[None][refactor] Table-drive the lazy-safetensors model-type check in HfWeightLoader - #18681
[None][refactor] Table-drive the lazy-safetensors model-type check in HfWeightLoader#18681moraxu wants to merge 5 commits into
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… HfWeightLoader `HfWeightLoader._is_kimi_k3_checkpoint` decided, from `config.json`'s `model_type`, whether a checkpoint must stay mmapped instead of being materialized in host RAM, and hard-coded the two Kimi model types in the method body. Rename it to `_requires_lazy_safetensors` and move the model types into a module-level `_LAZY_SAFETENSORS_MODEL_TYPES` tuple, so the next model family whose checkpoint is too large for host RAM opts in by adding one entry instead of editing a Kimi-named predicate. Behavior is unchanged: the tuple holds exactly the two model types the method used to name. Add unit tests that exercise the predicate for every listed model type, for a non-lazy model type, for a config without `model_type`, and for a directory without `config.json`. Signed-off-by: Michal Guzek <mguzek@nvidia.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. WalkthroughThe checkpoint loader now uses a shared model-type allowlist to select lazy SafeTensors loading. Rank-striped preflight and native loading use the generalized detection predicate. Tests cover supported, unsupported, and missing checkpoint configurations. ChangesLazy SafeTensors loading
Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: ⚪ Minimal · up to This refactor generalizes lazy SafeTensors model-type detection while retaining existing behavior and adding predicate coverage. No current merge-blocking risk is identified. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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In `@tests/unittest/_torch/models/checkpoints/hf/test_weight_loader.py`:
- Around line 525-531: Update the appropriate integration test list to include
entries resolving to all three new tests in test_weight_loader.py, including
test_requires_lazy_safetensors_for_every_listed_model_type and the other two
added tests. Preserve the existing test-list format and naming conventions.
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Short docstrings for the two new test functions that had none. Signed-off-by: Michal Guzek <mguzek@nvidia.com>
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Inline comments:
In `@tests/unittest/_torch/models/checkpoints/hf/test_weight_loader.py`:
- Line 543: Add precise annotations to
test_requires_lazy_safetensors_is_false_without_a_config by typing tmp_path as
Path and annotating the function return as None.
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tests/unittest/_torch/models/checkpoints/hf/test_weight_loader.py
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Review asked for type annotations on the new test functions: the three predicate tests now declare tmp_path: Path, model_type: str, config: dict[str, str] and a None return. Also give _start_rank_striped_read_ahead and _load_weights_native, which this change touches, one-line docstrings to address the pre-merge docstring-coverage check. Signed-off-by: Michal Guzek <mguzek@nvidia.com>
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What does "table-drive" mean? Also, do I understand correctly that this is a minor refactoring? It seems like a (private) helper was renamed, and some hardcoded values put into a module-level "constant", but other than that, the behavior is completely unchanged wrt main?
| tmp_path: Path, model_type: str | ||
| ) -> None: | ||
| """Each model type in the table routes to the lazy (mmapped) load path.""" | ||
| import json |
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Nt: please move to module-level.
This a bit unfortunate wording means the module-level constant now acts as a table, perhaps "data-drive" could be more accurate?
Yes, it's just a refactor, nothing changes wrt main. This is just to easily accommodate similar models in the future. |
Signed-off-by: Michal Guzek <mguzek@nvidia.com>
Requested in review: one module-level import instead of function-local imports in the predicate tests. Signed-off-by: Michal Guzek <mguzek@nvidia.com>
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Description
HfWeightLoader._is_kimi_k3_checkpointdecides, fromconfig.json'smodel_type, whether a checkpoint must stay mmapped instead of being materialized in host RAM (the model then streams rank-local slices out of the lazy handles). The method name and body hard-coded the two Kimi model types.This renames it to
_requires_lazy_safetensorsand moves the model types into a module-level_LAZY_SAFETENSORS_MODEL_TYPEStuple, so the next model family whose checkpoint is too large for host RAM opts in by adding one entry instead of editing a Kimi-named predicate. No behavior change: the tuple holds exactly the two model types the method used to name.Test Coverage
tests/unittest/_torch/models/checkpoints/hf/test_weight_loader.py::test_kimi_k3_lazy_load_records_the_checkpoint_dirstill exercises the lazy path end to end._LAZY_SAFETENSORS_MODEL_TYPES, false for anothermodel_type, false for a config withoutmodel_type, and false for a directory withoutconfig.json.PR Checklist
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Please check this after reviewing the above items as appropriate for this PR.
Dev Engineer Review
_is_kimi_k3_checkpointto_requires_lazy_safetensors._LAZY_SAFETENSORS_MODEL_TYPESfor extensible model-family support.QA Engineer Review
test_requires_lazy_safetensors_for_every_listed_model_type.test_requires_lazy_safetensors_is_false_for_other_checkpoints.test_requires_lazy_safetensors_is_false_without_a_config.model_type, and missingconfig.json.tests/integration/test_lists/test-db/orqa/.