| commit | 80bcf4cdb702f5d223e8f2a9de367b7be1250b3c | [log] [tgz] |
|---|---|---|
| author | Ruihang Lai <ruihangl@cs.cmu.edu> | Sun Mar 24 08:05:17 2024 -0400 |
| committer | GitHub <noreply@github.com> | Sun Mar 24 08:05:17 2024 -0400 |
| tree | e1514d7b40e78383dbadf3c591c4926590e2f23d | |
| parent | 77a7b010817d2d8fbdf89223bb814e9c38f68365 [diff] |
[Fix][Dlight] (Low-batched-)GeMV on small spatial loops (#16775) This PR fixes an issue in the dlight GeMV rule and the low-batch GeMV rule. The issue happens when the inner spatial loop has small length (e.g., in the MoE gate layer, this length is usually 8). The error is because the GeMV scheduling does not make sure that each TIR block reads/writes the same number of local registers, and this inconsistency leads to wrong generated code. For example, in the schedule (prior to this fix), the first TIR block was scheduled to assign each thread 2 local registers, while the second block was scheduled to assign each thread 1 local register, which is incorrect. Unfortunately, this error only shows up when the spatial loop has small length. One regression test is added.
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.