Add an initial wasi-nn implementation for Wasmtime (#2208)

* Add an initial wasi-nn implementation for Wasmtime

This change adds a crate, `wasmtime-wasi-nn`, that uses `wiggle` to expose the current state of the wasi-nn API and `openvino` to implement the exposed functions. It includes an end-to-end test demonstrating how to do classification using wasi-nn:
 - `crates/wasi-nn/tests/classification-example` contains Rust code that is compiled to the `wasm32-wasi` target and run with a Wasmtime embedding that exposes the wasi-nn calls
 - the example uses Rust bindings for wasi-nn contained in `crates/wasi-nn/tests/wasi-nn-rust-bindings`; this crate contains code generated by `witx-bindgen` and eventually should be its own standalone crate

* Test wasi-nn as a CI step

This change adds:
 - a GitHub action for installing OpenVINO
 - a script, `ci/run-wasi-nn-example.sh`, to run the classification example

ci/run-experimental-x64-ci.sh

@@ -13,6 +13,7 @@ cargo $CARGO_VERSION \
             --features experimental_x64 \
             --all \
             --exclude wasmtime-lightbeam \
+            --exclude wasmtime-wasi-nn \
             --exclude peepmatic \
             --exclude peepmatic-automata \
             --exclude peepmatic-fuzzing \

ci/run-wasi-nn-example.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+
+# The following script demonstrates how to execute a machine learning inference using the wasi-nn module optionally
+# compiled into Wasmtime. Calling it will download the necessary model and tensor files stored separately in $FIXTURE
+# into $TMP_DIR (optionally pass a directory with existing files as the first argument to re-try the script). Then,
+# it will compile the example code in crates/wasi-nn/tests/example into a Wasm file that is subsequently
+# executed with the Wasmtime CLI.
+set -e
+WASMTIME_DIR=$(dirname "$0" | xargs dirname)
+FIXTURE=https://gist.github.com/abrown/c7847bf3701f9efbb2070da1878542c1/raw/07a9f163994b0ff8f0d7c5a5c9645ec3d8b24024
+
+# Inform the environment of OpenVINO library locations. Then we use OPENVINO_INSTALL_DIR below to avoid building all of
+# OpenVINO from source (quite slow).
+source /opt/intel/openvino/bin/setupvars.sh
+
+# Build Wasmtime with wasi-nn enabled; we attempt this first to avoid extra work if the build fails.
+OPENVINO_INSTALL_DIR=/opt/intel/openvino cargo build -p wasmtime-cli --features wasi-nn
+
+# Download all necessary test fixtures to the temporary directory.
+TMP_DIR=${1:-$(mktemp -d -t ci-XXXXXXXXXX)}
+wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/frozen_inference_graph.bin
+wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/frozen_inference_graph.xml
+wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/tensor-1x3x300x300-f32.bgr
+
+# Now build an example that uses the wasi-nn API.
+pushd $WASMTIME_DIR/crates/wasi-nn/examples/classification-example
+cargo build --release --target=wasm32-wasi
+cp target/wasm32-wasi/release/wasi-nn-example.wasm $TMP_DIR
+popd
+
+# Run the example in Wasmtime (note that the example uses `fixture` as the expected location of the model/tensor files).
+OPENVINO_INSTALL_DIR=/opt/intel/openvino cargo run --features wasi-nn -- run --mapdir fixture::$TMP_DIR $TMP_DIR/wasi-nn-example.wasm
+
+# Clean up.
+rm -rf $TMP_DIR