Repository: second-state/WasmEdge-WASINN-examples Branch: master Commit: ae14392ea47c Files: 189 Total size: 52.4 MB Directory structure: gitextract_91qb3w5n/ ├── .github/ │ └── workflows/ │ ├── build_openvino_mobilenet.yml │ ├── build_openvino_road_seg_adas.yml │ ├── build_pytorch_yolo.yml │ ├── chatTTS.yml │ ├── llama.yml │ ├── piper.yml │ ├── pytorch.yml │ └── tflite.yml ├── .gitignore ├── LICENSE ├── README.md ├── openvino-mobilenet-image/ │ ├── README.md │ ├── download_mobilenet.sh │ ├── rust/ │ │ ├── Cargo.toml │ │ └── src/ │ │ ├── imagenet_classes.rs │ │ └── main.rs │ └── wasmedge-wasinn-example-mobilenet-image.wasm ├── openvino-mobilenet-raw/ │ ├── README.md │ ├── download_mobilenet.sh │ ├── rust/ │ │ ├── Cargo.toml │ │ └── src/ │ │ ├── imagenet_classes.rs │ │ └── main.rs │ └── wasmedge-wasinn-example-mobilenet.wasm ├── openvino-road-segmentation-adas/ │ ├── README.md │ ├── model/ │ │ └── road-segmentation-adas-0001.xml │ ├── openvino-road-seg-adas/ │ │ ├── Cargo.toml │ │ └── src/ │ │ └── main.rs │ ├── tensor/ │ │ ├── wasinn-openvino-inference-input-512x896x3xf32-bgr.tensor │ │ └── wasinn-openvino-inference-output-1x4x512x896xf32.tensor │ └── visualize_inference_result.ipynb ├── openvinogenai-raw/ │ ├── README.md │ └── rust/ │ ├── Cargo.toml │ └── src/ │ └── main.rs ├── pytorch-mobilenet-image/ │ ├── README.md │ ├── gen_mobilenet_model.py │ ├── gen_tensor.py │ ├── image-1x3x224x224.rgb │ ├── mobilenet.pt │ ├── rust/ │ │ ├── Cargo.toml │ │ └── src/ │ │ ├── imagenet_classes.rs │ │ ├── main.rs │ │ └── named_model.rs │ ├── wasmedge-wasinn-example-mobilenet-image-named-model.wasm │ └── wasmedge-wasinn-example-mobilenet-image.wasm ├── pytorch-yolo-image/ │ ├── README.md │ ├── get_model.py │ ├── rust/ │ │ ├── Cargo.toml │ │ └── src/ │ │ ├── main.rs │ │ └── yolo_classes.rs │ └── yolov8n.torchscript ├── scripts/ │ ├── install_libtorch.sh │ └── install_openvino.sh ├── tflite-birds_v1-image/ │ ├── README.md │ ├── lite-model_aiy_vision_classifier_birds_V1_3.tflite │ ├── rust/ │ │ ├── Cargo.toml │ │ └── src/ │ │ ├── imagenet_classes.rs │ │ └── main.rs │ └── wasmedge-wasinn-example-tflite-bird-image.wasm ├── wasmedge-chatTTS/ │ ├── .gitignore │ ├── Cargo.toml │ ├── README.md │ ├── assets/ │ │ └── demo.webm │ ├── src/ │ │ └── main.rs │ └── wasmedge-chattts.wasm ├── wasmedge-ggml/ │ ├── README.md │ ├── basic/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-basic.wasm │ ├── chatml/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-chatml.wasm │ ├── command-r/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-command-r.wasm │ ├── embedding/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-llama-embedding.wasm │ ├── gemma/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-gemma.wasm │ ├── gemma-3/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ ├── base64.rs │ │ │ └── main.rs │ │ ├── wasmedge-ggml-gemma-3-base64.wasm │ │ └── wasmedge-ggml-gemma-3.wasm │ ├── gemma-4/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-gemma-4.wasm │ ├── grammar/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-grammar.wasm │ ├── json-schema/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-json-schema.wasm │ ├── llama/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-llama.wasm │ ├── llama-stream/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-llama-stream.wasm │ ├── llava/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-llava.wasm │ ├── llava-base64-stream/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-llava-base64-stream.wasm │ ├── multimodel/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-multimodel.wasm │ ├── nnrpc/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-nnrpc.wasm │ ├── qwen/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ └── src/ │ │ └── main.rs │ ├── qwen2vl/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-qwen2vl.wasm │ ├── test/ │ │ ├── model-not-found/ │ │ │ ├── Cargo.toml │ │ │ ├── README.md │ │ │ ├── src/ │ │ │ │ └── main.rs │ │ │ └── wasmedge-ggml-model-not-found.wasm │ │ ├── phi-3/ │ │ │ ├── Cargo.toml │ │ │ ├── README.md │ │ │ ├── src/ │ │ │ │ └── main.rs │ │ │ └── wasmedge-ggml-phi-3.wasm │ │ ├── set-input-twice/ │ │ │ ├── Cargo.toml │ │ │ ├── README.md │ │ │ ├── src/ │ │ │ │ └── main.rs │ │ │ └── wasmedge-ggml-set-input-twice.wasm │ │ └── unload/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-unload.wasm │ ├── tts/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── src/ │ │ │ └── main.rs │ │ └── wasmedge-ggml-tts.wasm │ └── whisper/ │ ├── Cargo.toml │ ├── README.md │ ├── src/ │ │ └── main.rs │ └── whisper-basic.wasm ├── wasmedge-mlx/ │ ├── llama/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ └── src/ │ │ └── main.rs │ ├── vlm/ │ │ ├── Cargo.toml │ │ ├── README.md │ │ ├── decode.py │ │ ├── encode.py │ │ └── src/ │ │ └── main.rs │ └── whisper/ │ ├── Cargo.toml │ ├── README.md │ └── src/ │ └── main.rs ├── wasmedge-piper/ │ ├── Cargo.toml │ ├── README.md │ ├── config.schema.json │ ├── dependencies.d2 │ ├── json_input.schema.json │ └── src/ │ └── main.rs ├── wasmedge-tf-llama/ │ ├── README.md │ └── rust/ │ ├── Cargo.toml │ └── src/ │ └── main.rs ├── wasmedge-tf-mobilenet_v2/ │ ├── README.md │ ├── imagenet_slim_labels.txt │ ├── mobilenet_v2_1.4_224_frozen.pb │ ├── rust/ │ │ ├── Cargo.toml │ │ └── src/ │ │ └── main.rs │ └── wasmedge-tf-example-mobilenet.wasm └── wasmedge-tf-mtcnn/ ├── README.md ├── mtcnn.pb ├── rust/ │ ├── Cargo.toml │ └── src/ │ └── main.rs └── wasmedge-tf-example-mtcnn.wasm ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/workflows/build_openvino_mobilenet.yml ================================================ name: OpenVINO Mobilenet Example on: schedule: - cron: "0 0 * * *" push: branches: [master] paths: - ".github/workflows/build_openvino_mobilenet.yml" - "openvino-mobilenet-raw/**" - "openvino-mobilenet-image/**" - "scripts/install_openvino.sh" pull_request: branches: [master] paths: - ".github/workflows/build_openvino_mobilenet.yml" - "openvino-mobilenet-raw/**" - "openvino-mobilenet-image/**" - "scripts/install_openvino.sh" merge_group: env: CARGO_TERM_COLOR: always jobs: build_openvino_examples: runs-on: ubuntu-latest strategy: matrix: rust: [1.84] container: image: wasmedge/wasmedge:ubuntu-build-clang steps: - name: Checkout Wasi-NN examples uses: actions/checkout@v3 with: fetch-depth: 0 - name: Install Rust-stable uses: dtolnay/rust-toolchain@stable with: toolchain: ${{ matrix.rust }} target: wasm32-wasip1 - name: Install dependencies run: | apt update apt install -y libtbbmalloc2 - name: Install OpenVINO working-directory: scripts run: | bash install_openvino.sh - name: Install WasmEdge with Wasi-NN OpenVINO plugin env: CMAKE_BUILD_TYPE: "Release" VERSION: "0.14.1" run: | curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino - name: Build and run openvino-mobilenet-raw working-directory: openvino-mobilenet-raw run: | bash download_mobilenet.sh cd rust cargo build --target wasm32-wasip1 --release cd .. wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet.wasm mobilenet.xml mobilenet.bin tensor-1x224x224x3-f32.bgr - name: Build and run openvino-mobilenet-image working-directory: openvino-mobilenet-image run: | bash download_mobilenet.sh cd rust cargo build --target wasm32-wasip1 --release cd .. wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm mobilenet.xml mobilenet.bin input.jpg ================================================ FILE: .github/workflows/build_openvino_road_seg_adas.yml ================================================ name: OpenVINO Road Segmentation ADAS Example on: