[Marvell BYOC]: Marvell AI Accelerator Integration - Phase 1

[f2013519]

Summary

This PR adds support for partitioning and compiling the Marvell BYOC target along with initial integration with tvmc. Support for the runtime (simulator based & hardware) and other features (int8) will be added in phases as described in the pre-RFC post: https://discuss.tvm.apache.org/t/prerfc-byoc-integrating-marvell-ml-ai-accelerator-to-the-tvm-byoc-framework/16155.

Please see the pre-RFC for a detailed description of the design and roadmap going forward.

Building

We have introduced a new cmake flag:
USE_MRVL=ON/OFF

This flag enables Marvell BYOC codegen and is required for using the Marvell BYOC functionality, running unit tests, etc.

Usage

The tvmc interface for Marvell BYOC will be similar to other composite targets.

The below command is an example of cross-compilation of an ONNX model for an Octeon target.

python3 -m tvm.driver.tvmc compile
--target="mrvl, llvm"
--target-llvm-mtriple=aarch64-linux-gnu
--target-llvm-mcpu=neoverse-n2
--target-mrvl-num_tiles=4
--cross-compiler aarch64-linux-gnu-gcc
--output model.tar
model.onnx

Supported operators will be partitioned and compiled for the MLIP and the remaining operators will be compiled for the ARM Neoverse cores using the default LLVM target.

TVM Python API based compilation is also supported, please refer to the doc added as part of this PR for details.

[f2013519]

cc @Hzfengsy @vinx13

[Hzfengsy]

LGTM. BTW, as we are moving to TVM Unity / Relax, would be great if you could consider supporting MRVL for unity flow for emerging needs (e.g. LLMs)

[f2013519]

LGTM. BTW, as we are moving to TVM Unity / Relax, would be great if you could consider supporting MRVL for unity flow for emerging needs (e.g. LLMs)

Thanks @Hzfengsy. We are closely following TVM Unity / Relax development and it is part of our future roadmap. We will work on it once we complete the initial Relay flow support.