cp: fix: transformers v5.5.0 validation (2010) into r0.4.0#2013
Merged
cp: fix: transformers v5.5.0 validation (2010) into r0.4.0#2013
fix: transformers v5.5.0 validation (2010) into r0.4.0#2013Conversation
* catch StrictDataclassClassValidationError Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> * test: limit nightly to stepfun recipe for stepfun CI repro Temporarily prune nightly_recipes.yml to the single stepfun/step_3.5_flash_hellaswag_pp.yaml recipe to iterate on the StrictDataclassClassValidationError fix without paying for the full nightly matrix. Not intended for merge. Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> * fix: retry NeMoAutoTokenizer load when config trips layer_types validator AutoTokenizer.from_pretrained internally calls AutoConfig.from_pretrained to resolve the tokenizer class. For checkpoints whose config has layer_types longer than num_hidden_layers (e.g. stepfun-ai/Step-3.5-Flash), newer transformers rejects the config and huggingface_hub wraps the ValueError in StrictDataclassClassValidationError (not a ValueError subclass). The previous get_hf_config fix only covered the model-load path; the tokenizer path hit the same failure independently. On that specific validator failure, preload a config via get_hf_config (which truncates layer_types) and retry the tokenizer load with an explicit config=, bypassing the internal AutoConfig call. Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> * refactor: relax validate_layer_type globally instead of preloading config The previous tokenizer retry preloaded a fixed config via get_hf_config and re-entered AutoTokenizer.from_pretrained with an explicit config=. That round-trip is brittle (reconstructs a config the tokenizer does not use) and only fixes the tokenizer call site. Replace it with relax_layer_types_validator(): a one-shot monkey-patch that swaps PretrainedConfig.validate_layer_type with a no-op and rewrites the already-frozen validator entries in every live subclass's __class_validators__ list. After that, any downstream call that instantiates a config with mismatched layer_types/num_hidden_layers skips the check. The tokenizer retry now just applies the patch and re-invokes super().from_pretrained(...) with the original kwargs. Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> * fix: retry VLM AutoProcessor load on layer_types validation failure AutoProcessor.from_pretrained internally loads AutoConfig, so configs whose layer_types length differs from num_hidden_layers trip validate_layer_type through the processor path too. Previously the VLM build_dataloader caught the error under a broad except and silently set processor=None, producing a cryptic downstream failure. On the specific validator signature, call relax_layer_types_validator() and retry AutoProcessor.from_pretrained once. Unrelated exceptions keep the original fall-through to processor=None with a warning. LLM tokenizer path is already covered via NeMoAutoTokenizer. Also pass --force-exclude to the ruff pre-commit hooks so the tests/ exclusion already declared in pyproject.toml takes effect when pre-commit passes files explicitly. Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> * revert Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> * revert Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> * fmt Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> --------- Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com>
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/ok to test c748500 |
akoumpa
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Apr 23, 2026
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beep boop [🤖]: Hi @akoumpa 👋,