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[OMNIML-6054] [1/2] Move fsdp2_wrap out of distributed.py into fsdp2_utils - #3140
[1/2] of a split of #2779, as review asked to keep each PR under the source-line budget. This PR moves fsdp2_wrap out of modelopt.torch.utils.distributed; [2/2], #2779 ("Route load_pretrained by the distribution plan"), stacks on it and routes load_pretrained by the distribution plan.
Merge order: this PR first, then #2779, which targets this PR's branch until then.
What does this PR do?
Type of change: Refactor (no behavior change)
fsdp2_wrap moves from modelopt.torch.utils.distributed into a module of its own, modelopt.torch.utils.fsdp2_utils, which imports no Hugging Face package. The loaders (modelopt.torch.utils.plugins.model_load_utils) import it from there and wrap with it in parallel_load_and_prepare_fsdp2, as before; #2779 routes load_pretrained to that load.
The function bodies are unchanged. The dtype helpers it uses (_off_dtype_params, _move_to_fsdp_device), which nothing else calls, move with it, and their tests move into tests/unit/torch/utils/test_fsdp2_utils.py.
It imports the layer detection (LayerActivationCollector) where it uses it, as before.
modelopt.torch.utils.distributed.fsdp2_wrap forwards to it, so callers of the old path keep working, with the same dependencies as before: a model whose decoder layers are registered with LayerActivationCollector wraps without transformers or huggingface_hub.
Usage
# Every path reaches the same function; only the loaders' own needs the hf extra.frommodelopt.torch.utils.fsdp2_utilsimportfsdp2_wrapfrommodelopt.torch.utils.distributedimportfsdp2_wrapfrommodelopt.torch.utils.plugins.model_load_utilsimportfsdp2_wrap
Testing
tests/unit/torch/utils/test_distributed.py::test_fsdp2_wrap_needs_no_hugging_face (two gloo ranks): with transformers, huggingface_hub and accelerate unimportable and the loaders' module evicted, the old path wraps a plain PyTorch decoder registered with register_decoder_layer_support for real, on a CPU mesh; it fails when the old path goes through the loaders.
The _off_dtype_params tests, moved with the helper into tests/unit/torch/utils/test_fsdp2_utils.py.
The GPU tests that wrap through the old path (tests/gpu/torch/utils/test_distributed.py, tests/gpu/torch/export/test_export_carry_over.py) and the FSDP2 load's own (tests/gpu/torch/utils/test_model_load_utils.py) run in CI's GPU workflow.
2026-10-30: pre-commit, mypy included, passes against main; tests/unit/torch/utils and the hf_ptq example tests pass on CPU with transformers 5.18 and 5.5.4. Source insertions: 54.
Review fixes, 2026-10-30 (Claude): the old path names the hf extra when it cannot import the loaders; the dtype helpers and their tests move with fsdp2_wrap. Pre-commit, mypy included, passes against main; tests/unit/torch/utils and the hf_ptq example tests pass on CPU with transformers 5.18 and 5.5.4. Source insertions: 114.
Review suggestion, 2026-10-30 (Claude's approving review): the old path names the hf extra only when transformers or accelerate is the missing module, and lets any other import error through as it is; the test has a row for each, and the second fails without the change. Pre-commit passes against main; tests/unit/torch/utils and the hf_ptq example tests pass on CPU with transformers 5.18 and 5.5.4. Source insertions: 116.
Review suggestion, 2026-10-30 (CodeRabbit): the old path also names the hf extra when huggingface_hub is the missing module, which model_load_utils imports at module scope before transformers; the test's new row fails without the change. Pre-commit, mypy included, passes against main; tests/unit/torch/utils and the hf_ptq example tests pass on CPU with transformers 5.18 and 5.5.4. Source insertions: 117.
Review suggestion, 2026-10-30 (Claude's approving review at 26bf59ace7c): the old path matches the missing module's exact name, so a module missing from an installed transformers reaches the caller as it is rather than as a missing extra; the test's new row (transformers.conversion_mapping) fails without the change. Pre-commit, mypy included, passes against main; tests/unit/torch/utils passes on CPU with transformers 5.18. Source insertions: 117.
Review fix, 2026-10-30 (CodeRabbit): the transformers.conversion_mapping row is skipped where transformers is not installed, as in the partial-install torch unit lane, where the loaders' import of transformers itself fails first and rightly names the extra; reproduced in such an environment, where tests/unit/torch/utils now passes (339 passed, 35 skipped). Pre-commit, mypy included, passes against main; tests/unit/torch/utils passes on CPU with transformers 5.18 and 5.5.4. Source insertions: 117.
Review fix, 2026-10-30 (cjluo-nv): fsdp2_wrap and its dtype helpers live in modelopt.torch.utils.fsdp2_utils, which imports no Hugging Face package, rather than in the loaders' module, so the old path wraps without the hf extra again, as on main; the loaders import it from there. The old path's ImportError for a missing extra and its tests are gone, and the changelog no longer lists the move. Pre-commit, mypy included, passes against main; tests/unit/torch/utils passes on CPU with transformers 5.18 and 5.5.4, and without transformers installed (342 passed, 33 skipped). Source insertions: 138.
Review nudge, 2026-10-30 (cjluo-nv): the old path's mock-only forwarding test is gone; the two-rank test that wraps a registered decoder for real through the old path, without Hugging Face, and the GPU tests that wrap through it with cpu_offload cover it. The generated release notes below now describe the refactor. Pre-commit, mypy included, passes against main; tests/unit/torch/utils passes on CPU with transformers 5.18 (548 passed) and without transformers (341 passed, 33 skipped).
Rebased on 2026-10-30 onto main 059588b89a5, without conflicts; this PR's own diff is unchanged. Pre-commit, mypy included, passes against main; tests/unit/torch/utils passes on CPU with transformers 5.18 (554 passed) and without transformers (341 passed, 35 skipped).
Rebased on 2026-10-30 onto main d464d30b120, without conflicts; this PR's own diff is unchanged. Pre-commit, mypy included, passes against main; tests/unit/torch/utils passes on CPU with transformers 5.18 (556 passed) and without transformers (342 passed, 36 skipped).
Before your PR is "Ready for review"
Is this change backward compatible?: ✅ Every import path keeps working, with the same dependencies as before.
