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Latest commit

Prior to this commit, the tests in
`test_tir_transform_inject_ptx_async_copy.py` registered the
`"tvm_callback_cuda_postproc"` function during pytest collection, and
used a global variable to disable its functionality outside of the
tests in this file.  This had two major issues.  First, if any other
test also installs a postproc function, these postproc function
required by the NVPTX tests would be overwritten.  Second, if one of
the NTPTX tests fails, the global variable controlling the postproc
function would not be reset, causing any subsequent CUDA-related tests
to also fail.

This commit updates these NVPTX tests to conditionally install the
postproc function, to de-register it after the test instead of
disabling its functionality, and to de-register it regardless of the
test result.

This issue was initially found when debugging
#15103, when a failure in
`test_tir_transform_inject_ptx_async_copy.py::test_cp_async_in_if_then_else`
caused failures in 32 unrelated tests ([CI
link](https://ci.tlcpack.ai/blue/organizations/jenkins/tvm-gpu/detail/PR-15103/7/tests)).
0bb390b

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July 1, 2023 22:36
November 28, 2022 19:35

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.