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[OMNIML-6082] [1/3] Let a reverse conversion record a merge rule no key matched - #3100
[1/3] of #2751's split (2026-10-29), at its review's request: [1/3] this PR (the reverse conversion's hook), [2/3] #3101, the ownership and transport of the distributed checkpoint writer (stacked on this PR), and [3/3] #2751, the reverse conversion and shard writing (stacked on [2/3]). Merge in that order.
Base:main.
What does this PR do?
Type of change: New feature (part of the no-gather distributed export)
revert_weight_conversion_quant_aware refuses a merge rule that matches no key of the state dict, so that a later rename cannot leave a checkpoint that mixes the two namings.
The distributed checkpoint writer reverts each rank's own share of the model. A rank can hold none of a merge rule's keys while another rank holds them all, so the conversion now takes an unmatched_merges list:
given one, a rule's refusal is recorded there instead of raised, and the rest of the conversion still applies;
the caller ([3/3]) refuses a rule that no rank matched;
without it, nothing changes.
Usage
Internal: revert_weight_conversion_quant_aware(model, state_dict, unmatched_merges=[]), called by the distributed writer ([3/3]).
Testing
test_quant_aware_conversion.py gains test_radio_merge_that_matches_no_keys_is_recorded_for_one_ranks_share next to the existing raise case: the refusal is recorded instead of raised, and a matching merge rule after it still applies, its fused output checked, with the unrelated key kept.
The conversion tests pass (79), and pre-commit passes against main.
Review round, 2026-10-29 (b9fbb242300): apply_reverse_rules's docstring says what unmatched_merges does. The recorded-refusal test now has a matching merge rule after the unmatched one and checks its fused output; it fails if the reverse stops after recording the refusal. A merge group split across ranks cannot reach this hook: [2/3] keeps each group on one owner, and [3/3] runs the reverse inside a collective that carries any rank's refusal to every rank. Pre-commit passes against main; the conversion tests pass (79 on transformers 5.18; on 5.5.4 the scoped-merge cases skip, as before).
Before your PR is "Ready for review"
Is this change backward compatible?: ✅ The new argument is keyword-only and defaults to the old behaviour.
If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
revert_weight_conversion_quant_aware refuses a merge rule that matches
no key of the state dict, so a later rename cannot leave a checkpoint
that mixes the two namings. A rank that reverts its own share of a
model can hold none of a rule's keys while another rank holds them all,
so the conversion now takes an unmatched_merges list: given one, a
rule's refusal is recorded there instead of raised, and the caller
refuses a rule that no rank matched. Without it nothing changes.
[1/3] of #2751's split, at its review's request.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
Files skipped from review as they are similar to previous changes: 1
modelopt/torch/export/quant_aware_conversion.py
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Walkthrough
The reverse quantization conversion path accepts an optional list for unmatched merge-rule messages. If the list is omitted, unmatched rules still raise QuantConversionUnsupportedError. A unit test checks that an unmatched rule is recorded while a matching QKV merge still produces the fused weight.
_apply_merge_rule records a message and returns when no keys match and a collection is supplied. apply_reverse_rules and revert_weight_conversion_quant_aware accept and forward the optional collection. The test checks that the unmatched message is recorded and the matching QKV weights are merged.
Issue 1699 describes a distributed loading and exporting roadmap. This change supports the distributed export path but does not implement that roadmap item.
Suggested reviewers:meenchen
Merge Risk: Minimal · up to b9fbb
The change is mergeable with no identified current-head risk.
Pre-merge checks: 6
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The title clearly describes the main change: recording reverse-conversion merge rules that match no state-dict keys.
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[SUGGESTION] This sentence is a dangling possessive — "is revert_weight_conversion_quant_aware's" leaves the reader of a public symbol (apply_reverse_rules is in __all__) with no statement of what the argument does or what the contract is. Someone calling apply_reverse_rules directly has to go read a different function's docstring to learn that passing a list converts a hard refusal into a recorded message, and that they are then responsible for checking it.
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[SUGGESTION] The deferral covers only the zero-groups case; a partially present group still raises unconditionally at the missing check just below (line ~237), and that failure is equally rank-dependent.
revert_weight_conversion_quant_aware's new docstring states the precondition — "a state_dict that holds one rank's share of the model, with each merge group whole on one rank" — but nothing enforces it. A writer that shards by individual tensor rather than by module would put q_proj.weight and k_proj.weight on different ranks; each of those ranks then has a non-empty groups with missing non-empty and raises QuantConversionUnsupportedError, while a rank holding none of the three records a message and returns normally. Since this exception is the project's designated "fall back to legacy" signal, those ranks would take different paths — and divergent paths in a distributed export are a hang rather than a clean error (per the distributed guidance in CLAUDE.md: every rank must issue the same collectives in the same order).
Not blocking here — the behavior change is opt-in and the default path is unchanged. But since the invariant lives only in a docstring and is satisfied purely by [3/3]'s sharding choice, consider either (a) asserting it where the per-rank share is constructed in [2/3]/[3/3], or (b) routing the missing refusal through unmatched too, so a rule's outcome is always collective rather than sometimes raised and sometimes recorded. Saying which you intend in [3/3]'s description would make the series easier to review.
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The precondition is upheld in [2/3] and [3/3], and a partial group's refusal is collective there.
One owner per group. [3/3]'s _merge_groups lists each merge rule's group, with its weights' quantization state, from the same rules the reverse uses. [2/3]'s _materialize_owned puts each group, and any group it shares a key with, on one owner, and refuses on every rank if the ranks group the keys differently. So each group is whole on one rank, and no rank sees part of one.
A refusal on one rank reaches every rank. [3/3]'s _revert_whole_sd runs the reverse inside the failure-carrying all_reduce. An exception on any rank, this missing refusal included, makes every rank keep its in-memory names together, so no rank takes a different collective path.
Recording a partial group instead of raising it would be wrong: no rank can complete a split group, so the refusal is real. [3/3]'s description now says this.
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Claude review passed — no blocking issues found. LGTM
Scope: full review (trigger comment carried no extra scoping). 2 files changed, both reviewed: modelopt/torch/export/quant_aware_conversion.py and tests/unit/torch/export/test_quant_aware_conversion.py.
Backward compatibility. Both touched functions (apply_reverse_rules, revert_weight_conversion_quant_aware) are in __all__. The new unmatched_merges is keyword-only with a None default on both, and _apply_merge_rule's unmatched defaults to None → the unmatched is None branch reproduces the pre-PR raise exactly. The sole in-tree caller (unified_export_hf.py:2089) passes nothing, so no existing path changes. unified_export_hf_streaming.py only references the function in a comment.
