Validate generation subgraph shapes (#32078)
This pull request strengthens input validation and error handling for
subgraph classes in the ONNX Runtime transformers codebase. It
introduces additional checks for required input/output shapes and
dimensions, ensuring that missing or malformed shapes are detected early
with clear error messages. The changes also add corresponding unit tests
to verify the new validation logic.
**Validation and Error Handling Improvements:**
* Added checks to ensure subgraph output names are not empty in
`subgraph_base.cc`, and that required tensor shapes (such as logits and
past state shapes) are not null before proceeding with parameter
extraction or validation. This applies to GPT, T5, and Whisper subgraph
classes (`subgraph_base.cc`, `subgraph_gpt.cc`,
`subgraph_t5_decoder.cc`, `subgraph_t5_encoder.cc`,
`subgraph_whisper_decoder.cc`, `subgraph_whisper_encoder.cc`).
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* Improved validation of input dimensions, such as ensuring decoder
subgraph input tensors have the expected number of dimensions for T5 and
Whisper models (`subgraph_t5_decoder.cc`,
`subgraph_whisper_decoder.cc`).
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**Unit Testing:**
* Added new unit tests in `beam_search_test.cc` to verify that missing
logits or past state shapes are correctly rejected, and that error
messages are descriptive. This includes helper code to load test models
and manipulate subgraph outputs for testing.
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**Bug Fixes:**
* Fixed an error message in `subgraph_whisper_encoder.cc` to reference
the correct input index for `decoder_input_ids`.
These changes improve robustness by ensuring that subgraph classes fail
early and clearly when required inputs are missing or malformed, and
they are now covered by dedicated unit tests.
