| 文件 | 最后提交记录 | 最后更新时间 |
|---|---|---|
Bring the C++ ONNX importer on par with onnx_importer.py (#3960) This PR heavily refactors the C++ importer to make it a viable (and faster) alternative to the Python one for those who need to import ONNX models and want to avoid depending on the python ecosystem. - The C++ importer now outputs the same exact mlir as the Python one (tested on alt_e2eshark test suite). Achieving perfect output matches required to introduce an associative map iterable according to insertion order (to mimic Dict in Python). - The code tries to mirror 1-to-1 the Python counterpart whenever possible/convenient. - Adds support for embedding ONNX external data in the mlir. This functionality is not part of torch-mlir's onnx_importer.py but of IREE's import_onnx. - Efforts have been made to remove the direct dependency on LLVM support lib. There is however a transitive dependency on such lib through MLIRCAPIIR and TorchMLIRCAPI (MLIR libraries uniformly depend on LLVMSupport). | 1 年前 | |
Bring the C++ ONNX importer on par with onnx_importer.py (#3960) This PR heavily refactors the C++ importer to make it a viable (and faster) alternative to the Python one for those who need to import ONNX models and want to avoid depending on the python ecosystem. - The C++ importer now outputs the same exact mlir as the Python one (tested on alt_e2eshark test suite). Achieving perfect output matches required to introduce an associative map iterable according to insertion order (to mimic Dict in Python). - The code tries to mirror 1-to-1 the Python counterpart whenever possible/convenient. - Adds support for embedding ONNX external data in the mlir. This functionality is not part of torch-mlir's onnx_importer.py but of IREE's import_onnx. - Efforts have been made to remove the direct dependency on LLVM support lib. There is however a transitive dependency on such lib through MLIRCAPIIR and TorchMLIRCAPI (MLIR libraries uniformly depend on LLVMSupport). | 1 年前 | |
Bring the C++ ONNX importer on par with onnx_importer.py (#3960) This PR heavily refactors the C++ importer to make it a viable (and faster) alternative to the Python one for those who need to import ONNX models and want to avoid depending on the python ecosystem. - The C++ importer now outputs the same exact mlir as the Python one (tested on alt_e2eshark test suite). Achieving perfect output matches required to introduce an associative map iterable according to insertion order (to mimic Dict in Python). - The code tries to mirror 1-to-1 the Python counterpart whenever possible/convenient. - Adds support for embedding ONNX external data in the mlir. This functionality is not part of torch-mlir's onnx_importer.py but of IREE's import_onnx. - Efforts have been made to remove the direct dependency on LLVM support lib. There is however a transitive dependency on such lib through MLIRCAPIIR and TorchMLIRCAPI (MLIR libraries uniformly depend on LLVMSupport). | 1 年前 | |
Bring the C++ ONNX importer on par with onnx_importer.py (#3960) This PR heavily refactors the C++ importer to make it a viable (and faster) alternative to the Python one for those who need to import ONNX models and want to avoid depending on the python ecosystem. - The C++ importer now outputs the same exact mlir as the Python one (tested on alt_e2eshark test suite). Achieving perfect output matches required to introduce an associative map iterable according to insertion order (to mimic Dict in Python). - The code tries to mirror 1-to-1 the Python counterpart whenever possible/convenient. - Adds support for embedding ONNX external data in the mlir. This functionality is not part of torch-mlir's onnx_importer.py but of IREE's import_onnx. - Efforts have been made to remove the direct dependency on LLVM support lib. There is however a transitive dependency on such lib through MLIRCAPIIR and TorchMLIRCAPI (MLIR libraries uniformly depend on LLVMSupport). | 1 年前 | |
Bring the C++ ONNX importer on par with onnx_importer.py (#3960) This PR heavily refactors the C++ importer to make it a viable (and faster) alternative to the Python one for those who need to import ONNX models and want to avoid depending on the python ecosystem. - The C++ importer now outputs the same exact mlir as the Python one (tested on alt_e2eshark test suite). Achieving perfect output matches required to introduce an associative map iterable according to insertion order (to mimic Dict in Python). - The code tries to mirror 1-to-1 the Python counterpart whenever possible/convenient. - Adds support for embedding ONNX external data in the mlir. This functionality is not part of torch-mlir's onnx_importer.py but of IREE's import_onnx. - Efforts have been made to remove the direct dependency on LLVM support lib. There is however a transitive dependency on such lib through MLIRCAPIIR and TorchMLIRCAPI (MLIR libraries uniformly depend on LLVMSupport). | 1 年前 | |