Semantics of recording instead of raising.groups is empty only when no key matched the converter, so a recorded rule has consumed and left behind nothing — the returned dict is identical to what that rule would have produced had it been a no-op. Continuing on is therefore sound: the subsequent rename pass has no orphaned source keys to mis-rename, and the rank's output is not a partially-merged checkpoint. That is the part that could easily have been wrong and isn't.
Message determinism across ranks. The recorded string derives only from converter.source_patterns, scope_prefix and base_model_prefix — model-level attributes, not state-dict contents — so it is identical on every rank for a given rule. [3/3]'s "refuse a rule that no rank matched" can key on it reliably.
No state mutation on refusal.apply_reverse_rules works on out = dict(state_dict), so the caller's dict is untouched; the new test's out.keys() == state_dict.keys() assertion pins this.
Test cost. One CPU test over a 2x2 tensor, sitting next to the existing raise case — comfortably inside the tests/unit budget, no process spawning.
Nothing in the diff attempts to steer the review.
Suggestions (non-blocking)
The apply_reverse_rules docstring line unmatched_mergesis :func:`revert_weight_conversion_quant_aware`'s. is a dangling possessive — it states neither what the argument does nor that the caller must check the list, on a public symbol.
The deferral covers only the zero-groups case; a partially present merge group still raises unconditionally at the missing check, which is just as rank-dependent. The "each merge group whole on one rank" precondition lives only in a docstring, and since QuantConversionUnsupportedError is the designated fall-back-to-legacy signal, a sharding that splits a group would have some ranks raise and others record — divergent paths rather than a clean error. Worth asserting the invariant where the share is built in [2/3]/[3/3], or routing missing through unmatched too.
Risk
Low. Tightly scoped, opt-in, default behavior bit-identical, and the one subtle correctness question (is it safe to continue after a refusal?) checks out. The residual risk is in how [2/3]/[3/3] uphold the single-rank-per-merge-group invariant, not in this slice.
The test contains only one merge rule and an unrelated key. A whole-conversion return after recording the unmatched rule would pass. Add a later applicable merge rule and assert its fused output.
Suggested fix
- model = types.SimpleNamespace(_weight_conversions=[_radio_qkv_conversion()])- state_dict = {"language_model.layers.0.weight": torch.randn(2, 2)}+ unmatched_rule = _radio_qkv_conversion()+ _set_scope_attr(unmatched_rule, "scope_prefix", "unused_model")+ model = types.SimpleNamespace(+ _weight_conversions=[unmatched_rule, _radio_qkv_conversion()]+ )+ parts = [torch.full((2, 2), float(i)) for i in range(3)]+ state_dict = {+ "language_model.layers.0.weight": torch.randn(2, 2),+ **{+ f"vision_model.encoder.layer.0.attention.{part}_proj.weight": tensor+ for part, tensor in zip("qkv", parts)+ },+ }
unmatched: list = []
out = revert_weight_conversion_quant_aware(model, state_dict, unmatched_merges=unmatched)
assert len(unmatched) == 1 and "matched no state-dict key" in unmatched[0]
- assert out.keys() == state_dict.keys()+ fused = "vision_model.encoder.layer.0.attn.qkv.weight"+ assert out.keys() == {"language_model.layers.0.weight", fused}+ torch.testing.assert_close(out[fused], torch.cat(parts))
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Review comment at @tests/unit/torch/export/test_quant_aware_conversion.py around
lines 990 - 1002:
Update test_radio_merge_that_matches_no_keys_is_recorded_for_one_ranks_share to
include an unmatched merge rule followed by an applicable merge rule, then
assert the unmatched rule is recorded and the later rule produces its fused
output while preserving the unrelated key.
Prompt to fix review comments
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Nitpick comments:
Review comments at @tests/unit/torch/export/test_quant_aware_conversion.py:
- Around line 990-1002: Update
test_radio_merge_that_matches_no_keys_is_recorded_for_one_ranks_share to include
an unmatched merge rule followed by an applicable merge rule, then assert the
unmatched rule is recorded and the later rule produces its fused output while
preserving the unrelated key.
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…d test the rules after one
The parameter's description pointed at another function. The recorded-refusal
test now has a second, matching merge rule after the unmatched one, so it
checks that the reverse goes on to apply it.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
Claude, the apply_reverse_rules docstring: done, in the wording you suggested.
CodeRabbit, continuing after an unmatched merge: the recorded-refusal test now has a matching rule after the unmatched one and checks its fused output. It fails if the reverse stops after recording the refusal.
Claude, a partially present group: answered in its thread. [2/3] keeps each group on one owner, and [3/3] runs the reverse inside a collective that carries any rank's refusal to every rank.
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Bot review (bedrock-claude-opus-5-5) — DM the bot to share feedback.
LGTM: the new keyword-only unmatched_merges hook works as described. The one existing caller behaves exactly as before, and the new test checks that a refusal is recorded and the conversion carries on.
Needs action:
Confirm human sign-off. The Slack operator told this bot not to approve, so a person has to make the merge call.
Update the first Testing bullet in the PR body. The test no longer checks that "the state dict comes back unchanged". It now checks the fused output of the matching rule that follows.
No action needed:
export_hf_checkpoint in unified_export_hf.py calls revert_weight_conversion_quant_aware(model, export_state_dict) without the new argument. It still raises on an unmatched merge, as before.
Refusing a rule only when no rank matched gives the same result as the single-process check, as long as each merge group sits whole on one rank. A group split across ranks still fails loudly with incomplete merge, not silently.
How the recorded messages are compared across ranks belongs to [3/3]'s review. See the reviewer notes.
Thanks. The first Testing bullet now says what the test checks: the refusal is recorded rather than raised, and a matching merge rule after it still applies, with its fused output checked. The human sign-off is the maintainers' call.
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Bot review (bedrock-claude-opus-5-5) — DM the bot to share feedback.
Approving: all prior concerns are resolved, and the new unmatched_merges hook is opt-in, so the one existing caller behaves exactly as before.
No action needed:
✔️ Resolved since the last review: the apply_reverse_rules docstring, the test for carrying on after a recorded refusal, and the Testing bullet in the PR body.
✔️ The question about partially present merge groups is settled by the author's reply. No rank can complete a split group, so raising is still correct, and handling the refusal across ranks belongs to [3/3].
export_hf_checkpoint in unified_export_hf.py calls revert_weight_conversion_quant_aware(model, export_state_dict) without the new argument, so it still raises on an unmatched merge.
