Fix Diffusers export dtype resolution - #2225
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Included review availability: Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughThe export dtype resolver now handles models without ChangesExport dtype fallback
Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: 🔵 Low · up to The export dtype resolution change is localized and preserves configured-dtype mismatch warnings, but the regression suite does not yet exercise the exact FrozenDict-without-torch_dtype case. The PR is mergeable with explicit follow-up to add that targeted test. Suggested reviewers: 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2225 +/- ##
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- Coverage 78.99% 78.41% -0.59%
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Signed-off-by: realAsma <akuriparambi@nvidia.com>
Signed-off-by: realAsma <akuriparambi@nvidia.com>
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Inline comments:
In `@tests/unit/torch/export/test_unified_export_hf.py`:
- Around line 36-43: Add focused offline pytest coverage for
_resolve_export_dtype covering configured and unconfigured model dtypes,
including a FrozenDict configuration fixture and the mismatch-warning path.
Reuse local fixtures, assert the resolved dtype for matching and absent
configuration values, and verify that an explicitly requested dtype differing
from the configured dtype emits the expected warning.
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Inline comments:
In `@tests/unit/torch/export/test_unified_export_hf.py`:
- Around line 40-44: Add a parameterized test case using an empty FrozenDict()
without torch_dtype, with no requested dtype, expecting torch.float32 and the
existing default status value.
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In `@tests/unit/torch/export/test_unified_export_hf.py`:
- Around line 63-64: Add a brief comment immediately before the
pytest.importorskip call in
test_resolve_export_dtype_with_empty_diffusers_config explaining that Diffusers
is imported locally to keep it optional during torch-only unit-test collection.
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Small, low-risk change and the added unit tests are welcome, but two things should be resolved before merge:
-
The new fallback duplicates an existing helper.
modelopt/torch/export/diffusers_utils.py::infer_dtype_from_modelalready implements exactly "first parameter's dtype, elsetorch.float16" — the same semantics, same magic default. The diffusers export path (_export_diffusers_checkpoint) already calls it. Please reuse it (it's pure torch, so it can be moved to a neutral module if thediffusers-optional import is a problem) instead of inlining a second copy inunified_export_hf.py. -
The stated motivation doesn't obviously match the code path.
_resolve_export_dtypeis only called from_export_transformers_checkpointand_export_transformers_checkpoint_streaming.export_hf_checkpointroutes diffusers objects to_export_diffusers_checkpointbefore either is reached, and that function never touchesmodel.config.torch_dtype. A diffusers pipeline/component can only reach this line ifis_diffusers_object()returnedFalse(e.g. thefrom diffusers import ...guard inunified_export_hf.py/diffusers_utils.pyfailed under the minimum-transformers combination) — in which case the real bug is the detection/import, and this patch just moves the crash further down the transformers-only path (TiedWeightMap,requantize_resmooth_fused_llm_layers, ... on a non-transformers module). Please paste the actual traceback in the PR body and confirm which path is failing.
Minor: the torch.float16 no-parameter branch is untested, and there is a small behavior change for dtype=None + torch_dtype=None (previously returned None, i.e. weight.to(None) was a no-op preserving per-tensor dtypes; now everything is cast to the first parameter's dtype). That is probably what you want, but it's worth stating explicitly.
No licensing changes and no prompt-injection content in the PR text.
| if configured_dtype is not None: | ||
| return configured_dtype | ||
| first_parameter = next(model.parameters(), None) | ||
| return first_parameter.dtype if first_parameter is not None else torch.float16 |
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Bot comment.
This is a verbatim re-implementation of infer_dtype_from_model() in modelopt/torch/export/diffusers_utils.py (first parameter dtype, torch.float16 fallback), which the diffusers export path already uses via _export_diffusers_checkpoint. Please call that helper here rather than adding a second copy — otherwise the two defaults can drift apart. If importing it from diffusers_utils is awkward because that module is diffusers-optional, note that the function itself has no diffusers dependency and could be moved to a neutral module (e.g. model_utils.py).
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| def _resolve_export_dtype(model: nn.Module, dtype: torch.dtype | None) -> torch.dtype: | ||
| """Return the export dtype, defaulting to the model's own and warning on a mismatch.""" | ||
| configured_dtype = getattr(model.config, "torch_dtype", None) |
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Bot comment.
How does a Diffusers export reach this function? export_hf_checkpoint() dispatches is_diffusers_object(model) to _export_diffusers_checkpoint(), which resolves dtype through infer_dtype_from_model() and never reads model.config.torch_dtype; _resolve_export_dtype is only reachable from the transformers (resident + streaming) paths. If a diffusers pipeline/component is landing here, that means is_diffusers_object() returned False (most likely HAS_DIFFUSERS/_HAS_DIFFUSERS being False because the from diffusers import ... guard failed under the minimum-transformers combo), and the root cause is the detection, not the missing torch_dtype — this patch would only push the failure to the next transformers-only step (TiedWeightMap(model), requantize_resmooth_fused_llm_layers(model), model.named_modules() on a pipeline). Please include the traceback in the PR body so the actual failing dispatch is on record.
Also, getattr(model.config, ...) still raises if the object has no .config at all; getattr(getattr(model, "config", None), "torch_dtype", None) would be the fully defensive form if that's the intent.
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| def test_resolve_export_dtype_with_empty_diffusers_config(): |
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Bot comment.
This case is behaviorally identical to the already-parameterized (None, None, torch.float32, 0) row: an empty FrozenDict has no torch_dtype attribute, so both take the next(model.parameters()) branch. What isn't covered is (a) a config that does carry torch_dtype as a string ("bfloat16") — which is how diffusers FrozenDicts deserialize it from JSON, and which would be returned as-is here and then fed to weight.to(...), and (b) the parameterless torch.float16 fallback. Both are worth a row.
3 participants
What does this PR do?
Type of change: Bug fix
When an explicit export dtype is provided, tolerate model configs that do not define
torch_dtype. This fixes Diffusers export under dependency combinations where the pipeline config is aFrozenDictwithout that field, while preserving existing mismatch warnings when a configured dtype is available.Usage
N/A — no API change.
Testing
pytest_pwd tests/unit/torch/export/test_unified_export_hf.py -k resolve_export_dtype(1 passed)pytest_pwd tests/unit/torch/export/test_unified_export_hf.py(23 passed)pre-commit run --files modelopt/torch/export/unified_export_hf.py tests/unit/torch/export/test_unified_export_hf.pyBefore your PR is "Ready for review"
Make sure you read and follow Contributor guidelines and your commits are signed (
git commit -s -S).Make sure you read and follow the Security Best Practices.
CONTRIBUTING.md: N/AAdditional Information
This is independent of PR #2223 and addresses the unrelated minimum-Transformers Diffusers export failure.
Summary by CodeRabbit
Bug Fixes
Tests