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?: N/A, no user-facing change.
Did you get Claude approval on this PR?: ✅ Approved at a4bdab004de, before this change.
Additional Information
Split from #2779 (expert-parallel PTQ for Hugging Face MoE models). The move was reviewed there, into the loaders' module; review here asked to keep the old path free of the hf extra, so it moved into a module of its own instead.
Summary by CodeRabbit
Refactor
FSDP2 wrapping (fsdp2_wrap) and its dtype helpers move to modelopt.torch.utils.fsdp2_utils, which needs no Hugging Face package. The previous import path, modelopt.torch.utils.distributed.fsdp2_wrap, forwards to it and still works without the hf extra.
No behavior change: mixed-precision policies, CPU offload and the handling of mixed parameter dtypes work as before.
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Inline comments:
Review comments at @tests/unit/torch/utils/test_distributed.py:
- Line 177: Update the `transformers.conversion_mapping` case in the test
parameterization to skip when the `transformers` package is absent, while
preserving the existing expected result when the package is installed.
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modelopt/torch/utils/distributed.py
tests/unit/torch/utils/test_distributed.py
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Walkthrough
The FSDP2 wrapping implementation moves to model_load_utils. The distributed utility retains fsdp2_wrap as a delegating wrapper. The changelog documents the import paths and the hf extra requirement.
The plugin module implements FSDP2 sharding and handles parameters with differing dtypes. It moves ignored parameters when it can infer a device from a DTensor mesh. Tests cover off-dtype parameter selection based on total element counts.
Distributed utility delegation and validation modelopt/torch/utils/distributed.py, tests/unit/torch/utils/test_distributed.py, CHANGELOG.rst
dist.fsdp2_wrap delegates to the plugin implementation. If transformers, accelerate, or huggingface_hub is missing, the wrapper raises an ImportError with hf extra installation guidance. Tests check delegation and import errors. The changelog documents the import paths and extra requirement.
The FSDP2 wrapping code moves to the model-loading plugin and the old import path still forwards to it. One new unit-test case would fail in the torch-only test session, which does not install the Hugging Face extra. Guard that case before merging; runtime behavior is not affected.
Pre-merge checks: 5 · 1
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PASS: The pull request introduces none of the listed security anti-patterns. Added Python lines contain no unsafe torch.load, numpy.load, hardcoded trust_remote_code=True, eval/exec calls, or # nosec …
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The title names the main change: moving fsdp2_wrap out of distributed.py into the fsdp2 utilities module. The changeset moves fsdp2_wrap and its dtype helpers from modelopt.torch.utils.distributed to …
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PASS: The pull request introduces none of the listed security anti-patterns. Added Python lines contain no unsafe torch.load, numpy.load, hardcoded trust_remote_code=True, eval/exec calls, or # nosec comments. No pyproject.toml or requirements.txt file changed, so no new dependency licensing issue is present.
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The title names the main change: moving fsdp2_wrap out of distributed.py into the fsdp2 utilities module. The changeset moves fsdp2_wrap and its dtype helpers from modelopt.torch.utils.distributed to modelopt.torch.utils.plugins.model_load_utils, and leaves a forwarding wrapper behind. The title says fsdp2_utils, but the code uses model_load_utils. The title is a close match in intent. Its module name is inaccurate.
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[IMPORTANT Compatibility] The old path now fails with a bare ModuleNotFoundError: No module named 'transformers', with nothing tying it to the hf extra.
What:model_load_utils imports transformers/accelerate at module scope, so this lazy import raises an unguarded import error on a partial install. Before the move, the chain was transformers-free: layerwise_calib → opt.searcher + utils.{distributed,dataset_utils,network} + quantization.utils.core_utils, none of which import transformers or accelerate at module scope (dataset_utils is star-imported from modelopt/torch/utils/__init__.py, so it cannot). fsdp2_wrap genuinely worked without the hf extra, and get_decoder_layers is duck-typed, so a non-HF model with an HF-like layout was a working case.
Why it matters:fsdp2_wrap is in distributed.__all__ — a core, documented entry point whose own module still imports fine. A user on a partial install gets an error naming transformers, not the extra they need, and no hint that the cause is a relocation. This module is also exactly where the repo is most careful about this dependency edge: hf_checkpoint_utils.py:661 and unified_export_hf.py:1801 both call out "that module imports transformers and accelerate at module scope" and deliberately route around it (the second one with a message naming load_pretrained). The changelog entry documents the break, which covers the policy; this is about the failure mode being actionable.
Suggested fix — translate the import error at the shim boundary:
deffsdp2_wrap(model, shard_root=True, mp_policy=None, cpu_offload: bool=False):
"""Forward to :func:`~modelopt.torch.utils.plugins.model_load_utils.fsdp2_wrap`. It moved there with the FSDP2 load that wraps with it, so this old path needs the ``hf`` extra. """try:
frommodelopt.torch.utils.plugins.model_load_utilsimportfsdp2_wrapaswrapexceptImportErrorasexc:
raiseImportError(
"fsdp2_wrap moved to modelopt.torch.utils.plugins.model_load_utils, which imports ""transformers and accelerate at module scope; install the 'hf' extra ""(pip install nvidia-modelopt[hf]) to reach it from this path."
) fromexcreturnwrap(model, shard_root=shard_root, mp_policy=mp_policy, cpu_offload=cpu_offload)
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Taken in 155392b. The old path now catches the ImportError from model_load_utils and raises one naming the move and the extra it needs (pip install 'nvidia-modelopt[hf]'). test_fsdp2_wrap_names_the_extra_its_old_path_needs simulates the missing module by putting None in sys.modules. Before the change it failed with the bare "import ... halted" error.
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[SUGGESTION] The move left both private helpers behind, so they now have no caller in their own module.
After this PR, _off_dtype_params and _move_to_fsdp_device (distributed.py:716, :747) are referenced from nowhere in distributed.py — their only production caller is the fsdp2_wrap that just moved here, reaching across the package boundary for underscore-prefixed names. The PR body notes this is deliberate ("they stay"), and it does keep the diff smaller, so this is non-blocking — but the split leaves the private/public boundary pointing the wrong way, and the next reader of distributed.py sees two helpers with no local use.