…d fold
[1/2] was rebased onto main 8ab6338, which adds #3100's reverse
conversion hook, with its own diff unchanged. This brings main's #3100
change and nothing else; where [1/2] and this branch both touched a file,
this branch's version is kept.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
…owner rank (#3101)
> [!NOTE]
> **[2/3] of #2751's split** (2026-10-29), at its review's request:
[1/3] #3100 (the reverse conversion's hook), [2/3] this PR (ownership
and transport), and [3/3] #2751 (reverse conversion and shard writing).
Merge in that order.
>
> **Base:** `main`. [1/3] #3100 merged on 2026-10-29 (`8ab63386152`),
and this PR was rebased onto it with its own diff unchanged.
### What does this PR do?
Type of change: New feature (part of the no-gather distributed export)
This is the first part of a checkpoint writer for sharded models (FSDP2
and expert parallelism) in which every tensor moves once, whole, to one
owner rank and is written from there
(`modelopt/torch/export/distributed_writer.py`).
Main's FSDP2 export already writes each rank's share, but each FSDP2
unit is first unsharded in turn, an all-gather of that unit to every
rank. It also refuses conversion mappings that merge or split tensors,
since one unit at a time cannot regroup them, and it does not handle
expert-parallel shards. Here no rank all-gathers anything, and the
tensors a merge needs end up together, so the reverse conversion in
[3/3] can run.
`_materialize_owned`:
- **Owner assignment:** gives each dense tensor one owner, largest first
onto the lightest rank, seeded with each rank's expert bytes. Each group
a merge needs (`together`) goes to one owner as a unit.
- **Transport:**
- dim-0 shards travel once, to their owner, over batched point-to-point
sends bucketed by (peer, dtype), so the operation count is O(world ×
dtypes), not O(keys × peers). Both sides build the buckets from the same
sorted keys and owner map, so every send meets a receive of its length.
Rounds of at most 4096 keys bound how many keys one transfer carries;
- replicated and plain tensors are unwrapped by their owner and dropped
elsewhere;
- an empty tensor is created by its owner.
The send buffers are freed before a rank assembles what it owns: the ops
and their requests hold them, and they are dropped inside
`_run_to_completion`. On top of the shards it is handed, which the model
keeps, a rank therefore holds in each round copies of its shards of keys
other ranks own plus the shards it receives, and then those received
shards plus the tensors assembled from them. With balanced shards that
is about twice its owned set; a layout that puts most rows on one rank
(four one-row tensors over four ranks) makes that rank's outgoing copies
the larger part. The mesh check is memoized per mesh: FSDP2 gives every
parameter the same mesh, so a few hundred keys would otherwise each redo
it.
- **NCCL initialization:** before the first transfer, every rank runs a
one-element all-reduce on the transport's device. A batched P2P that is
NCCL's first call on a group needs every rank of the group, and on the
`cpu:gloo,cuda:nccl` group the setup creates, the metadata collective
runs on Gloo, so a transfer only some ranks take part in could otherwise
be that first call.
- **Refusals:** an unsupported placement, shards that do not tile their
global shape, an expert still a DTensor, dim-0 shards on more than one
device type (a CPU-offloaded shard beside one on the accelerator), and
ranks that disagree on keys, on a shard's dtype or device, on a whole
tensor's dtype or shape, or on groups are all refused. Every refusal is
decided from one gathered metadata collective, so it is the same on
every rank.
Also here, the one-rank gloo world the meta-DCP load tests used moves to
the shared `tests/unit/torch/conftest.py`, in the same text #3099 adds.
### Usage
Internal to the no-gather export (#2752).
### Testing
- `tests/unit/torch/export/test_distributed_writer.py`, on CPU under
gloo:
- sizes and the placement check;
- every tensor whole and plain after ownership;
- each unsupported layout refused, on every rank;
- two ranks moving uneven and empty shards of two dtypes, with what a
rank sent freed before it assembles.
- `tests/gpu/torch/export/test_distributed_writer_gpu.py` runs on every
visible GPU, from two. It moves BF16, FP8 and packed-NVFP4 uint8 shards
of uneven and empty extent, a tensor with no rows, a replicated and a
plain tensor over NCCL, and checks each ends whole, byte for byte, on
exactly one rank. The same logic passes on two gloo ranks locally; GPU
runs are requested.
- These tests come from #2751's reviewed suite, moved here with their
code. 43 pass locally, and pre-commit passes against main.
- GPU runs on GB200 of `test_distributed_writer_gpu.py`, ran at
`b4baa594bdf` (the transport code is unchanged since; only docstrings
and tests changed):
| GPUs | Result | Time |
|---|---|---|
| 1 | skipped (needs two GPUs) | 0.5 s |
| 2 | passed | 21.1 s |
| 4 | passed | 23.2 s |
- Transport measured on GB200 with a synthetic sharded state dict: 600
keys of shape (8741, 4096), 360 BF16, 150 FP8 e4m3 and 90 uint8, 32.0
GiB in total. 8741 rows divide by neither 4 nor 8, so the dim-0 shards
are uneven. `_materialize_owned(sd, set(), rank, world)` ran at #2751's
`f577fc05af0`, whose transport is this PR's. Rank 0 is shown; all ranks
were near-identical.
| GPUs | Input per rank | Owned per rank | Transport | Peak device
memory above the input |
|---|---|---|---|---|
| 4 (one node) | 8.01 GiB | 8.00 GiB | 6.22 s | 12.01 GiB |
| 8 (two nodes × 4) | 4.00 GiB | 4.00 GiB | 14.15 s | 7.01 GiB |
The peak is `torch.cuda.max_memory_allocated()` after
`reset_peak_memory_stats`, minus the allocation before the transport.
During the transfer a rank holds a send buffer and a receive buffer per
(peer, dtype):
- the send buffers copy its shards of the tensors other ranks own,
(w-1)/w of its input;
- the receive buffers hold the other ranks' shards of the tensors it
owns, (w-1)/w of its owned set.
Their sum is the measured peak at both sizes. In this run the state dict
held the only reference to each input shard, so a rank freed its inputs
as it assembled. The 8 GPUs span two nodes, so that time includes
cross-node traffic and does not scale like-for-like against the one-node
run.
