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* Initial commit of the unique operator

Add unit tests for unique operator

* Add tensorflow unique op

* Refactor unique to use sort-based algorithm

* Change relay.unique test to run only on cpu

* Change topi.unique test to run only on cpu

* Change range to parallel for parallelizable loops

* Add return_counts option for relay.unique and topi.unique, add pytorch frontend

* Fix pylint

* Patch pytorch frontend

* Initial support of topi.cuda.unique

* Refactor to use ir_builder directly

* Modularize adjacent difference

* Refactor to simplify

* Fix typo

* Combine _unique and _unique_with_counts

* Reuse indices_ptr to remove arange_ptr

Co-authored-by: Yanming Wang <yanmwang@amazon.com>
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README.md

Open Deep Learning Compiler Stack

Documentation | Contributors | Community | Release Notes

Build Status WinMacBuild

Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.

License

© Contributors Licensed under an Apache-2.0 license.

Contribute to TVM

TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Checkout the Contributor Guide

Acknowledgement

We learned a lot from the following projects when building TVM.

  • Halide: Part of TVM's TIR and arithmetic simplification module originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.
  • Loopy: use of integer set analysis and its loop transformation primitives.
  • Theano: the design inspiration of symbolic scan operator for recurrence.