Moving both (plus the three test_off_dtype_params_* cases in tests/unit/torch/utils/test_distributed.py, which are their only other callers) alongside fsdp2_wrap would make the relocation self-contained and keep them private to the module that uses them. It also fixes the docstring xref on line 517: :func:~modelopt.torch.utils.distributed._off_dtype_params`` points at a private symbol autodoc does not emit, so it will not resolve as a link.
If you prefer to keep them where they are for diff size, consider stating that in a one-line comment at the import so the asymmetry reads as intentional rather than as leftover.
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Taken in 6c44b40. _off_dtype_params and _move_to_fsdp_device moved with fsdp2_wrap into model_load_utils, their only caller. They use that module's own warn_rank_0 instead of distributed's lazy import. Their three test_off_dtype_params_* tests moved unchanged into tests/unit/torch/utils/test_model_load_utils.py, and fsdp2_wrap's docstring now names the helper in its own module. This PR now adds 114 source lines.
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Claude review — [1/2] Move fsdp2_wrap into model_load_utils
Scope: full review (trigger comment carried no scoping instructions). All 4 changed files reviewed: modelopt/torch/utils/distributed.py, modelopt/torch/utils/plugins/model_load_utils.py, tests/unit/torch/utils/test_distributed.py, CHANGELOG.rst.
Findings
Severity
Count
CRITICAL
0
IMPORTANT
1
SUGGESTION
1
[IMPORTANT Compatibility] — distributed.py:779-785: the forwarding shim raises a bare ModuleNotFoundError: No module named 'transformers' on a partial install, with nothing naming the hf extra or the relocation. I traced the old chain to confirm the regression is real rather than theoretical — layerwise_calib → opt.searcher + utils.{distributed,dataset_utils,network} + quantization.utils.core_utils has no module-scope transformers or accelerate import, and dataset_utilscannot have one since modelopt/torch/utils/__init__.py star-imports it. So fsdp2_wrap did work without the hf extra. The changelog entry covers the policy decision; the ask is only that the failure be actionable, which is a 5-line try/except at the shim. Notably this is the same dependency edge hf_checkpoint_utils.py:661 and unified_export_hf.py:1801 both comment on and route around.
[SUGGESTION] — model_load_utils.py:52-53: _off_dtype_params and _move_to_fsdp_device are now called from nowhere in distributed.py; their only production caller is the relocated fsdp2_wrap, reaching across the package for private names. Non-blocking (the PR body says this is deliberate for diff size), but it also leaves a docstring xref at line 517 pointing at a symbol autodoc will not emit.
Verified clean
The refactor itself checks out as a no-op, which is what the PR claims:
Function body is byte-identical across the move — every fsdp_kwargs branch, the ignored_params path, and the config.architectures snapshot/restore survive unchanged. Only the blank line before return decoder_layers differs.
No new import cycle.model_load_utils → distributed at module scope, distributed → model_load_utils lazily inside the function body, which is safe. Nothing under modelopt/torch/quantization/ imports model_load_utils, so the rewritten comment ("Importing the quantization package registers every mode and plugin") is accurate in the new location — the old "circular" rationale genuinely no longer applies.
No stranded imports.CPUOffloadPolicy and fully_shard now appear in distributed.py only inside docstrings/comments, and Any is still used at 15+ sites, so nothing is left dangling.
Public surface intact.fsdp2_wrap stays in distributed.__all__; model_load_utils has no __all__ and is not star-imported by plugins/__init__.py, so the new definition needs no registration. Nothing in docs/source/reference/ references either path.
Call sites. The only in-tree production caller is parallel_load_and_prepare_fsdp2 (model_load_utils.py:891), now a same-module call. GPU tests in tests/gpu/torch/utils/test_distributed.py and tests/gpu/torch/export/test_export_carry_over.py go through the old path and so exercise the shim.
The unit test is sound.monkeypatch.setattr resolves because the shim imports inside the function body rather than binding at module scope, and it would fail loudly if the target symbol were ever removed — so it pins both the forwarding and the symbol's existence. Fast, no GPU, correctly placed in tests/unit.
Changelog is filed under Backward Breaking Changes, which is the right section given the new extra requirement, and reads for external users.
No mode registration, config schema, modelopt_state, or export path is touched, so none of those concerns apply.
Risk
Low. Pure relocation with a compat shim, no behavior change on the paths that matter, and the single IMPORTANT finding is about error-message quality on a partial install rather than a functional break. The [1/2] split and merge order are stated clearly, and at 54 source insertions it is well under the review budget.
I did not run the GPU tests (no GPU in this environment), so the FSDP2 wrap behavior itself rests on the body being unchanged plus CI's GPU workflow.
❌ Patch coverage is 94.44444% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 80.06%. Comparing base (d464d30) to head (b4445d1).
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[SUGGESTION] The shim attributes everyImportError to a missing hf extra, so a user who already has it installed gets a misleading instruction.
model_load_utils imports a fairly version-sensitive slice of the transformers surface at module scope — transformers.conversion_mapping.get_model_conversion_mapping and transformers.core_model_loading.{WeightConverter, dot_natural_key, rename_source_key}. If any of those moves in a future transformers release, the failure is an ImportError with the extra already present, and this message sends the user to pip install 'nvidia-modelopt[hf]', which changes nothing.
Impact is limited — from error keeps the real cause in the traceback, so it costs a reader one extra frame rather than hiding the problem. Worth one clause to point them at it:
Suggested change
exceptImportErroraserror:
raiseImportError(
"fsdp2_wrap moved to modelopt.torch.utils.plugins.model_load_utils, which needs the hf "
"extra (pip install 'nvidia-modelopt[hf]') to import."
) fromerror
exceptImportErroraserror:
raiseImportError(
"fsdp2_wrap moved to modelopt.torch.utils.plugins.model_load_utils, which needs the hf "
"extra (pip install 'nvidia-modelopt[hf]') to import. If that extra is already "
"installed, the chained error above gives the real cause."
) fromerror
Non-blocking; the current message is already a clear improvement over the bare ModuleNotFoundError: No module named 'transformers' it replaces.
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Taken in 48891f6, by narrowing the catch rather than widening the message. The old path now names the hf extra only for a ModuleNotFoundError whose missing module is transformers or accelerate (or a submodule of either). Any other import error, from inside model_load_utils or from a dependency that moved, reaches the caller unchanged. test_fsdp2_wrap_names_the_extra_its_old_path_needs has a row for each. The row for an unrelated import error failed before the change, which turned it into the hf message.