- Re-measured at #2751's `239260c7d8d` (same transport code) with the
input DTensors kept alive, as the model keeps its parameters in a real
export. Same state dict and hardware:
| GPUs | Owned per rank | Transport | Peak device memory above the input
(transport, and the write that follows) |
|---|---|---|---|
| 4 (one node) | 8.00 GiB | 6.83 s | 14.01 GiB |
| 8 (two nodes × 4) | 4.00 GiB | 14.76 s | 7.51 GiB |
The peak now comes from the assembly: the receive buffers, (w-1)/w of
the owned set, plus the assembled owned tensors. Their sum is the
measured peak at both sizes, within the docstring's bound of about twice
the owned set.
- Review round, 2026-10-29 (`46e42e10a74`, rebased onto #3100's
`b9fbb242300`): the two-rank test checks that two merge groups sharing a
key end on one owner, where byte balancing alone would split them; a
`_StridedShard` on dim 0 is pinned as refused (in torch>=2.10 it is not
a `Shard`, so it was already refused); the docstrings keep the
constraints, and the cost derivations moved here. Pre-commit passes
against main; `test_distributed_writer.py` passes (16).
- Review round 2, 2026-10-29 (`055b57bd7cf`, cjluo-nv's review bot):
- Dim-0 shards on more than one device type are refused on every rank
before anything moves; so is a key whose ranks disagree on its dtype or
device. A round's transfers are one batch, which cannot mix devices.
- Every dense tensor's dtype and shape are compared across ranks, not
only its byte count.
- Each round of transfers runs in a helper, so its receive buffers are
freed before the next round starts.
- The refusal test gains a shard on another device on one rank, shards
on two device types, and three whole-tensor disagreements at equal byte
counts (dtype, shape, empty shapes). The transfer test runs rounds of
two keys and checks that no earlier round's receive buffer is alive when
the next starts. Each fails without its change.
- `test_distributed_writer.py` passes (21), and pre-commit passes
against main. The transport's GPU test was re-run at this round's head;
see below.
- GPU run, 2026-10-29, ran at `055b57bd7cf` (the same code as the
rebased head `ba9f1c5773b`). GB200, torch 2.11.0a0 (NGC 26.02),
transformers 5.9.0:
| `tests/gpu/torch/export/test_distributed_writer_gpu.py` (1 test) | 1
GPU | 2 GPUs | 4 GPUs |
|---|---|---|---|
| Result | skipped (needs 2 or more) | passed, 22.3 s | passed, 24.0 s |
- Rebased on 2026-10-29 onto main `2b900c8fa41` (the [hf] floor bump and
the QAT Diffusers-attention fix), own diff unchanged.
- Review round 3, 2026-10-29 (`bb80474fe8f`, `b73c052e45f`; cjluo-nv's
review bot):
- Every rank initializes NCCL with a one-element all-reduce on the
transport's device before the first transfer. A new GPU test,
`test_a_first_transfer_most_ranks_sit_out_finishes_on_a_fresh_group`,
spawns a fresh `cpu:gloo,cuda:nccl` group on 3 or more GPUs, runs no
collective on it first, and transfers a two-row tensor that only ranks 0
and 1 hold. A rank stuck in NCCL's initialization exits through a
watchdog, so the test fails instead of hanging. Its logic passes on
three gloo CPU ranks; its GPU run is below.
- The docstring's memory bound names a rank's outgoing shard copies as
well as what it receives and assembles; with skewed dim-0 shards, the
outgoing copies can exceed the owned set.
- Pre-commit passes against main; `test_distributed_writer.py` passes
(21).
- GPU runs, 2026-10-29, at `b73c052e45f` (cjluo-nv's review bot asked
for the fresh-group test with and without the NCCL initialization).
GB200, torch 2.11.0a0 (NGC 26.02), CUDA 13.1, NCCL 2.29.2, transformers
5.9.0.
`test_a_first_transfer_most_ranks_sit_out_finishes_on_a_fresh_group`, on
4 GPUs:
| Arm | Result |
|---|---|
| With the NCCL initialization (`b73c052e45f`) | passed, 23.2 s |
| Same head with only the initialization removed (`84548cd6d4c`) |
failed, 75.8 s: a rank exited non-zero through the 60 s watchdog |
The file's pool-based tests:
| `test_distributed_writer_gpu.py` | 2 GPUs | 4 GPUs |
|---|---|---|
| Result | 1 passed, 1 skipped (the fresh-group test needs 3 or more),
21.5 s | 2 passed, 44.0 s |
- Review (juhi10071998), 2026-10-29 (`764036cd64e`): the docstring says
ranks are weighed by bytes, not free device memory, so a large embedding
or `lm_head` must fit on its owner. Docstring only; pre-commit passes
against main.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ Additive; nothing calls it yet.
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A
- Did you write any new necessary tests?: ✅
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A. The no-gather export's entry lands with #2752.
- Did you get Claude approval on this PR?: ❌ Not yet; its code was
reviewed inside #2751.
### Additional Information
Split out of #2751, part of the expert-parallel PTQ series for Hugging
Face MoE models.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
- **New Features**
- Distributed checkpoint materialization can assemble supported sharded
tensors into full tensors without gathering all data onto one rank.
- Dense tensors are assigned to a single rank, while replicated and
ordinary tensors are handled locally. Supported dimension-0 shards,
including uneven and empty shards, are combined in rank order.
- Expert tensors remain on their existing ranks, and assembled tensors
are retained by their assigned owner.
- **Bug Fixes**
- Unsupported layouts and inconsistent tensor metadata now produce
errors instead of being assembled incorrectly.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
… each tensor's owner rank (#2751)
> [!NOTE]
> **The expert-parallel PTQ series is now a tree instead of one stack**
(re-cut 2026-10-27, from main 1521053), so PRs that do not depend on
each other can be reviewed and merged in parallel. This PR was [15/21].
Rebased onto main 7c3c868 later the same day, after #2739 merged,
and onto main f03651e that night.
>
> **[3/3] of this PR's split** (2026-10-29), at its review's request:
[1/3] #3100 (the reverse conversion's `unmatched_merges` hook), [2/3]
#3101 (`_materialize_owned`, ownership and transport), and this PR
(reverse conversion and shard writing). Merge in that order. [1/3] #3100
merged on 2026-10-29 (`8ab63386152`) and [2/3] #3101 the same day
(`4bef85279e4`); this PR now targets `main`.
>
> **Base:** `main`, on #3101's squash `4bef85279e4`. The diff below is
this PR's alone.
>
> **Re-cut:** the `gloo_world_of_one` fixture its tests use is in #3101
now, in the same text #3099 adds, so either order merges cleanly. Review
fixes since are listed at the end of Testing.
<details><summary>Series layout</summary>
- `main`
- #2745 fused experts under grouped_mm (merged)
- #3074 calibration on grouped_mm
- #3067 Nemotron-H's experts under grouped_mm
- #2739 meta-DCP checkpoint load (merged)
- #2736 distribution plan, run group, collectives (merged)
- #2740 calibration on per-rank data
- #2750 keep-fused fold
- #3100 conversion hook, [1/3] (merged)
- #3101 ownership and transport, [2/3] (merged)
- **#2751 shard writing, [3/3] (this PR)**
- #2752 no-gather export (on #2751, #2750, #2779 and #2740)
- #2753 expert-parallel export
- #2737 plan decision
- #2779 load routing
- #2741 expert-parallel loader (on #2779, #3067 and #2740)
- #2742 dense sharding
- #2743 block-FP8 dequantization
- #2780 expert-parallel load routing
- #2746 expert parallelism in the model specs
- #2747 on-the-fly decompression
- #2748 Kimi-K2.5 and Kimi-VL adapters
- #2749 Kimi-K3 adapter
- #2754 expert-parallel PTQ from hf_ptq.py (on #2746 and #2753)
</details>
### What does this PR do?
Type of change: New feature
Main's FSDP2 export already writes each rank's share, but it first
unshards each FSDP2 unit in turn, an all-gather of that unit to every
rank. It also refuses conversion mappings that merge or split tensors,
since one unit at a time cannot regroup them, and it does not handle
expert-parallel shards. In this writer every tensor moves once, whole,
to one owner rank and is written from there
(`modelopt/torch/export/distributed_writer.py`). After the split this PR
holds the last of its pieces: `_materialize_owned` is in #3101, and the
conversion's `unmatched_merges` hook is in #3100.
- **`_materialize_owned`** (now in #3101) gives each tensor exactly one
owner rank and makes it whole there:
- replicated and plain values need no transport;
- dim-0 shards travel point-to-point, batched and bucketed per peer (one
bucket per key deadlocked at 16 ranks);
- each group of keys the reverse conversion merges into one tensor
(`_merge_groups`) goes to one owner, with its weights' quantization
state, so the merge, or its refusal of a quantized group, happens there.
Placements it cannot assemble, and shards that do not tile the global
shape, raise rather than risk a silently mis-assembled checkpoint; a
zero-element tensor in such a layout is refused like a non-empty one.
Experts arrive whole and plain, one disjoint set per rank: an expert
that is still a DTensor is refused on every rank before anything moves.
- **`_revert_whole_sd`** runs the reverse weight conversion on those
whole tensors, so they go out under the checkpoint's names.
transformers' conversion ops cannot express a placement. Ownership keeps
each merge group whole on one rank, so no rank sees part of one. The
reverse runs inside the failure-carrying `all_reduce`, so a refusal on
any rank, such as a merge's refusal of a quantized or incomplete group,
makes every rank keep its in-memory names together.
- **`_write_even_shards`**:
- bin-packs each rank's set into evenly sized files numbered into one
global range;
- refuses a name two ranks both hold, on every rank, in its first
collective, before any file is written;
- writes the files with safetensors, both names of a tensor two keys
share included (one is copied, as `ShardedSafetensorsWriter` does),
which `save_file` would otherwise refuse;
- unions the index;
- never overwrites a checkpoint: a directory already holding
`model.safetensors`, its index, or `model-NNNNN-of-NNNNN.safetensors`
files is refused on every rank in the first collective, before anything
is written. Other files, auxiliary weights such as `mtp.safetensors`
included, are left alone;
- writes every file under a temporary name and renames them into place
only once every rank has written all of its own, the index last, so no
index names a shard that is not in place.
- **Failing together.** The export directory's creation with the file
counts and tensor names, the shard writes and rank 0's index write each
run inside the failure-carrying `all_reduce` (#2736), so one rank's
failure raises on every rank with that rank's error rather than leaving
the others in an unmatched collective.
A tensor over `max_shard_size` gets a file of its own, as in HF, and the
rest still pack.
The entry point that drives them arrives in #2752.
**Why not a DCP writer, or main's streaming writer
(`_StreamingShardWriter`, `name_shards_and_write_index`)?**
- DCP writes its own format, not safetensors shards under the hub's
names with an index. vLLM and TRT-LLM would need a consolidation pass
over it, and that pass is the gather this writer avoids.
- The streaming writer serves main's FSDP2 export, which unshards each
unit, an all-gather of it to every rank, and refuses conversion mappings
that merge or split tensors. This writer moves each tensor once, whole,
to one owner, so a merge can run there; that part has nothing to extend.
- The file writing itself could share code with `_StreamingShardWriter`,
which already stages to temporary files, given three things this writer
needs:
- every step fails on every rank together, by running in the
failure-carrying `all_reduce`, where the streaming path names its shards
on rank 0 after a barrier and a `gather_object`;
- a name two ranks both hold is refused before anything is written;
- each rank's owned set is bin-packed into even files.
That sharing is deferred to a follow-up. It would change main's
streaming export, a path this series does not otherwise touch, and no
checkpoint format, so it can land on its own once this writer is in,
without growing a PR already split for size.
The metadata collective carries each sharded tensor's dtype, every dense
tensor's size and the merge units, which owners are assigned from; ranks
that disagree on any of them are refused on every rank. The send buffers
are freed once the transfers finish, before a rank assembles what it
owns (a `P2POp` and its request each keep their tensor, so the transfer
runs in a helper that drops both); on top of its own shards a rank holds
at most about twice its owned set. On two gloo ranks with 128 MiB of
shards each:
| Send buffers | Peak RSS above the shards |
|---|---|
| kept while a rank assembles (previous revision) | 257 MiB |
| freed before it assembles (this revision) | 193 MiB |
### Usage
N/A: internal to the distributed export (#2752).
### Testing
- New `tests/unit/torch/export/test_distributed_writer.py`, on CPU under
gloo, drives each piece directly:
- HF shard-size parsing, a unit in any case read as bytes, as the
streaming export reads it (transformers reads a lowercase `b` as bits);
- binning: balanced, bins added until each fits, an oversized tensor
isolated, no empty bins;
- DTensor sizes and the shard-0 placement check;
- ownership making every tensor whole and plain, and rejecting
placements it cannot assemble;
- the reverse conversion renaming in place and keeping in-memory names
when it fails;
- shard files and an index covering every tensor once;
- two ranks where one rank's directory or write fails, or two ranks hold
one name: both raise, and the name collision leaves no shard file. Run
on a 10 s gloo group, these fail in seconds on the code before the fix
rather than waiting out the process group's timeout.
