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(1) Save gpt2 test data during test generation. (2) Use torch fp32 model as baseline when onnx model is fp16. (3) Refine logic to compose onnx model path |
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| .. | ||
| backend | ||
| datasets | ||
| tools | ||
| training | ||
| __init__.py | ||
| _ld_preload.py | ||
| _pybind_state.py | ||
| exported_symbols.lst | ||
| onnxruntime_pybind_exceptions.h | ||
| onnxruntime_pybind_mlvalue.cc | ||
| onnxruntime_pybind_mlvalue.h | ||
| onnxruntime_pybind_state.cc | ||
| onnxruntime_pybind_state_common.cc | ||
| onnxruntime_pybind_state_common.h | ||
| onnxruntime_validation.py | ||
| pybind.def | ||
| session.py | ||
| version_script.lds | ||