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Claude review — [1/2] Move fsdp2_wrap into model_load_utils (commit 6c44b40)
Scope: full review (trigger comment carried no scoping instructions). All 5 changed files reviewed: modelopt/torch/utils/distributed.py, modelopt/torch/utils/plugins/model_load_utils.py, tests/unit/torch/utils/test_distributed.py, tests/unit/torch/utils/test_model_load_utils.py, CHANGELOG.rst.
Findings
Severity
Count
CRITICAL
0
IMPORTANT
0
SUGGESTION
1
[SUGGESTION] — distributed.py:723-727: the shim attributes every ImportError to a missing hf extra, so a user who already has it (hitting, say, a transformers rename of core_model_loading.WeightConverter) is told to install something they have. from error keeps the real cause one frame away, so this costs a reader a moment rather than hiding anything.
Both findings from the previous round are resolved
IMPORTANT, unactionable failure on a partial install — fixed. The shim now raises an ImportError naming both the relocation and pip install 'nvidia-modelopt[hf]', chained from the original. test_fsdp2_wrap_names_the_extra_its_old_path_needs pins it, and the monkeypatch.setitem(sys.modules, ..., None) technique is sound — a None entry makes the import machinery raise ImportError, so the test exercises the real except branch rather than a stub.
SUGGESTION, dtype helpers stranded — fixed. _off_dtype_params and _move_to_fsdp_device moved with fsdp2_wrap, so nothing reaches across the package for private names any more, and the _off_dtype_params xref in the fsdp2_wrap docstring now resolves within its own module.
Verified clean
The move is a no-op. I compared all three relocated functions line by line against origin/main. The only differences are the two deleted lazy from modelopt.torch.utils.logging import warn_rank_0 imports (now satisfied by model_load_utils's existing module-scope import of the same symbol), the reworded lazy-import comment, and one dropped blank line. Every fsdp_kwargs branch, the ignored_params path, the config.architectures snapshot/restore, and both warning f-strings are unchanged.
The warn_rank_0 lazy import was safe to drop. The old comment cited a cycle (logging imports distributed), which does not apply in the new home: model_load_utils to logging to distributed is acyclic, and model_load_utils already imported print_rank_0, warn_rank_0 at module scope before this PR, so no new import edge is added.
No new cycle from the shim.model_load_utils imports distributed at module scope; distributed imports model_load_utils only inside the function body. The reverse direction is unreachable at import time since model_load_utils never calls fsdp2_wrap at module scope.
The rewritten lazy-import rationale is accurate. Nothing under modelopt/torch/quantization/ imports model_load_utils, so the layerwise_calib import is no longer breaking a cycle — keeping it lazy for mode/plugin-registration cost, as the new comment says, is the correct reading.
No stranded or missing imports.CPUOffloadPolicy/fully_shard are gone from distributed.py's import line and now appear there only in prose; FSDPModule (line 713) and DTensor (lines 845-846) are still used, and Any still has 15 uses. On the other side Any is already imported in model_load_utils.
Plugin laziness preserved.plugins/__init__.py does not import model_load_utils, so importing modelopt.torch.utils.plugins still does not pull in transformers; only the explicit submodule import does. fsdp2_wrap stays in distributed.__all__, and modelopt/torch/utils/__init__.py does not star-import distributed, so no public surface shifted.
Call sites. The sole in-tree production caller, parallel_load_and_prepare_fsdp2 (model_load_utils.py:945), is now a same-module call and does not round-trip through the shim. GPU tests in tests/gpu/torch/utils/test_distributed.py and tests/gpu/torch/export/test_export_carry_over.py still go through the old path, so CI covers the shim on real hardware.
Test placement.test_model_load_utils.py already guards with pytest.importorskip on accelerate and transformers, so the moved _off_dtype_params tests skip cleanly rather than erroring at collection on a bare install — the right trade now that the helper lives behind the hf extra. Both new test_distributed.py tests are CPU-only and fast, and sys is now used where it was added.
Changelog is correctly filed under Backward Breaking Changes (the new extra requirement is the breaking part, not the import path) and reads for external users in two sentences.
No mode registration, config schema, modelopt_state, or export path is touched, so none of those concerns apply. At 54 source insertions the [1/2] slice is well inside the budget, and the merge order is stated.
Risk
Low. A pure relocation with a compat shim, verified mechanically as behavior-preserving, plus a genuine usability fix for partial installs. No blocking issues.
I could not execute pytest in this environment (approval-gated sandbox), so the test verification above is static — correctness of the FSDP2 wrap itself rests on the body being unchanged plus CI's GPU workflow, which the author reports passing locally.
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Inline comments:
Review comments at @modelopt/torch/utils/distributed.py:
- Around line 716-728: Update the ImportError handler in the fsdp2_wrap
compatibility wrapper to catch only ModuleNotFoundError; re-raise errors unless
error.name identifies huggingface_hub, transformers, or
modelopt.torch.utils.plugins.model_load_utils, and retain the existing hf-extra
message for those missing-module cases.
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Reviewing files that changed from the base of the PR and between cad3720 and 6c44b40.
Files selected for processing: 4
modelopt/torch/utils/distributed.py
modelopt/torch/utils/plugins/model_load_utils.py
tests/unit/torch/utils/test_distributed.py
tests/unit/torch/utils/test_model_load_utils.py
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Claude review — [1/2] Move fsdp2_wrap into model_load_utils (commit 48891f6)
Scope: full review (trigger comment carried no scoping instructions). All 5 changed files read: modelopt/torch/utils/distributed.py, modelopt/torch/utils/plugins/model_load_utils.py, tests/unit/torch/utils/test_distributed.py, tests/unit/torch/utils/test_model_load_utils.py, CHANGELOG.rst.