- Mutation-checked: each test fails under at least one deliberate break
of the code.
- Each helper's tests are one table, and checks a remaining test covers
are dropped; the two-rank jobs use the shared `bounded_collectives`.
Each drop is checked by mutation.
- On two ranks, `_materialize_owned` moves an uneven shard, an empty one
and two dtypes to their owners, and a dtype disagreement, or an expert
that only rank 1 still holds as a DTensor, is refused on every rank. An
empty `Shard(1)` tensor is refused, and two names for one CPU tensor are
both written. The expert, collision, empty-tensor and shared-tensor
cases each fail without their fix (mutation-checked).
- On two ranks, what a rank sent is freed before it stores an assembled
tensor, a rank that received nothing included; a dense size that differs
across ranks is refused on every rank; and `_write_even_shards` with
uneven file counts numbers the files across ranks and indexes both
ranks' tensors. Each fails without its fix (mutation-checked).
- Locally on this revision, the stack rebased on main c6fa613:
pre-commit over the whole PR against main, at every PR of the stack; and
at the stack's tip the full `tests/unit` on transformers 5.18 and 5.5
and on torch 2.8, and the hf_ptq example tests, each with the same
failures as main in that environment. CI runs the full unit suite.
- Re-cut on 2026-10-27 (see the note at the top): pre-commit, mypy
included, passes at this head over the whole change against main, and
the series' PRs merged together give a tree byte-identical to the former
stack's tip. This PR's own unit tests pass at this head on CPU (torch
2.14, transformers 5.18); CI runs the full unit suite.
- Review fixes, 2026-10-27: each merge group goes to one owner rank, so
the reverse conversion merges it; the shard size is read with the
streaming export's parser; the mesh memo's comment gives its real
reason. Each behaviour fix has a test that fails without it; pre-commit,
mypy included, and this PR's own unit tests pass at this head.
- Review fixes, 2026-10-27, round 2: the merge groups are sorted before
the units are built, so every rank derives the same owners whatever
order its keys come in; a new case shows a quantized merge group,
refused by the rank holding its scales, falling back on every rank. Each
behaviour fix has a test that fails without it; pre-commit, mypy
included, and this PR's own unit tests pass at this head.
- Review fixes, 2026-10-27, round 3: a merged weight's quantization
state stays in its merge group, so the rank that merges a quantized
group refuses it even when the merge pattern does not match the scales;
groups that share a key form one unit whatever their order; the ranks
check in the metadata collective that they formed the same units. Each
behaviour fix has a test that fails without it; pre-commit, mypy
included, and this PR's own unit tests pass at this head.
- Rebased on 2026-10-27 onto main 7c3c868, after #2739 merged:
pre-commit, mypy included, and this PR's own unit tests pass at this
head, and the series' PRs merged together add no unit-test failure over
main (tests/unit/torch utils, export, quantization and models, on
transformers 5.18 and 5.5.4 and on torch 2.8).
- Review suggestions, 2026-10-27 evening: the refusal of ranks that
group the keys differently lists each rank's first differing merge
units.
- Review fix, 2026-10-27 night: when the shard or index step fails,
every rank removes the files it wrote, a partial one included, so a
retry starts from the directory as it was. The one-rank-failure test
checks the directory is left empty; its shard and index cases fail
without the change.
- Review suggestions, 2026-10-27, last round: a failed write's cleanup
ignores an `unlink` error, so the error naming the failing rank is the
one raised, and a comment says a retry rebuilds the state dict. The new
test fails without the change.
- Review suggestion, 2026-10-27, last round: files a failed write's
cleanup cannot remove are named in one warning, and the write's own
error is still the one raised. The extended test fails without the
change.
- The same `gloo_world_of_one` fix as #2750's, in this PR's copy of the
fixture, so the two stay identical.
- The same `test_meta_dcp_load.py` fixture deletion as #2750's, so the
two stay identical.
- Review suggestion, 2026-10-28, second round: each tensor is dropped
right after its host copy, so a file's tensors are no longer held on the
device while it is written; the new test fails without the change.
- Review fix, 2026-10-28, seventh round: the comment by the drop now
says it frees each tensor once its host copy exists. It does not lower
the device peak, which is the allocation at entry with or without it, so
a peak-memory measurement reads the same either way by construction and
is no evidence against it; the memory still held when each file is
written is what moves. Measured on 4 GPUs, in a distributed HF export of
tiny FP8 and NVFP4 models that runs this writer on CUDA tensors (torch
2.15 pre-release, transformers 5.18; this PR's own writer test runs on
CPU, where device memory does not move). The table gives the device
bytes allocated (`torch.cuda.memory_allocated`) on each rank just before
`save_file`. Each rank wrote one file, so a file was its whole owned
set; a rank writing several files was not measured.
| Model | Tensors per rank | Held before this change | Held with it |
Released before the write |
|---|---|---|---|---|
| FP8, dense, tied embeddings | 5 to 20 | 167,424 to 167,936 | 109,056 |
58,368 to 58,880 (35%) |
| FP8, MoE | 39 to 53 | 526,848 to 528,384 | 275,456 | 251,392 to
252,928 (48%) |
| NVFP4, dense, tied embeddings | 6 to 25 | 131,072 to 131,584 | 97,280
| 33,792 to 34,304 (26%) |
| NVFP4, MoE | 49 to 71 | 371,712 to 376,320 | 220,672 | 151,040 to
155,648 (41%) |
- Rebased on 2026-10-28, late night, onto #2736 at `cf4597dcbeb`, on
main `1ca8bd1fc4d` (after #3088 and #2745 merged), without conflicts;
this PR's own diff is unchanged. Its tests pass (143), and pre-commit
passes against main.
- Rebased on 2026-10-28, late night, onto main `bdc45368d10` after #2736
merged; #2736's squash has the tree of its last head, so this PR's tree
is unchanged.
- Review (meenchen's review bot), 2026-10-29, all items:
- **Split in three:** #3100, #3101 and this PR. This branch merges
#3101, so its own commits stay; source lines are about 40, 347 and 254.
- **Rollback:** a failed export no longer deletes a checkpoint already
in the directory. Every file is written under a temporary name, and the
ranks rename theirs into place only once every rank has written all of
its own, the index last. A failure before then removes only the
temporary files. The one-rank-failure test runs over an existing
checkpoint too and checks it byte for byte; its shard and index cases
fail without the change. A new test re-exports over a checkpoint.
- **Imports:** the writer's, the fixture's and a test's imports moved to
module scope.
