[multimodal] Allow float32 image input #14490
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Letting `Image` class support both `uint8_t` and `float` data types, changing `MultimodalPrefiller` class to support text, image, and audio modalities with error checking and modularity. **Image Data Handling and Type Safety:** * Refactored the `Image` class in `image.h` from a simple struct to a class that uses a `std::variant` to support both `uint8_t` and `float` image data, providing type-safe accessors and a `toTensor` method for conversion to tensors. * Updated `load_image` in Llava `main.cpp` to construct `Image` objects using the new class interface and move semantics, ensuring correct data layout and encapsulation. * Added a runtime check in `LlavaImagePrefiller` to ensure only `uint8_t` images are processed, using the new type-checking methods. **Multimodal Prefill Logic and Flexibility:** * Updated the `MultimodalPrefiller` class in `multimodal_prefiller.h` to dynamically check input types, validate tensor types against model expectations, and handles encoder/decoder execution with improved error handling and modularity. (cherry picked from commit bc18834)
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This was referenced Sep 22, 2025
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14490
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Letting
Imageclass support bothuint8_tandfloatdata types, changingMultimodalPrefillerclass to support text, image, and audio modalities with error checking and modularity.Image Data Handling and Type Safety:
Imageclass inimage.hfrom a simple struct to a class that uses astd::variantto support bothuint8_tandfloatimage data, providing type-safe accessors and atoTensormethod for conversion to tensors.load_imagein Llavamain.cppto constructImageobjects using the new class interface and move semantics, ensuring correct data layout and encapsulation.LlavaImagePrefillerto ensure onlyuint8_timages are processed, using the new type-checking methods.Multimodal Prefill Logic and Flexibility:
MultimodalPrefillerclass inmultimodal_prefiller.hto dynamically check input types, validate tensor types against model expectations, and handles encoder/decoder execution with improved error handling and modularity.