Findings
Severity
Count
CRITICAL
0
IMPORTANT
0
SUGGESTION
0
The one open finding from the previous round is resolved
distributed.py:723-727 — the shim no longer attributes every ImportError to a missing hf extra. It now catches ModuleNotFoundError only, tests error.name's top-level package against ("transformers", "accelerate"), and re-raises anything else untouched. Checks I ran on the new branch:
Submodule names resolve correctly..split(".")[0] means transformers.conversion_mapping and transformers.core_model_loading — the two newest-API imports in model_load_utils and the likeliest to break on a version skew — map to transformers and get the actionable message, while a similarly-prefixed third-party package (transformers_stream_generator) does not, since the comparison is on the whole first segment.
The message stays correct on a version skew, not just a bare install.pyproject.toml:90 pins transformers>=5.5,<5.19 in the hf extra, so a user who has transformers but at a version without conversion_mapping is told to run pip install 'nvidia-modelopt[hf]', and that command actually resolves their problem (up- or down-grade into the pinned window). That was the substance of the earlier objection — the advice is never wrong for the errors it now claims.
error.name of None is handled — (error.name or "") falls into the re-raise branch rather than matching, which is the safe default.
Non-ModuleNotFoundErrorImportErrors pass through, so a transformers symbol rename (e.g. core_model_loading.WeightConverter) now surfaces as itself instead of a bogus install instruction. from error still chains the original in the one branch that rewrites.
The new parametrized test covers both arms, and the second one fails without the change: with sys.modules["modelopt.torch.utils.plugins.model_load_utils"] = None, the import machinery raises ModuleNotFoundError(name="modelopt.torch.utils.plugins.model_load_utils"), whose first segment is modelopt, so only the re-raise branch lets "halted; None in sys.modules" reach the caller. The delitem(..., raising=False) + setitem pairing unwinds in reverse at teardown, so neither row leaves sys.modules polluted for later tests, and both fail fast (the transformers row stops at model_load_utils's import line; the other never executes the module at all).
accelerate in the tuple is defensive rather than live — model_load_utils imports it inside a function body (line 236), not at module scope — but it is harmless and correct if that ever changes.
Re-verified clean this round
The move is still a no-op. All three relocated functions are line-for-line identical to origin/main apart from the two dropped lazy warn_rank_0 imports (now covered by model_load_utils's pre-existing module-scope import of the same symbol), the reworded lazy-import comment, and one blank line. Every fsdp_kwargs branch, the ignored_params path, the config.architectures snapshot/restore, and both warning f-strings are unchanged.
No import cycle.model_load_utils imports distributed at module scope; distributed imports model_load_utils only inside the function body, and model_load_utils never calls fsdp2_wrap at import time.
No stranded or unused imports.CPUOffloadPolicy/fully_shard are gone from distributed.py's import line and appear there only in prose; FSDPModule (713), DTensor (847) and Any (15+ sites) are all still live after the helpers left.
Plugin laziness preserved.plugins/__init__.py does not import model_load_utils, so import modelopt.torch.utils.plugins still pulls in no transformers. fsdp2_wrap stays in distributed.__all__.
Call sites. A repo-wide grep finds the sole production caller, parallel_load_and_prepare_fsdp2, now a same-module call that does not round-trip the shim; tests/gpu/torch/utils/test_distributed.py and tests/gpu/torch/export/test_export_carry_over.py still import the old path, so CI exercises the shim on real hardware. No doc or example references either symbol.
Test placement. The moved _off_dtype_params tests land in a file already guarded by pytest.importorskip on transformers/accelerate, which is the right home now that the helper lives behind the hf extra; the two new test_distributed.py tests are CPU-only and fast.
Changelog is filed under Backward Breaking Changes (the new extra requirement is the breaking part) and reads for external users in two sentences.
No mode registration, config schema, modelopt_state, or export path is touched, so none of those concerns apply. At 116 source insertions the [1/2] slice is well inside the budget and the merge order is stated.
Risk: low. A pure relocation behind a compat shim, verified mechanically as behavior-preserving, plus a usability fix for partial installs that no longer over-claims.
Caveat on evidence: I could not execute pytest or a Python interpreter in this environment (approval-gated sandbox), so the test and import-machinery reasoning above is static source analysis, and the FSDP2 wrap behavior itself rests on the body being unchanged plus CI's GPU workflow.
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[SUGGESTION] Matching on the root of error.name rather than the name itself widens the hf-extra message to submodule misses of an installed package. model_load_utils imports transformers.conversion_mapping and transformers.core_model_loading at module scope, and the hf extra admits a range (transformers>=5.5,<5.19 — the PR body reports testing both ends). A user on an in-range version that lacks one of those submodules gets ModuleNotFoundError(name="transformers.conversion_mapping"), whose root is transformers, so the shim tells them to install an extra they already have instead of surfacing the version mismatch. The chained from error keeps the real cause reachable, so this is message quality, not a functional break.
Comparing error.name exactly keeps the "extra is missing" case (a top-level miss always reports the bare package name) and lets every submodule miss through as-is. The existing third parametrize row still re-raises, since modelopt.torch.utils.plugins.model_load_utils is not in the tuple either way.
Minor, and orthogonal: accelerate is only imported lazily inside build_meta_causal_lm (model_load_utils.py:236), never at module scope, so that entry cannot be hit today. Harmless as future-proofing — just noting it in case it was meant to cover a module-scope import that isn't there.
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Taken in a4bdab0. The old path now compares error.name exactly, so only a missing top-level package gets the hf extra message. A module missing from an installed transformers (transformers.conversion_mapping, say) reaches the caller as it is. The test has a row for that case, and it fails without the change.
I kept accelerate in the list. Nothing imports it at module scope today, but an install without it is the same missing extra.
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Claude review — [1/2] Move fsdp2_wrap into model_load_utils (commit 26bf59a)
Scope: full review (trigger comment carried no scoping instructions). All 5 changed files reviewed: modelopt/torch/utils/distributed.py, modelopt/torch/utils/plugins/model_load_utils.py, tests/unit/torch/utils/test_distributed.py, tests/unit/torch/utils/test_model_load_utils.py, CHANGELOG.rst.
Findings
Severity
Count
CRITICAL
0
IMPORTANT
0
SUGGESTION
1
[SUGGESTION] — distributed.py:722-723: the shim matches the root of error.name, so a missing submodule of an installed transformers (e.g. transformers.conversion_mapping, both of whose module-scope imports sit inside the extra's >=5.5,<5.19 range) still advises installing an extra the user has. Comparing error.name exactly covers the missing-extra case — a top-level miss always reports the bare package name — and lets submodule misses through as-is; the existing third parametrize row re-raises either way. Message quality only, with from error keeping the real cause one frame away.