schedule: - cron: "0 0 * * *" push: branches: [master] paths: - ".github/workflows/build_openvino_road_seg_adas.yml" - "openvino-road-segmentation-adas/**" - "scripts/install_openvino.sh" pull_request: branches: [master] paths: - ".github/workflows/build_openvino_road_seg_adas.yml" - "openvino-road-segmentation-adas/**" - "scripts/install_openvino.sh" merge_group: env: CARGO_TERM_COLOR: always jobs: build_openvino_examples: runs-on: ubuntu-latest strategy: matrix: rust: [1.84] container: image: wasmedge/wasmedge:ubuntu-build-clang steps: - name: Checkout Wasi-NN examples uses: actions/checkout@v3 with: fetch-depth: 0 - name: Install Rust-stable uses: dtolnay/rust-toolchain@stable with: toolchain: ${{ matrix.rust }} target: wasm32-wasip1 - name: Install dependencies run: | apt update apt install -y libtbbmalloc2 - name: Install OpenVINO working-directory: scripts run: | bash install_openvino.sh - name: Install WasmEdge with Wasi-NN OpenVINO plugin env: CMAKE_BUILD_TYPE: "Release" VERSION: "0.14.1" run: | curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino - name: Build and run openvino-road-segmentation-adas working-directory: openvino-road-segmentation-adas run: | cd openvino-road-seg-adas cargo build --target=wasm32-wasip1 --release cp target/wasm32-wasip1/release/openvino-road-seg-adas.wasm .. cd .. wasmedge --dir .:. openvino-road-seg-adas.wasm ./model/road-segmentation-adas-0001.xml ./model/road-segmentation-adas-0001.bin ./image/empty_road_mapillary.jpg ================================================ FILE: .github/workflows/build_pytorch_yolo.yml ================================================ name: Pytorch Yolo Detection on: schedule: - cron: "0 0 * * *" push: branches: ['*'] paths: - ".github/workflows/build_pytorch_yolo.yml" - "pytorch-yolo-image/**" - "scripts/install_libtorch.sh" pull_request: branches: ['*'] paths: - ".github/workflows/build_pytorch_yolo.yml" - "pytorch-yolo-image/**" - "scripts/install_libtorch.sh" merge_group: env: CARGO_TERM_COLOR: always jobs: build_pytorch_examples: runs-on: ubuntu-latest strategy: matrix: rust: [1.84] container: image: wasmedge/wasmedge:ubuntu-build-clang steps: - name: Checkout Wasi-NN examples uses: actions/checkout@v3 with: fetch-depth: 0 - name: Install Rust-stable uses: dtolnay/rust-toolchain@stable with: toolchain: ${{ matrix.rust }} target: wasm32-wasip1 - name: Install LibTorch working-directory: scripts run: | set -e bash install_libtorch.sh cp ./libtorch/lib/* /lib/ - name: Install WasmEdge env: CMAKE_BUILD_TYPE: "Release" VERSION: "0.13.4" run: | curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION --plugins wasi_nn-pytorch -p /usr/local - name: Build and run pytorch-yolo-detection run: | cd pytorch-yolo-image cd rust cargo build --target=wasm32-wasip1 --release cp target/wasm32-wasip1/release/wasmedge-wasinn-example-yolo-image.wasm .. cd .. wasmedge --dir .:. wasmedge-wasinn-example-yolo-image.wasm ./yolov8n.torchscript ./input.jpg ================================================ FILE: .github/workflows/chatTTS.yml ================================================ name: ChatTTS example on: schedule: - cron: "0 0 * * *" push: paths: - ".github/workflows/chatTTS.yml" - "wasmedge-chatTTS/**" pull_request: paths: - ".github/workflows/chatTTS.yml" - "wasmedge-chatTTS/**" merge_group: jobs: build: runs-on: ubuntu-22.04 steps: - name: Install Dependencies for building WasmEdge run: | sudo apt-get -y update sudo apt-get -y install wget git curl software-properties-common build-essential python3 python3-dev python3-pip ninja-build pip install chattts==0.1.1 - name: Install Rust target for wasm run: | rustup target add wasm32-wasip1 - name: Checkout WasmEdge uses: actions/checkout@v4 with: repository: WasmEdge/WasmEdge path: WasmEdge - name: Build WasmEdge with WASI-NN ChatTTS plugin run: | cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_USE_LLVM=OFF -DWASMEDGE_PLUGIN_WASI_NN_BACKEND=ChatTTS cmake --build build working-directory: WasmEdge - name: Checkout WasmEdge-WASINN-examples uses: actions/checkout@v4 with: path: WasmEdge-WASINN-examples - name: Build wasm run: cargo build --target wasm32-wasip1 --release working-directory: WasmEdge-WASINN-examples/wasmedge-chatTTS - name: Execute run: WASMEDGE_PLUGIN_PATH=WasmEdge/build/plugins/wasi_nn WasmEdge/build/tools/wasmedge/wasmedge --dir .:. WasmEdge-WASINN-examples/wasmedge-chatTTS/target/wasm32-wasip1/release/wasmedge-chattts.wasm - name: Verify output run: test "$(file --brief output1.wav)" == 'RIFF (little-endian) data, WAVE audio, mono 24000 Hz' ================================================ FILE: .github/workflows/llama.yml ================================================ name: ggml llama2 examples on: schedule: - cron: "0 0 * * *" workflow_dispatch: inputs: logLevel: description: 'Log level' required: true default: 'info' push: branches: [ '*' ] paths: - ".github/workflows/llama.yml" - "wasmedge-ggml/**" pull_request: branches: [ '*' ] paths: - ".github/workflows/llama.yml" - "wasmedge-ggml/**" merge_group: jobs: build: strategy: matrix: runner: [ubuntu-latest] wasmedge: ["0.14.1"] plugin: [wasi_nn-ggml] job: - name: Gemma 2B shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/gemma curl -LO https://huggingface.co/second-state/Gemma-2b-it-GGUF/resolve/main/gemma-2b-it-Q5_K_M.gguf cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --env n_gpu_layers="$NGL" \ --nn-preload default:GGML:AUTO:gemma-2b-it-Q5_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-gemma.wasm \ default \ 'user Where is the capital of Japan? model' - name: Llama3 8B shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/llama curl -LO https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct.Q5_K_M.gguf cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --env n_gpu_layers="$NGL" \ --env llama3=true \ --nn-preload default:GGML:AUTO:Meta-Llama-3-8B-Instruct.Q5_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-llama.wasm \ default \ $"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you do not know the answer to a question, please do not share false information.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\nWhat's the capital of Japan?<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n" - name: Llama3 8B (Streaming) shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/llama-stream curl -LO https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/resolve/main/Meta-Llama-3-8B-Instruct.Q5_K_M.gguf cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --env n_gpu_layers="$NGL" \ --env llama3=true \ --nn-preload default:GGML:AUTO:Meta-Llama-3-8B-Instruct.Q5_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-llama-stream.wasm \ default \ $"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you do not know the answer to a question, please do not share false information.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\nWhat's the capital of Japan?<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n" - name: Embedding Example (All-MiniLM) shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/embedding curl -LO https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/resolve/main/all-MiniLM-L6-v2-ggml-model-f16.gguf cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-llama-embedding.wasm \ default \ 'hello world' - name: RPC Example shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/nnrpc curl -LO https://huggingface.co/second-state/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --env n_gpu_layers="$NGL" \ --nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-nnrpc.wasm \ default \ $'[INST] <>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you do not know the answer to a question, please do not share false information.