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* add relay.f.frontend.fm_oneflow support cnns

* support cuda

* fix mobilenetv2 and reviews

* fix: model without meta info

* support eager and yolo, add test

* fix: license

* add: tutorials

* fix: support new graph

* fix some comments

* refine

* fix concat op convert bug

* refine

* refine

* change cuda to cpu

* fix bug

* fix ci error in tvm

* fix pylint check

* delete useless file

* add skimage package in docker

* fix ci error

* fix bug

* add oneflow fronted test in ci

* merge conflict

* fix tutorial

* try to find error in ci

* revert

* merge conflict

* black oneflow

* Delete from_oneflow.py

* fix bug when upgrade oneflow to 0.7.0

* add tutorials

* add tutorials

* try to fix

* fix bug

* add test

* fix bug

* fix flowvision bug

* Update test_forward.py

* Update test_forward.py

Co-authored-by: hhhfccz <hjk1938927583@163.com>
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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

TVM is licensed under the Apache-2.0 license.

Getting Started

Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.

Contribute to TVM

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