  • Added tensor registration for refreshing transfer metadata.
  • Improved replicated-shard handling by selecting one source for duplicate regions and distributing reads across available sources.

• Bug Fixes

  • Added validation for unsupported formats, invalid layouts, changed shard sets, and unsafe source reuse.

• Tests

  • Added coverage for FSDP publishing, staging, validation, manifests, and replicated-shard selection.

[coderabbitai]

Walkthrough

The PR adds FSDP/DTensor shard capture, NIXL registration, staging, and manifest publication. It also adds validation tests and changes rendezvous source planning to deduplicate replicated shards by selecting one source per identical shard box.

Changes

FSDP refit publication

Layer / File(s)	Summary
Shard capture and manifest construction modelexpress_client/python/modelexpress_rl/train/adapter.py, modelexpress_client/python/modelexpress_rl/train/engines/fsdp/*, modelexpress_client/python/tests/test_refit_fsdp_publisher.py	Adds the register_tensors protocol method, captures floating-point FSDP shards, and builds rendezvous manifests with tensor, device, shape, offset, and address metadata.
FSDP staging lifecycle modelexpress_client/python/modelexpress_rl/train/engines/fsdp/adapter.py, modelexpress_client/python/tests/test_refit_fsdp_adapter.py	Adds FSDPTrainerAdapter with copy and in-place staging, source registration, arena snapshots, layout and storage validation, completion fences, and source reuse rejection.

Rendezvous source deduplication

Layer / File(s)	Summary
Replicated source selection modelexpress_client/python/modelexpress/refit/reshard/rendezvous.py, modelexpress_client/python/tests/test_reshard_refit_rendezvous.py	build_sources groups shards by offset and shape, randomly selects one source for duplicate boxes, and retains distinct boxes separately.

Estimated code review effort: 4 (Complex) | ~60 minutes

Merge Risk: 🟠 High · up to 85ff3

The PR adds FSDP weight publication and changes reshard source selection, but unresolved shard-shape validation, scalar manifest offsets, and duplicate-source handling can cause failed refits, invalid manifests, or inconsistent weights. These concrete correctness and availability risks should be fixed or explicitly accepted before merge.

Poem

I’m a rabbit with tensors in tow,
FSDP shards now publish and flow.
One source per box, selected with care,
Copy or in-place, buffers stay there.
NIXL manifests hop through the air!

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 11.54% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly identifies the main change: adding an FSDP trainer publisher for RL resharding.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

✨ Finishing Touches 💡 1

🛠️ Fix failing CI checks 💡

• Create stacked PR
• Commit on current branch

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Actionable comments posted: 3

🤖 Prompt for all review comments with AI agents

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Inline comments:
In `@modelexpress_client/python/modelexpress_rl/train/engines/fsdp/adapter.py`:
- Around line 107-109: Update the shard registration logic around
_expected_names to also store each shard’s initial geometry keyed by name,
including local_shape and, if required for a fixed layout, global_shape and
shard_offset. Before _snapshot_into_arenas copies a later state dict, validate
that every registered shard’s geometry matches the stored values and reject
changed geometry before copying or publishing the manifest.

In `@modelexpress_client/python/modelexpress_rl/train/engines/fsdp/publisher.py`:
- Line 83: Update the zero_offset construction in the shard publishing logic so
scalar tensors retain the rank-zero offset tuple () instead of defaulting to
(0,), while non-scalar tensors keep one zero offset per full_shape dimension.
Extend test_capture_publishes_full_copies_and_skips_non_float with a
floating-point scalar tensor case that verifies the published manifest preserves
rank-zero shape, full_shape, and offset.

In `@modelexpress_client/python/modelexpress/refit/reshard/rendezvous.py`:
- Around line 181-198: Update build_sources to retain all duplicate shard
candidates per box instead of selecting one with random.choice before
validation. Ensure candidate validation rejects digest mismatches, and preserve
alternates for retry when digest metadata is missing or transport setup fails,
so handshake_endpoints_for_plan can try another session.

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Fix all unresolved CodeRabbit comments on this PR:

• Push a commit to this branch (recommended)
• Create a new PR with the fixes

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ℹ️ Review info

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📥 Commits

Reviewing files that changed from the base of the PR and between 5d2edb4 and 85ff30d.

📒 Files selected for processing (8)

• modelexpress_client/python/modelexpress/refit/reshard/rendezvous.py
• modelexpress_client/python/modelexpress_rl/train/adapter.py
• modelexpress_client/python/modelexpress_rl/train/engines/fsdp/__init__.py
• modelexpress_client/python/modelexpress_rl/train/engines/fsdp/adapter.py
• modelexpress_client/python/modelexpress_rl/train/engines/fsdp/publisher.py
• modelexpress_client/python/tests/test_refit_fsdp_adapter.py
• modelexpress_client/python/tests/test_refit_fsdp_publisher.py
• modelexpress_client/python/tests/test_reshard_refit_rendezvous.py

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[KavinKrishnan]

The FSDP adapter direction looks good. I checked out the branch and all 30 focused tests pass; the scalar path also makes it through the planner correctly. The main thing to sort out is the replica dedup overlap with #635 below. The address check is small but worth fixing here; step stamping and configurable wire dtype can be follow-ups.