feat: Add native Gemma3 support for ONNX export #87
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
🎯 Problem
Users trying to export fine-tuned Gemma3 models to ONNX format encounter this error:
This prevents users from converting their Gemma3 models to ONNX format for deployment and optimization.
🔧 Solution
This PR adds native Gemma3 support to optimum by implementing the
Gemma3OnnxConfigclass and properly registering it in the tasks manager.📝 Changes Made
✨ Core Implementation
Added
Gemma3OnnxConfigclass** inoptimum/exporters/onnx/model_configs.pyTextDecoderOnnxConfig(same pattern as Gemma2)GemmaDummyPastKeyValuesGeneratorfor compatibility🔧 Supporting Changes
optimum/exporters/onnx/utils.py** - Added "gemma3" toMODEL_TYPES_REQUIRING_POSITION_IDS🧪 Testing & Verification
📋 Usage Example
After this change, users can export Gemma3 models seamlessly: