Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56923
Next Steps in order:
- Add backward support for CUDA
- Add support for more aggregation types
- Benchmarking (for cuda mainly)/more testing/documentation
- Support for multi dimension
Test Plan: Updated unit test to include 0 length segment as well.
Reviewed By: ngimel
Differential Revision: D27992228
fbshipit-source-id: 28851811f8a784a63162721c511d69e617a93727
Summary:
This is to setup boiler plate code for backward and CPU implementation.
Next Steps in order:
- Add backward support for CUDA
- Add support for more aggregation types
- Benchmarking (for cuda mainly)/more testing/documentation
- Support for multi dimension
Test Plan:
Updated unit test to also check correctness of backward.
Wait for CI signal
Reviewed By: ngimel
Differential Revision: D27970340
fbshipit-source-id: 3e608c7fe3628b0a761dd8affc6aad8f65a6ef7f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56704
This is re submit of PR: https://github.com/pytorch/pytorch/pull/54175
Main changes compared to original PR:
- Switch to importing "<ATen/cuda/cub.cuh>"
- Use CUB_WRAPPER to reduce boiler plate code.
Test Plan:
Will check CI status to make sure a
Added unit test
Reviewed By: ngimel
Differential Revision: D27941257
fbshipit-source-id: 24a0e0c7f6c46126d2606fe42ed03dca15684415
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54175
Building on top of previous PR. This PR adds cuda support for 1D max reduction.
Next steps:
- Add support for other major reduction types (e.g. min, sum) for 1D tensor
- Documentation for the op
- Perf optimizations and benchmark util
- Backward support (not high priority)
- Support for multi dimensional tensors (on data and lengths) (not high priority)
- Support for 'indices' (not high priority)
Test Plan: Added unit test
Reviewed By: ngimel
Differential Revision: D27121170
fbshipit-source-id: 1c2565f42e2903e6fc089d56983ce8857efbfa3c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53727
This is first diff to add native support for segment reduction in PyTorch. It provides similar functionality like torch.scatter or "numpy.ufunc.reduceat".
This diff mainly focuses on API layer to make sure future improvements will not cause backward compatibility issues. Once API is settled, here are next steps I am planning:
- Add support for other major reduction types (e.g. min, sum) for 1D tensor
- Add Cuda support
- Backward support
- Documentation for the op
- Perf optimizations and benchmark util
- Support for multi dimensional tensors (on data and lengths) (not high priority)
- Support for 'indices' (not high priority)
Test Plan: Added unit test
Reviewed By: ngimel
Differential Revision: D26952075
fbshipit-source-id: 8040ec96def3013e7240cf675d499ee424437560
