| commit | 9e98bcdbee19371e04396fd6cfbbc3ab7d4f9930 | [log] [tgz] |
|---|---|---|
| author | bjacob <benoitjacob@google.com> | Mon Feb 07 23:04:28 2022 -0500 |
| committer | GitHub <noreply@github.com> | Mon Feb 07 20:04:28 2022 -0800 |
| tree | 44082ff396ff2ce6d69f0e5dcd42f9715d1fa275 | |
| parent | 3944dbb147477cc5a5d65579437a6d3ca1b7442a [diff] |
Generalize the asm kernel pattern. (#8248)
This makes MMTCustomKernelPattern generic in what kernel it's
generating, in a way that should suffice for the next few kernels that
we need to add (first motivation: float32/aarch64).
MMTCustomKernelPattern now takes the kernel-specific details from a
MMTKernel struct, including the asm code and information about the shape
and data types. The inline asm constraints string becomes
auto-generated.
All this automation had the effect of making it "too magical" for the
inline asm to continue to refer to its register operands by fixed
indices like "$12" as the llvm inline asm syntax allows, so we introduce
a pre-processing step allowing us to write kernels using placeholders
like "$(lhs:0)" for the 0-th register of the LHS, and that gets
"allocated" as the pre-processed code and the constraints string get
co-generated.
This makes for a self-contained way to describe kernels. The current
aarch64 +dotprod i8 kernel becomes:
```
// i8*i8->i32 kernel for Aarch64 NEON +dotprod
MMTKernel MMTKernel_8x4x8_i8i8i32_Aarch64Dotprod_InlineAsm() {
MMTKernel kernel;
kernel.arch = CustomKernelTargetArch::Aarch64;
kernel.m0 = 8;
kernel.k0 = 4;
kernel.n0 = 8;
kernel.lhsType = MMTKernel::ScalarType::I8;
kernel.rhsType = MMTKernel::ScalarType::I8;
kernel.accType = MMTKernel::ScalarType::I32;
kernel.registerBitWidth = PowerOfTwo(128);
kernel.implAsm = R"ASM(
sdot $(acc:0).4s, $(rhs:0).16b, $(lhs:0).4b[0]
sdot $(acc:1).4s, $(rhs:1).16b, $(lhs:0).4b[0]
sdot $(acc:2).4s, $(rhs:0).16b, $(lhs:0).4b[1]
sdot $(acc:3).4s, $(rhs:1).16b, $(lhs:0).4b[1]
sdot $(acc:4).4s, $(rhs:0).16b, $(lhs:0).4b[2]
sdot $(acc:5).4s, $(rhs:1).16b, $(lhs:0).4b[2]
sdot $(acc:6).4s, $(rhs:0).16b, $(lhs:0).4b[3]
sdot $(acc:7).4s, $(rhs:1).16b, $(lhs:0).4b[3]
sdot $(acc:8).4s, $(rhs:0).16b, $(lhs:1).4b[0]
sdot $(acc:9).4s, $(rhs:1).16b, $(lhs:1).4b[0]
sdot $(acc:10).4s, $(rhs:0).16b, $(lhs:1).4b[1]
sdot $(acc:11).4s, $(rhs:1).16b, $(lhs:1).4b[1]
sdot $(acc:12).4s, $(rhs:0).16b, $(lhs:1).4b[2]
sdot $(acc:13).4s, $(rhs:1).16b, $(lhs:1).4b[2]
sdot $(acc:14).4s, $(rhs:0).16b, $(lhs:1).4b[3]
sdot $(acc:15).4s, $(rhs:1).16b, $(lhs:1).4b[3]
)ASM";
return kernel;
}
```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.