This round's change is correct
The only delta since my previous approval is adding huggingface_hub to the shim's recognized set, per CodeRabbit. Verified: model_load_utils.py:28 does from huggingface_hub import snapshot_download at module scope, before the transformers imports at line 35, so on a bare install huggingface_hub is genuinely the first module to go missing and the old path would otherwise have raised an unactionable ModuleNotFoundError. huggingface_hub>=1.5.0 is in the [hf] extra (pyproject.toml:84), so the message is accurate. The new ids=["huggingface_hub"] row pins it, and monkeypatch.setitem(sys.modules, ..., None) exercises the real except branch — a None entry makes the import machinery raise ModuleNotFoundError with name set, not a stub.
Also verified this round
The three-row parametrize covers all branches. Rows 1-2 (transformers, huggingface_hub) take the hf-extra path; row 3 sets model_load_utils itself to None, whose error.name root is modelopt, so it re-raises and the test asserts on the raw halted; None in sys.modules text. The delitem(..., raising=False) before each setitem forces re-execution of the module body rather than hitting a cached entry, and monkeypatch restores the real module at teardown, so no cross-test pollution.
Narrowing to ModuleNotFoundError is the right catch. An ImportError: cannot import name ... from a version skew (e.g. core_model_loading.WeightConverter being renamed) now propagates untouched instead of being misattributed, which closes the previous round's SUGGESTION.
Helper relocation is complete.grep confirms _off_dtype_params/_move_to_fsdp_device have no remaining reference in distributed.py or anywhere outside model_load_utils.py and its own test file, so the :func:_off_dtype_params`` xref in the fsdp2_wrap docstring resolves in-module.
Function bodies remain byte-equivalent to origin/main across all three moved functions, modulo the two dropped lazy warn_rank_0 imports (satisfied by model_load_utils's existing module-scope import), the reworded lazy-import comment, and one blank line. Every fsdp_kwargs branch, the ignored_params path, the config.architectures snapshot/restore, and both warning f-strings are unchanged.
No new import cycle and no plugin-laziness regression.model_load_utils → distributed at module scope; distributed → model_load_utils only inside the function body, and model_load_utils never calls fsdp2_wrap at module scope, so the reverse edge is unreachable at import time. plugins/__init__.py does not import model_load_utils, so importing modelopt.torch.utils.plugins still pulls in no HF dependency.
Call sites. The sole in-tree production caller, parallel_load_and_prepare_fsdp2 (model_load_utils.py:945), is now a same-module call and bypasses the shim entirely. GPU tests in tests/gpu/torch/utils/test_distributed.py and tests/gpu/torch/export/test_export_carry_over.py still import from the old path, so CI exercises the shim's success route on real hardware. fsdp2_wrap stays in distributed.__all__.
Stranded imports.CPUOffloadPolicy/fully_shard are gone from distributed.py's import line and appear there only in prose; FSDPModule and DTensor are still used; Any has many remaining uses on both sides.
Test placement and cost. The moved _off_dtype_params tests land in a file already guarded by pytest.importorskip on accelerate/transformers, which is right now that the helper lives behind the extra; they skip cleanly on a bare install rather than erroring at collection. Both test_distributed.py additions are CPU-only and sub-second — no process spawning, no model load — so the 20-min linux budget is unaffected.
Changelog is one sentence under Backward Breaking Changes (the new extra requirement is the breaking part), written for external users, no internal detail.
No mode registration, config schema, modelopt_state, or export path is touched, so none of those concerns apply. At 117 source insertions the [1/2] slice is well inside the ~500-line budget, and the merge order (this PR, then #2779) is stated.
Risk
Low. A pure relocation with a compat shim, mechanically verified as behavior-preserving, plus a genuine usability fix for partial installs that is now complete across the module's import order. The one SUGGESTION is non-blocking.
I could not execute pytest in this environment (approval-gated sandbox), so test verification is static — FSDP2 wrap correctness rests on the unchanged bodies plus CI's GPU workflow, which the author reports passing locally.
shengliangxu
changed the title
[OMNIML-6054] [1/2] Move fsdp2_wrap into model_load_utils
[OMNIML-6054] [1/2] Move fsdp2_wrap out of distributed.py into fsdp2_utils
Oct 30, 2026
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Claude review passed — no blocking issues found. LGTM
Scope: full review (the trigger comment carried no scoping instructions). 5 changed files, all reviewed: modelopt/torch/utils/distributed.py, modelopt/torch/utils/fsdp2_utils.py (new), modelopt/torch/utils/plugins/model_load_utils.py, tests/unit/torch/utils/test_distributed.py, tests/unit/torch/utils/test_fsdp2_utils.py (new).
Findings
CRITICAL: 0 — IMPORTANT: 0 — SUGGESTION: 0
What I verified
This is a pure code move, and I checked that it is actually pure:
Function bodies identical._off_dtype_params, _move_to_fsdp_device and fsdp2_wrap match the versions deleted from distributed.py, modulo two changes that are both correct: the two lazy from modelopt.torch.utils.logging import warn_rank_0 imports collapse into one module-level import (safe — see below), and one blank line before return decoder_layers. No change to the dominant-dtype-by-numel selection, the ignored_params plumbing, the CPUOffloadPolicy branch, the mesh-derived compute device, the meta-param skip, or the config.architectures snapshot/restore.
The import-cycle reasoning is right.modelopt/torch/utils/logging.py:47 does from . import distributed as dist at module scope, so a top-level import fsdp2_utils inside distributed.py would reach logging while it is only partially initialized and warn_rank_0 is not yet bound. The lazy import in the forwarder (modelopt/torch/utils/distributed.py:719) is genuinely required, and its comment states the real reason. In the other direction, fsdp2_utils' module-level warn_rank_0 import is safe: the parent modelopt/torch/utils/__init__.py star-imports .logging to completion before any submodule body runs, and nothing in logging reaches back to fsdp2_utils.
The "no Hugging Face" claim holds end to end.fsdp2_utils imports only torch, torch.distributed.fsdp, torch.distributed.tensor and modelopt.torch.utils.logging at module scope; the one remaining link, the lazily imported modelopt/torch/quantization/utils/layerwise_calib.py, has no HF package in its top-level imports either. So the module docstring is accurate rather than aspirational.
