commit | ac9a943c4dd45cb98c5801631450fd9bb44e7804 | [log] [tgz] |
---|---|---|
author | Andrei Hutu <andrei.hutu@arm.com> | Wed May 22 11:01:02 2024 +0100 |
committer | GitHub <noreply@github.com> | Wed May 22 11:01:02 2024 +0100 |
tree | 32a0a7b783aa454693139730f06ae64747a90162 | |
parent | a5862a5c696a3237f644f31bc312aae303213f3f [diff] |
[TOPI][Testing] Enable conv2d NHWC fp16 topi testing for `arm_cpu` (#17007) This commit adds fp16 test cases to the conv2d NHWC TOPI schedules for `arm_cpu`. Following the example of #8529, the numpy reference conv2d output is computed in fp32 instead of fp16, while the absolute tolerance varies for each test case according to the size of the summed axis and the output's largest element.
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.