onnxruntime/orttraining
Wei-Sheng Chin b0279b14d8
[DORT] Enable Dynamic Shape in DORT and Use Different InferenceSession's when Inputs Are Not Compatible (#16753)
Sometimes, ONNX exporter generates rank- or shape-dependent sub-graphs.
Thus, error could occur when running the ONNX model with different
inputs. This PR
([78e736d](78e736d857))
addresses this problem by
- if needed, exporting multiple ONNX models with different inputs for
the same GraphModule.
- implementing a naive mechanism to determine of existing ONNX models
(and the associated InferenceSession) can be reused.
 
On the other hand, in the second commit
[b5a9b5f](b5a9b5f849),
this PR also enables dynamic shapes in DORT by
- passing dynamic_shapes = True to exporter (see how
DEFAULT_DYNAMIC_BACKEND is created)
- calling torch._dynamo.optimize(dynamic_ort_aot, dynamic=True) (see how
dynamic_ort_aot is created).
2023-07-24 16:54:01 -07:00
..
orttraining [DORT] Enable Dynamic Shape in DORT and Use Different InferenceSession's when Inputs Are Not Compatible (#16753) 2023-07-24 16:54:01 -07:00
pytorch_frontend_examples [Better Engineering] Bump ruff to 0.0.278 and fix new lint errors (#16789) 2023-07-21 12:53:41 -07:00
tools [Better Engineering] Bump ruff to 0.0.278 and fix new lint errors (#16789) 2023-07-21 12:53:41 -07:00