commit | b14aaf486b4408b8bbbd6454b0247b105849a6e4 | [log] [tgz] |
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author | bjacob <benoitjacob@google.com> | Mon Jan 30 12:05:49 2023 -0500 |
committer | GitHub <noreply@github.com> | Mon Jan 30 12:05:49 2023 -0500 |
tree | 08f9c733269bfae1a4df1e2f4ab225716f2a97f5 | |
parent | 5f6f9892b5fe0c4b228e4e659e6205056d580c42 [diff] |
pack microkernel padding improvements (#11987) - More correct: the old code was incorrect for really large padding (test expanded). - Faster: padding is handled with an intermediate buffer, which allows padded tiles to still be packed by the fast target-specific tile func, as opposed to a super slow generic fallback. Moreover, we optimize the common case where the padding byte pattern is a single-byte pattern so we can use memset. Even with that, padding is still surprisingly slow. This means that compiler-level ways to remove padding overhead (pad fusions) are important in a way that is not diminished by the ability to call microkernels. @MaheshRavishankar @hanhanW Note: in ruy and XNNPACK, padding is handled by having separate source pointers for N strided rows being packed (so padding rows can just have their pointer point at some zeros). This does not scale well to the degree of generality and runtime-flexibility that we are aiming for here (this essentially requires knowing some tile dimensions at compile time, and being able to design the whole system around a restricted range of values for these tile sizes). While not the fastest, the intermediate-buffer approach taken here is not that slow in principle (that it happens to be less fast than I hoped seems to be an accident of how it's compiled by Clang, but I haven't been able to dig to the bottom of that), and at least it makes for smaller code and insulates the target-optimized kernels from any padding consideration. At first I thought that in large-stride cases, L1 cache aliasing would be a major issue, so I thought that the temporary buffer would be useful to deal with that. But it almost never was a net win in benchmarks. So the PR evolved towards its current state where the temp buffer is only used to deal with padding. But at least it's still easy to revisit that later.
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.