[GitHub] [incubator-tvm] leonwanghui commented on a change in pull request #5892: Add TVM application extension with WASM runtime

[GitBox <gi...@apache.org>]

leonwanghui commented on a change in pull request #5892:
URL: https://github.com/apache/incubator-tvm/pull/5892#discussion_r452590922



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File path: apps/wasm-graphcompiler-tvm/README.md
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+# WebAssembly GraphCompiler for Deep Learning Framework with TVM Runtime
+
+#### Experimental notice: This project is still *experimental* and only serves as a proof of concept for running deep learning frameworks (such like [MindSpore](https://github.com/mindspore-ai/mindspore)) on [WebAssembly runtime](https://github.com/bytecodealliance/wasmtime) with [TVM stack](https://tvm.apache.org/).
+
+- [WebAssembly GraphCompiler for Deep Learning Framework with TVM Runtime](#webassembly-graphcompiler-for-deep-learning-framework-with-tvm-runtime)
+    - [Motivation](#motivation)
+    - [Framework Landscape](#framework-landscape)
+    - [Project Status](#project-status)
+    - [PoC Guidelines](#poc-guidelines)
+        - [Pre-installation](#pre-installation)
+        - [Build ResNet50 model](#build-resnet50-model)
+        - [Build wasm-graphcompiler-tvm package](#build-wasm-graphcompiler-tvm-package)
+        - [Test](#test)
+    - [Future Work](#future-work)
+        - [More networks support](#more-networks-support)
+        - [Performance benchmark](#performance-benchmark)
+        - [Native TVM Rust runtime support](#native-tvm-rust-runtime-support)
+    - [Appendix](#appendix)
+        - [System packages install](#system-packages-install)
+    - [Contribution](#contribution)
+
+## Motivation
+
+<img src="https://github.com/dmlc/web-data/raw/master/tvm/tutorial/tvm_support_list.png" alt="TVM hardware support" width="600"/>
+
+As demonstrated in TVM runtime [tutorials](https://tvm.apache.org/docs/tutorials/relay_quick_start.html), TVM already supports WASM as the optional hardware backend, so we can leverage the features of WebAssembly (portability, security) and TVM runtime (domain-specific, optimization) to build a flexible and auto-optimized graph compiler for all deep learning frameworks.
+
+## Framework Landscape
+
+The figures below demonstrate the whole landscape of running deep learning frameworks on WASM runtime with TVM compiler stack.
+
+* WASM graph compiler stack
+    ```
+       _ _ _ _ _ _ _ _ _ _        _ _ _ _ _ _ _        _ _ _ _ _ _ _ _ _ _ _ _
+      |                   |      |             |      |                       |
+      |  Framework Model  | ---> |  ONNX Model | ---> |  TVM Relay Python API |
+      |_ _ _ _ _ _ _ _ _ _|      |_ _ _ _ _ _ _|      |_ _ _ _ _ _ _ _ _ _ _ _|
+                                                                 ||
+                                                                 \/
+                 _ _ _ _ _ _ _ _ _ _ _                  _ _ _ _ _ _ _ _ _ _ _
+                |                     |                |                     |
+                | WASM Graph Compiler |                |  TVM Compiler Stack |
+                |    (TVM runtime)    |                |_ _ _ _ _ _ _ _ _ _ _|

Review comment:
       Please notice that the "graph compiler" module is just a wrapper of TVM rust runtime, I need to compile tvm runtime into wasm code so it can be loaded with `wasmtime`. Is that ok to you?




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