commit | 6fb08c61c5c49f4e72a8c908d61222332996c8eb | [log] [tgz] |
---|---|---|
author | Lei Zhang <antiagainst@google.com> | Thu Mar 10 07:30:46 2022 -0500 |
committer | GitHub <noreply@github.com> | Thu Mar 10 07:30:46 2022 -0500 |
tree | 9f361bf8378eb3ec40d99e67ecf3f12f69310f18 | |
parent | 1be371788923c1aacb8ad34a4a4d546120c84ba1 [diff] |
[spirv] Fuse tensor.pad ops into their consumer ops (#8049) It's typical for convolutions to require padding. Right now we handle such padding with two separate steps, first performing `vkCmdFillBuffer` to initialize the target buffer and then launch a specific kernel to copy over the source content. These two steps also means two additional pipeline barriers to sequence the execution. This commit instead enables fusion of tensor.pad ops into the consumer ops directly to avoid generating the buffer fill and copy. This is expected to improve performance on mobile GPUs. After fusing the tensor.pad op, we generate the kernel to contain both a fast and a slow path. The fast path are for inner tiles that do not need padding; the slow path is for boundary tiles that do need padding. Both paths are vectorized properly.
IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.
See our website for project details, user guides, and instructions on building from source.
IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!
See our website for more information.
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.