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### Description <!-- Describe your changes. --> Transformer models can handle batch of inputs at once. However, sequences in a batch usually have different length. Then we have to pad the short one to have same length as the longest. This is not efficient especially for large batch with high variance. This PR introduces a PackedAttention operator which can take in packed sequences (no padding) and also produces output in packing mode. There will be another PR to use the PackedAttention to implement the encoder in packing mode. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> |
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| .. | ||
| c_cxx | ||
| execution_providers/images | ||
| images | ||
| python | ||
| ABI_Dev_Notes.md | ||
| Android_testing.md | ||
| C_API_Guidelines.md | ||
| cmake_guideline.md | ||
| Coding_Conventions_and_Standards.md | ||
| ContribOperators.md | ||
| FAQ.md | ||
| How_To_Update_ONNX_Dev_Notes.md | ||
| Memory_Optimizer.md | ||
| Model_Test.md | ||
| NotesOnThreading.md | ||
| ONNX_Runtime_Server_Usage.md | ||
| onnxruntime_dependencies.dot | ||
| onnxruntime_dependencies.png | ||
| onnxruntime_extensions.md | ||
| OperatorKernels.md | ||
| ORT_Format_Update_in_1.13.md | ||
| ORTMobilePackageOperatorTypeSupport.md | ||
| ORTModule_Training_Guidelines.md | ||
| PR_Guidelines.md | ||
| Privacy.md | ||
| Python_Dev_Notes.md | ||
| Reduced_Operator_Kernel_build.md | ||
| ReleaseManagement.md | ||
| Roadmap.md | ||
| Server.md | ||
| TVM_EP.md | ||
| Versioning.md | ||
| WinML_principles.md | ||