Backward compatibility is intact."fsdp2_wrap" stays in distributed.__all__, and the forwarder reproduces the signature exactly, so a positional fsdp2_wrap(model, False) still binds shard_root correctly. modelopt.torch.utils.fsdp2_wrap was never a valid path (__init__.py does not star-import .distributed), so not adding .fsdp2_utils there is consistent and regresses nothing.
No stranded references. Grepped the whole tree for _off_dtype_params, _move_to_fsdp_device, fsdp2_wrap and fsdp2_utils: the only remaining importers of the private helpers are the relocated unit tests. The GPU tests (tests/gpu/torch/utils/test_distributed.py, tests/gpu/torch/export/test_export_carry_over.py) import fsdp2_wrap from modelopt.torch.utils.distributed and reach it through the forwarder; none of them patch fully_shard, _off_dtype_params or _move_to_fsdp_device on distributed, so nothing silently stops taking effect under the move. ruff reports no unused imports, and Any, DTensor and FSDPModule all still have live uses in distributed.py.
The hf extra is off the old path again. Earlier revisions of this PR raised ImportError naming the extra from distributed.fsdp2_wrap; that is gone, which settles cjluo-nv's and CodeRabbit's threads on it. model_load_utils now takes fsdp2_wrap from fsdp2_utils, with the import placed correctly for isort between .distributed and .logging.
Test coverage is proportionate.test_fsdp2_wrap_needs_no_hugging_face is the test that earns its keep: it hides transformers/huggingface_hub/accelerate and evicts model_load_utils, then wraps a plain-PyTorch decoder registered via register_decoder_layer_support for real on a CPU mesh, asserting the layers are FSDPModule while the root is not. Its sys.modules mutation is restored in a finally, so the session-scoped cpu_dist_workers pool is not left polluted. The moved _off_dtype_params tests keep the by-numel dominance cases.
Risk
Low. A pure refactor behind a signature-preserving shim, with the one behavioral property that motivated the restructure — wrapping without the hf extra — covered by a real two-rank test rather than a mock. The remaining utils -> quantization layering dependency for decoder-layer detection is pre-existing, not introduced here.
Note on evidence: I did not run the test suite — this sandbox declined the pytest invocation, so the pass/fail evidence is the author's reported run (tests/unit/torch/utils: 342 passed, 33 skipped without transformers) plus the static checks above, which I did run.
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Bot review (gpt-6.1-sol) — DM the bot to share feedback.
Nudge: the dependency regression is resolved, but stale release notes contradict the fix and one mock-only test conflicts with repository test guidance.
Needs action:
Update the PR body’s generated CodeRabbit notes: wrapping no longer requires the HF extra, and mixed-precision/CPU-offload support is preserved rather than newly added.
Remove test_fsdp2_wrap_keeps_its_old_path’s mock-only forwarding check per CONTRIBUTING.md test-design principles; retain the real wrapping regression and existing GPU/offload coverage.
No action needed:
✔️ Resolved since the last review: the standalone dependency regression, real wrapping coverage without HF imports, and dtype-helper placement. The relocated dtype tests retain their assertions.
Release notes. The generated summary in the description now reads as a refactor. fsdp2_wrap and its dtype helpers move to modelopt.torch.utils.fsdp2_utils, which needs no Hugging Face package, and the old import path forwards to it without the hf extra. Mixed-precision policies, CPU offload and the handling of mixed dtypes work as before. The breaking-change and "improvements" lines are gone.
Mock-only test.test_fsdp2_wrap_keeps_its_old_path is removed. Two kinds of test still cover the old path:
test_fsdp2_wrap_needs_no_hugging_face wraps a registered PyTorch decoder for real through it on two gloo ranks, with Hugging Face unimportable, and checks that shard_root=False reaches the wrap.
The GPU tests in tests/gpu/torch/utils/test_distributed.py wrap through it, mixed dtypes and cpu_offload included.
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Bot review (gpt-6.1-sol) — DM the bot to share feedback.
LGTM: prior concerns are resolved, with no new correctness findings; human sign-off remains for the justified dtype-test relocation.
Needs action:
Sign off on moving the dtype-helper tests to tests/unit/torch/utils/test_fsdp2_utils.py; their cases and assertions are unchanged.
No action needed:
✔️ Resolved since the last review: stale generated release notes and the redundant mock-only forwarding test. The HF-independent implementation and real wrapping regression remain intact.
The FSDP2 load in model_load_utils wraps its model with fsdp2_wrap, so the
wrap moves there, beside the load that calls it. It reaches the dtype helpers
it uses from distributed, and imports the layer detection where it uses it:
importing the quantization package registers every mode and plugin, which only
a wrap needs. modelopt.torch.utils.distributed.fsdp2_wrap forwards to it, so
callers of the old path keep working, given the hf extra the loaders need.
test_fsdp2_wrap_keeps_its_old_path checks the old path forwards every argument
and the result.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
modelopt.torch.utils.distributed.fsdp2_wrap forwards to model_load_utils,
which imports transformers and accelerate at module scope, so without the hf
extra the old path failed on the missing transformers module, with nothing
saying it moved or which extra it needs. It now raises an ImportError naming
the move and the extra. test_fsdp2_wrap_names_the_extra_its_old_path_needs
fails without it.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
_off_dtype_params and _move_to_fsdp_device have no caller but fsdp2_wrap, so
they move beside it into model_load_utils rather than stay private names in
distributed that another module reaches for; they take model_load_utils's own
warn_rank_0, which distributed had to import lazily. Their tests move into
test_model_load_utils.py unchanged, and fsdp2_wrap's docstring names the helper
in its own module.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
The old path's import error named the hf extra for any ImportError from
model_load_utils, so one from inside it, or from a dependency that moved,
sent a user who has the extra to install it again. It now names the extra
only when transformers or accelerate is the missing module, and lets any
other import error through as it is; the test has a row for each.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
model_load_utils imports huggingface_hub at module scope, before transformers, so an
install without it fails on that import first.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
A module missing from an installed transformers is a version mismatch, which the extra's
install message would hide.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
…sformers
Without transformers, the loaders' import of it fails first, so the old path rightly names
the missing extra; the partial-install unit lane runs this file so.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
… own
In the loaders' module, fsdp2_wrap needed transformers and huggingface_hub to import, and
so did its old path, which wrapped without them before: a model whose decoder layers are
registered with LayerActivationCollector needs neither. It now lives with its dtype
helpers in modelopt.torch.utils.fsdp2_utils, which imports no Hugging Face package; the
loaders import it from there, and the old path forwards to it. The changelog no longer
lists the move as breaking.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
The two-rank test wraps a registered decoder for real through the old path, and the GPU
tests wrap through it with cpu_offload, so a check that the forwarder hands its arguments
to a stand-in adds nothing.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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cjluo-nv
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Note
[1/2] of a split of #2779, as review asked to keep each PR under the source-line budget. This PR moves
fsdp2_wrapout ofmodelopt.torch.utils.distributed; [2/2], #2779 ("Route load_pretrained by the distribution plan"), stacks on it and routesload_pretrainedby the distribution plan.Merge order: this PR first, then #2779, which targets this PR's branch until then.