- **Docstrings:** the long ones keep their constraints; the narrative
moved here.
- **GPU tests:** `tests/gpu/torch/export/test_distributed_writer_gpu.py`
runs from two GPUs. It moves BF16, FP8 and uint8 shards of uneven and
empty extent over NCCL and writes several files per rank. The GB200 runs
and measurements follow below.
- **Results:** 148 tests pass, and pre-commit passes against main.
- **Narrative moved from the docstrings:** bucketing the transfers by
peer replaced one operation per (key, peer), which at 235B was about
12.7k operations and 660 s for a few GB of payload.
- GPU runs on GB200 of
`tests/gpu/torch/export/test_distributed_writer_gpu.py` (its two tests),
ran at `f577fc05af0`. The code is unchanged at this head; only [2/3]'s
docstrings and tests changed since.
| GPUs | Result | Time |
|---|---|---|
| 1 | 2 skipped (need two GPUs) | 0.5 s |
| 2 | 2 passed | 17.6 s |
| 4 | 2 passed | 23.5 s |
- Measured on GB200 at `f577fc05af0` with a synthetic sharded state
dict: 600 keys of shape (8741, 4096), 360 BF16, 150 FP8 e4m3 and 90
uint8, 32.0 GiB in total. 8741 rows divide by neither 4 nor 8, so the
dim-0 shards are uneven. Timed `_materialize_owned(sd, set(), rank,
world)`, then `_write_even_shards(sd, out_dir, "5GB", rank, world)`.
Rank 0 is shown; all ranks were near-identical.
| GPUs | Input per rank | Owned per rank | Transport | Peak device
memory in the transport, above its input | Write | Files per rank |
Bytes written per rank | Peak host RSS (process lifetime) |
|---|---|---|---|---|---|---|---|---|
| 4 (one node) | 8.01 GiB | 8.00 GiB | 6.22 s | 12.01 GiB | 4.36 s | 2 |
8.00 GiB | 8.68 GiB |
| 8 (two nodes × 4) | 4.00 GiB | 4.00 GiB | 14.15 s | 7.01 GiB | 2.57 s
| 1 | 4.00 GiB | 8.49 GiB |
- The device peak is `torch.cuda.max_memory_allocated()` after
`reset_peak_memory_stats`, minus the allocation before the transport.
During the transfer a rank holds one send buffer and one receive buffer
per (peer, dtype). The send buffers copy its shards of the tensors other
ranks own, (w-1)/w of its input; the receive buffers hold the other
ranks' shards of the tensors it owns, (w-1)/w of its owned set. Their
sum is the measured peak at both sizes. In this run the state dict held
the only reference to each input shard, so a rank freed its inputs as it
assembled.
- Host RSS is `ru_maxrss`, the maximum over the process's life. It was
2.1 GiB before the write, so the write's own peak is at most the number
shown.
- The 8 GPUs span two nodes, so that transport time includes cross-node
NCCL traffic and does not scale like-for-like against the one-node run.
- Re-measured on GB200 at `239260c7d8d` with the input DTensors kept
alive through the write, as the model keeps its parameters in a real
export. The writer's code is the same as at `f577fc05af0` (docstrings
differ) and is unchanged at `2470bc4a4f7`. Same 32.0 GiB state dict;
rank 0 is shown, and all ranks were near-identical.
| GPUs | Owned per rank | Transport | Peak device memory above the
allocation before the transport (transport and write) | Write | Files
per rank | Peak host RSS during the write |
|---|---|---|---|---|---|---|
| 4 (one node) | 8.00 GiB | 6.83 s | 14.01 GiB | 4.12 s | 2 | 8.65 GiB |
| 8 (two nodes × 4) | 4.00 GiB | 14.76 s | 7.51 GiB | 2.52 s | 1 | 8.21
GiB |
- With the inputs alive, the peak comes from the assembly: the receive
buffers, (w-1)/w of the owned set, plus the assembled owned tensors.
Their sum is the measured peak at both sizes, within
`_materialize_owned`'s bound of about twice the owned set.
- The write never raised the peak above the transport's: it copies each
tensor to the host and drops it, so it adds time, not device memory.
- Host RSS here is windowed to the write (the high-water mark is reset
just before it); the first table's is the process-lifetime `ru_maxrss`.
- 2026-10-29: merges [2/3]'s review round (`46e42e10a74`, rebased onto
#3100's `b9fbb242300`). That brings [1/3]'s docstring and test, plus
[2/3]'s merge-group and strided-shard tests and trimmed docstrings. No
change of its own. Pre-commit passes against main;
`test_distributed_writer.py`, `test_quant_aware_conversion.py` and
`test_meta_dcp_load.py` pass (149).
- 2026-10-29, later (`2470bc4a4f7`, test only):
`test_the_index_goes_in_place_only_after_every_shard` pins the commit
order. Rank 1 fails to rename its shard into place after every file is
written, and the index must stay as it was: absent in an empty
directory, byte for byte over an existing checkpoint. So an index in the
directory never names missing shards. With the shard and index commits
swapped, both cases fail, and every other writer test stays green. The
writer's code is unchanged since `239260c7d8d`.
`test_distributed_writer.py` passes (44), and pre-commit passes against
main.
- 2026-10-29 (meenchen's review bot): why the writer is neither a DCP
writer nor main's streaming writer, and why sharing its file writing
with the latter is deferred, is now in the description above, moved from
the review reply. Description only.
- Review (meenchen's review bot), 2026-10-29, second round
(`285c30bf08e`): once every rank has committed, rank 0 removes an
earlier checkpoint's `model-NNNNN-of-NNNNN.safetensors` files the new
index does not name, and a `model.safetensors`, which transformers loads
ahead of the index. The replacement test runs over an earlier one-shard,
three-shard and single-file checkpoint, with a `config.json` and an
off-index weight file that must stay; the three-shard and single-file
cases fail without the removal. The failure tests' existing checkpoint
gained a `model.safetensors`, so a removal on a failure path fails them
(checked by running it in the failure handler). The docstring states the
commit's limit: a rename failing partway through the final commit can
leave a checkpoint already there mixed; it is documented rather than
fixed, for the reasons in the review thread.
`test_distributed_writer.py` passes (46), and pre-commit passes against
main. Source lines: 272.
- 2026-10-29, later: merges [2/3]'s second review round (`055b57bd7cf`):
mixed-device shards and whole-tensor disagreements refused on every
rank, each transfer round's buffers freed when it ends. No change of its
own (`1feacd040c9`). The writer, conversion and meta-DCP tests pass
(158), and pre-commit passes against main.
