pytorch
Loop Conversion
Description
Dynamic loop conversion from PyTorch to TRT. A few limitations with this approach, as of now:
- all recurrent values inside loop can be Tensors only
- works with for loops, albeit with a comparative threshold (i.e. TRT vs JIT) that is greater than the set limit of 2e-5
- breaks converter contract: all primitive types (e.g. int, float, etc.) could potentially be Tensors when processed inside the converter
Type of change
- Bug fix (non-breaking change which fixes an issue)
- New feature (non-breaking change which adds functionality)
- Breaking change (fix or feature that would cause existing functionality to not work as expected)
- This change requires a documentation update
Checklist:
- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my own code
- [ ] I have commented my code, particularly in hard-to-understand areas and hacks
- [ ] I have made corresponding changes to the documentation and have regenerated the documentation (
make htmlin docsrc) - [ ] I have added tests to verify my fix or my feature
- [ ] New and existing unit tests pass locally with my changes
TRT has no guarantee about output binding order.
e.g. an RNN loop that outputs hidden state hx and cell state cx might be reversed when outputs are processed, even if they were marked as inputs in the right order
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