Bring the C++ ONNX importer on par with onnx_importer.py (#3960) This PR heavily refactors the C++ importer to make it a viable (and faster) alternative to the Python one for those who need to import ONNX models and want to avoid depending on the python ecosystem. - The C++ importer now outputs the same exact mlir as the Python one (tested on alt_e2eshark test suite). Achieving perfect output matches required to introduce an associative map iterable according to insertion order (to mimic Dict in Python). - The code tries to mirror 1-to-1 the Python counterpart whenever possible/convenient. - Adds support for embedding ONNX external data in the mlir. This functionality is not part of torch-mlir's onnx_importer.py but of IREE's import_onnx. - Efforts have been made to remove the direct dependency on LLVM support lib. There is however a transitive dependency on such lib through MLIRCAPIIR and TorchMLIRCAPI (MLIR libraries uniformly depend on LLVMSupport). | 1 年前 | |
Bring the C++ ONNX importer on par with onnx_importer.py (#3960) This PR heavily refactors the C++ importer to make it a viable (and faster) alternative to the Python one for those who need to import ONNX models and want to avoid depending on the python ecosystem. - The C++ importer now outputs the same exact mlir as the Python one (tested on alt_e2eshark test suite). Achieving perfect output matches required to introduce an associative map iterable according to insertion order (to mimic Dict in Python). - The code tries to mirror 1-to-1 the Python counterpart whenever possible/convenient. - Adds support for embedding ONNX external data in the mlir. This functionality is not part of torch-mlir's onnx_importer.py but of IREE's import_onnx. - Efforts have been made to remove the direct dependency on LLVM support lib. There is however a transitive dependency on such lib through MLIRCAPIIR and TorchMLIRCAPI (MLIR libraries uniformly depend on LLVMSupport). | 1 年前 | |
Bring the C++ ONNX importer on par with onnx_importer.py (#3960) This PR heavily refactors the C++ importer to make it a viable (and faster) alternative to the Python one for those who need to import ONNX models and want to avoid depending on the python ecosystem. - The C++ importer now outputs the same exact mlir as the Python one (tested on alt_e2eshark test suite). Achieving perfect output matches required to introduce an associative map iterable according to insertion order (to mimic Dict in Python). - The code tries to mirror 1-to-1 the Python counterpart whenever possible/convenient. - Adds support for embedding ONNX external data in the mlir. This functionality is not part of torch-mlir's onnx_importer.py but of IREE's import_onnx. - Efforts have been made to remove the direct dependency on LLVM support lib. There is however a transitive dependency on such lib through MLIRCAPIIR and TorchMLIRCAPI (MLIR libraries uniformly depend on LLVMSupport). | 1 年前 | |
Bring the C++ ONNX importer on par with onnx_importer.py (#3960) This PR heavily refactors the C++ importer to make it a viable (and faster) alternative to the Python one for those who need to import ONNX models and want to avoid depending on the python ecosystem. - The C++ importer now outputs the same exact mlir as the Python one (tested on alt_e2eshark test suite). Achieving perfect output matches required to introduce an associative map iterable according to insertion order (to mimic Dict in Python). - The code tries to mirror 1-to-1 the Python counterpart whenever possible/convenient. - Adds support for embedding ONNX external data in the mlir. This functionality is not part of torch-mlir's onnx_importer.py but of IREE's import_onnx. - Efforts have been made to remove the direct dependency on LLVM support lib. There is however a transitive dependency on such lib through MLIRCAPIIR and TorchMLIRCAPI (MLIR libraries uniformly depend on LLVMSupport). | 1 年前 |
ONNX C Importer
This project provides a C++ implementation of the onnx_importer.py, which is
the canonical source. It is provided as sample code for anyone who wishes to
integrate it into their system. By design, it only depends on the ONNX API
and the MLIR C API via the mlir-c headers. As such, it should be easy to
build into any system that already has those things by adding the sources.