Enable pointer-generator T5 models in BeamSearch #23134
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onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_decoder.cc
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onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_decoder.cc
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/azp run Windows ARM64 QNN CI Pipeline,Windows x64 QNN CI Pipeline,Windows CPU CI Pipeline,Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline,Windows GPU Doc Gen CI Pipeline,Windows GPU TensorRT CI Pipeline,ONNX Runtime Web CI Pipeline,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline |
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/azp run Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-linux-gpu-ci-pipeline,onnxruntime-binary-size-checks-ci-pipeline,Big Models,Linux Android Emulator QNN CI Pipeline,Android CI Pipeline |
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/azp run iOS CI Pipeline,ONNX Runtime React Native CI Pipeline,CoreML CI Pipeline,Linux DNNL CI Pipeline,Linux MIGraphX CI Pipeline,Linux ROCm CI Pipeline |
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@tianleiwu I don't think I got which is the problem on the iOS failure. All the involved tests seems passing there. Do you have some insights? |
onnxruntime/contrib_ops/cpu/transformers/subgraph_t5_decoder.cc
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/azp run Windows ARM64 QNN CI Pipeline,Windows x64 QNN CI Pipeline,Windows CPU CI Pipeline,Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline,Windows GPU Doc Gen CI Pipeline,Windows GPU TensorRT CI Pipeline,ONNX Runtime Web CI Pipeline,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline |
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/azp run Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,onnxruntime-binary-size-checks-ci-pipeline,Big Models,Linux Android Emulator QNN CI Pipeline |
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/azp run Android CI Pipeline,iOS CI Pipeline,ONNX Runtime React Native CI Pipeline,CoreML CI Pipeline,Linux DNNL CI Pipeline,Linux MIGraphX CI Pipeline,Linux ROCm CI Pipeline |
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Azure Pipelines successfully started running 7 pipeline(s). |
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### Description Introduces a new optional input (encoder_ibnput_ids) in the decoder graph of the T5 implementation for BeamSearch. This allows usage of pointer generator networks in decoder graph. ### Motivation and Context - Fixes microsoft#23123
### Description Introduces a new optional input (encoder_ibnput_ids) in the decoder graph of the T5 implementation for BeamSearch. This allows usage of pointer generator networks in decoder graph. ### Motivation and Context - Fixes #23123
Description
Introduces a new optional input (encoder_input_ids) in the decoder graph of the T5 implementation for BeamSearch. This allows usage of pointer generator networks in decoder graph.
Motivation and Context