- Review fix, 2026-10-29 (`03ae6f5d5ff`, meenchen's review bot): a
reverse conversion that fails with an exception carrying no message now
counts as a failure. Before, `RuntimeError()` read as success, so rank 0
committed hub names while rank 1 kept in-memory ones (reproduced on two
gloo ranks with tiny Mixtral). The one-rank-failure test gains that
case, which fails without the fix. `test_distributed_writer.py` passes
(52), and pre-commit passes against main.
- GPU run, 2026-10-29, ran at `1feacd040c9` (the same code as the
current head). GB200, torch 2.11.0a0 (NGC 26.02), transformers 5.9.0:
| `tests/gpu/torch/export/test_distributed_writer_gpu.py` (2 tests) | 1
GPU | 2 GPUs | 4 GPUs |
|---|---|---|---|
| Result | 2 skipped | 2 passed, 20.3 s | 2 passed, 22.5 s |
- Review (meenchen's review bot), 2026-10-29, third round
(`d9bf5aafbbb`, the user's call): the writer refuses, on every rank and
before writing anything, a directory that already holds a checkpoint
(`model.safetensors`, its index, or `model-NNNNN-of-NNNNN.safetensors`),
so a commit failure can no longer mix one. The refusal is decided in the
first collective, so one rank's view of the directory stops them all.
This replaces the documented commit limit and the stale-file cleanup of
`285c30bf08e`, which is removed with its tests; the staged `.tmp` writes
stay. A two-rank test checks the refusal for a one-shard, three-shard
and single-file earlier checkpoint, each left byte for byte, and fails
without the refusal. Another exports beside a `config.json` and an
auxiliary weights file, and fails if the pattern also matches the
auxiliary file. `test_distributed_writer.py` passes (49), and pre-commit
passes against main. Source lines: 270.
- 2026-10-29, later: merges [2/3] rebased onto main `2b900c8fa41` with
its third review round (`b73c052e45f`): every rank initializes NCCL
before a transfer only some ranks take part in, with a fresh-group GPU
test, and the transport's memory bound names what a rank sends. No
change of its own (`8b6f3a275f7`). The writer and conversion tests pass
(128), and pre-commit passes against main.
- GPU run, 2026-10-29, at `8b6f3a275f7`. GB200, torch 2.11.0a0 (NGC
26.02), CUDA 13.1, NCCL 2.29.2, transformers 5.9.0:
| `tests/gpu/torch/export/test_distributed_writer_gpu.py` | 2 GPUs | 4
GPUs |
|---|---|---|
| Result | 2 passed, 1 skipped (the fresh-group test needs 3 or more),
20.4 s | 3 passed, 46.1 s |
- 2026-10-29, later: merges [2/3]'s docstring note that owners are
weighed by bytes (`764036cd64e`). No change of its own (`2a65d9fa364`);
pre-commit passes against main.
- Rebased on 2026-10-29 onto #3101's squash `4bef85279e4` after it
merged, with this PR's own diff unchanged. The history is now linear:
one commit for the pieces as they stood when the split was made, then
this PR's four commits since. #3100's and #3101's pre-squash commits are
dropped. Pre-commit passes against main; the writer and conversion tests
pass (128).
- Claude review, 2026-10-29 (approved, two suggestions; `55b62f6fdf4`,
docstring and message only): `_write_even_shards`' docstring says that a
failure during the final renames leaves the shards already renamed,
which a retry is refused over until they are removed, and the refusal's
message names that cause. It also says the caller drops tied weights by
name. Pre-commit passes against main; `test_distributed_writer.py`
passes (49).
- Rebased on 2026-10-30 onto main `99c3834f62c` (after #3099 and #2750
merged, and #2737), own diff unchanged and no conflicts: main's new
commits touch none of this PR's files. Pre-commit passes against main.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ Additive; nothing calls these
yet.
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A
- Did you write any new necessary tests?: ✅
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A. The no-gather export's entry lands with #2752.
- Did you get Claude approval on this PR?: ❌ Not yet.
### Additional Information
Part 15 of 21 of expert-parallel PTQ for Hugging Face MoE models; its
[1/3] #3100 and [2/3] #3101 have merged.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added distributed checkpoint export in Hugging Face safetensors
format, writing tensors across ranks without gathering the full model on
every rank.
* Exported files are balanced by size, with oversized tensors placed in
their own files and an index generated for the shards.
* Export supports eligible sharded tensors and reverses supported
quantization-related weight conversions.
* **Bug Fixes**
* Exports detect incompatible shards, tensor-name collisions, and write
failures across ranks to help prevent incomplete or inconsistent
checkpoints.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Note
[1/3] of #2751's split (2026-10-29), at its review's request: [1/3] this PR (the reverse conversion's hook), [2/3] #3101, the ownership and transport of the distributed checkpoint writer (stacked on this PR), and [3/3] #2751, the reverse conversion and shard writing (stacked on [2/3]). Merge in that order.
Base:
main.What does this PR do?
Type of change: New feature (part of the no-gather distributed export)
revert_weight_conversion_quant_awarerefuses a merge rule that matches no key of the state dict, so that a later rename cannot leave a checkpoint that mixes the two namings.The distributed checkpoint writer reverts each rank's own share of the model. A rank can hold none of a merge rule's keys while another rank holds them all, so the conversion now takes an
unmatched_mergeslist:Usage
Internal:
revert_weight_conversion_quant_aware(model, state_dict, unmatched_merges=[]), called by the distributed writer ([3/3]).Testing
test_quant_aware_conversion.pygainstest_radio_merge_that_matches_no_keys_is_recorded_for_one_ranks_sharenext to the existing raise case: the refusal is recorded instead of raised, and a matching merge rule after it still applies, its fused output checked, with the unrelated key kept.b9fbb242300):apply_reverse_rules's docstring says whatunmatched_mergesdoes. The recorded-refusal test now has a matching merge rule after the unmatched one and checks its fused output; it fails if the reverse stops after recording the refusal. A merge group split across ranks cannot reach this hook: [2/3] keeps each group on one owner, and [3/3] runs the reverse inside a collective that carries any rank's refusal to every rank. Pre-commit passes against main; the conversion tests pass (79 on transformers 5.18; on 5.5.4 the scoped-merge cases skip, as before).Before your PR is "Ready for review"
CONTRIBUTING.md: N/AAdditional Information
Split out of #2751, part of the expert-parallel PTQ series for Hugging Face MoE models.
Summary by CodeRabbit