What does this PR do?
Type of change: Refactor (no behavior change)
fsdp2_wrapmoves frommodelopt.torch.utils.distributedinto a module of its own,modelopt.torch.utils.fsdp2_utils, which imports no Hugging Face package. The loaders (modelopt.torch.utils.plugins.model_load_utils) import it from there and wrap with it inparallel_load_and_prepare_fsdp2, as before; #2779 routesload_pretrainedto that load._off_dtype_params,_move_to_fsdp_device), which nothing else calls, move with it, and their tests move intotests/unit/torch/utils/test_fsdp2_utils.py.LayerActivationCollector) where it uses it, as before.modelopt.torch.utils.distributed.fsdp2_wrapforwards to it, so callers of the old path keep working, with the same dependencies as before: a model whose decoder layers are registered withLayerActivationCollectorwraps withouttransformersorhuggingface_hub.Usage
Testing
tests/unit/torch/utils/test_distributed.py::test_fsdp2_wrap_needs_no_hugging_face(two gloo ranks): withtransformers,huggingface_hubandaccelerateunimportable and the loaders' module evicted, the old path wraps a plain PyTorch decoder registered withregister_decoder_layer_supportfor real, on a CPU mesh; it fails when the old path goes through the loaders._off_dtype_paramstests, moved with the helper intotests/unit/torch/utils/test_fsdp2_utils.py.tests/gpu/torch/utils/test_distributed.py,tests/gpu/torch/export/test_export_carry_over.py) and the FSDP2 load's own (tests/gpu/torch/utils/test_model_load_utils.py) run in CI's GPU workflow.tests/unit/torch/utilsand the hf_ptq example tests pass on CPU with transformers 5.18 and 5.5.4. Source insertions: 54.hfextra when it cannot import the loaders; the dtype helpers and their tests move withfsdp2_wrap. Pre-commit, mypy included, passes against main;tests/unit/torch/utilsand the hf_ptq example tests pass on CPU with transformers 5.18 and 5.5.4. Source insertions: 114.hfextra only whentransformersoraccelerateis the missing module, and lets any other import error through as it is; the test has a row for each, and the second fails without the change. Pre-commit passes against main;tests/unit/torch/utilsand the hf_ptq example tests pass on CPU with transformers 5.18 and 5.5.4. Source insertions: 116.hfextra whenhuggingface_hubis the missing module, whichmodel_load_utilsimports at module scope beforetransformers; the test's new row fails without the change. Pre-commit, mypy included, passes against main;tests/unit/torch/utilsand the hf_ptq example tests pass on CPU with transformers 5.18 and 5.5.4. Source insertions: 117.26bf59ace7c): the old path matches the missing module's exact name, so a module missing from an installedtransformersreaches the caller as it is rather than as a missing extra; the test's new row (transformers.conversion_mapping) fails without the change. Pre-commit, mypy included, passes against main;tests/unit/torch/utilspasses on CPU with transformers 5.18. Source insertions: 117.transformers.conversion_mappingrow is skipped wheretransformersis not installed, as in the partial-installtorchunit lane, where the loaders' import oftransformersitself fails first and rightly names the extra; reproduced in such an environment, wheretests/unit/torch/utilsnow passes (339 passed, 35 skipped). Pre-commit, mypy included, passes against main;tests/unit/torch/utilspasses on CPU with transformers 5.18 and 5.5.4. Source insertions: 117.fsdp2_wrapand its dtype helpers live inmodelopt.torch.utils.fsdp2_utils, which imports no Hugging Face package, rather than in the loaders' module, so the old path wraps without thehfextra again, as on main; the loaders import it from there. The old path'sImportErrorfor a missing extra and its tests are gone, and the changelog no longer lists the move. Pre-commit, mypy included, passes against main;tests/unit/torch/utilspasses on CPU with transformers 5.18 and 5.5.4, and without transformers installed (342 passed, 33 skipped). Source insertions: 138.cpu_offloadcover it. The generated release notes below now describe the refactor. Pre-commit, mypy included, passes against main;tests/unit/torch/utilspasses on CPU with transformers 5.18 (548 passed) and without transformers (341 passed, 33 skipped).059588b89a5, without conflicts; this PR's own diff is unchanged. Pre-commit, mypy included, passes against main;tests/unit/torch/utilspasses on CPU with transformers 5.18 (554 passed) and without transformers (341 passed, 35 skipped).d464d30b120, without conflicts; this PR's own diff is unchanged. Pre-commit, mypy included, passes against main;tests/unit/torch/utilspasses on CPU with transformers 5.18 (556 passed) and without transformers (342 passed, 36 skipped).Before your PR is "Ready for review"
CONTRIBUTING.md: N/Aa4bdab004de, before this change.Additional Information
Split from #2779 (expert-parallel PTQ for Hugging Face MoE models). The move was reviewed there, into the loaders' module; review here asked to keep the old path free of the
hfextra, so it moved into a module of its own instead.Summary by CodeRabbit
fsdp2_wrap) and its dtype helpers move tomodelopt.torch.utils.fsdp2_utils, which needs no Hugging Face package. The previous import path,modelopt.torch.utils.distributed.fsdp2_wrap, forwards to it and still works without thehfextra.