Make PyTorch models up to 40% faster! Thunder is a source to source compiler for PyTorch. It enables using different hardware executors at once; across one or thousands of GPUs.
Before submitting
- [x] Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
- [x] Did you read the [contributor guideline](https://github.com/Lightning-AI/pytorch-lightning/blob/main/.github/CONTRIBUTING.md), Pull Request section?
- [ ] Did you make sure to update the docs?
- [x] Did you write any new necessary tests?
What does this PR do?
Fixes #510.
[x] Updated TensorProxy's __repr__.
This resulted in updating prettyprint
[x] Updated Device's __repr__
make_tensor accepts thunder.devices.Device now!
[x] update dtype __repr__
[x] update executor __repr__
Potential Discussions
codeutils.py's prettyprint to handle TensorProxy x in dataclass
passing device.type for make_tensor
device indexing for cpu (do we allow cpu:0?) (related to _device_from_string_helper)
name of the __repr__
PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
Before submitting
- [x] Was this discussed/approved via a Github issue? (no need for typos and docs improvements) - [x] Did you read the [contributor guideline](https://github.com/Lightning-AI/pytorch-lightning/blob/main/.github/CONTRIBUTING.md), Pull Request section? - [ ] Did you make sure to update the docs? - [x] Did you write any new necessary tests?What does this PR do?
Fixes #510.
TensorProxy's__repr__.prettyprintDevice's__repr__make_tensoracceptsthunder.devices.Devicenow!__repr____repr__Potential Discussions
codeutils.py'sprettyprintto handleTensorProxy xindataclassdevice.typeformake_tensor_device_from_string_helper)__repr__PR review
Anyone in the community is free to review the PR once the tests have passed. If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
Did you have fun?
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