| commit | 94c1b89abc1f561627ab5e2a152e5ee4c949c580 | [log] [tgz] |
|---|---|---|
| author | Eric Lunderberg <Lunderberg@users.noreply.github.com> | Sun May 28 15:39:23 2023 -0500 |
| committer | GitHub <noreply@github.com> | Mon May 29 05:39:23 2023 +0900 |
| tree | 1ab62311d93f1a5547066af80f8566ace3994d3f | |
| parent | 5f1421dd0f74485bb051ae298311081658195fb9 [diff] |
[TVMScript][TIR] Parse subroutine calls with no arguments (#14919) * [TVMScript][TIR] Parse subroutine calls with no arguments In most cases, the IR dialect in `GlobalVar.__call__` can be inferred from the argument types. If there are no arguments, then the returned value is ambiguous. This commit updates the TIR parser to identify and fix this case of erroneously producing a `relay.Call` instead of `tir.Call`. In addition, to prevent this from re-occuring, an unrecognized type resulting from `def visit_expr_stmt` now results in an error, rather than being silently ignored. * Ignore str for unknown parser result These may are used as docstrings in the TVMScript, even though they are not represented in the TIR. * Lint fixes
Documentation | Contributors | Community | Release Notes
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.
TVM is licensed under the Apache-2.0 license.
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.
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.
We learned a lot from the following projects when building TVM.