\n<>\nWhat is the capital of Japan?[/INST]' - name: Set Input Twice shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/test/set-input-twice curl -LO https://huggingface.co/second-state/Gemma-2b-it-GGUF/resolve/main/gemma-2b-it-Q5_K_M.gguf cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --env n_gpu_layers="$NGL" \ --nn-preload default:GGML:AUTO:gemma-2b-it-Q5_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-set-input-twice.wasm \ default \ 'user Where is the capital of Japan? model' - name: Model Not Found shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/test/model-not-found cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:model-not-found.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-model-not-found.wasm \ default - name: Unload shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/test/unload curl -LO https://huggingface.co/second-state/Gemma-2b-it-GGUF/resolve/main/gemma-2b-it-Q5_K_M.gguf cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:gemma-2b-it-Q5_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-unload.wasm \ default \ 'user Where is the capital of Japan? model' - name: JSON Schema shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/json-schema curl -LO https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --env n_gpu_layers="$NGL" \ --nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-json-schema.wasm \ default \ $'[INST] <>\nYou are a helpful, respectful and honest assistant. Always output JSON format string.\n<>\nGive me a JSON array of Apple products.[/INST]' - name: Gemma-3 Vision shell: bash run: | test -f ~/.wasmedge/env && source ~/.wasmedge/env cd wasmedge-ggml/gemma-3 curl -LO https://huggingface.co/second-state/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-Q5_K_M.gguf curl -LO https://huggingface.co/second-state/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-mmproj-f16.gguf curl -LO https://llava-vl.github.io/static/images/monalisa.jpg cargo build --target wasm32-wasip1 --release time wasmedge --dir .:. \ --env n_gpu_layers="$NGL" \ --env image=monalisa.jpg \ --env mmproj=gemma-3-4b-it-mmproj-f16.gguf \ --nn-preload default:GGML:AUTO:gemma-3-4b-it-Q5_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-gemma-3.wasm \ default \ $'user\nDescribe this image\nmodel\n' - name: Build llama-stream run: | cd wasmedge-ggml/llama-stream cargo build --target wasm32-wasip1 --release - name: Build llava-base64-stream run: | cd wasmedge-ggml/llava-base64-stream cargo build --target wasm32-wasip1 --release name: ${{ matrix.runner == 'ubuntu-latest' && 'ubuntu:20.04' || matrix.runner }} - ${{ matrix.job.name }} - ${{ matrix.wasmedge }} - ${{ matrix.plugin }} runs-on: ${{ matrix.runner }} # set image to `ubuntu:20.04` if runner is `ubuntu-latest` container: ${{ matrix.runner == 'ubuntu-latest' && fromJSON('{"image":"ubuntu:20.04"}') || null }} steps: - uses: actions/checkout@v4 - if: ${{ matrix.runner == 'ubuntu-latest' }} name: Install apt-get packages run: | ACCEPT_EULA=Y apt-get update ACCEPT_EULA=Y apt-get upgrade -y apt-get install -y wget git curl software-properties-common build-essential env: DEBIAN_FRONTEND: noninteractive - name: Install Rust target for wasm uses: dtolnay/rust-toolchain@stable with: target: wasm32-wasip1 - name: Install WasmEdge + WASI-NN + GGML run: | curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v ${{ matrix.wasmedge }} --plugins ${{ matrix.plugin }} - name: Set environment variable run: echo "NGL=${{ matrix.ngl || 0 }}" >> $GITHUB_ENV - name: ${{ matrix.job.name }} run: ${{ matrix.job.run }} shell: bash ================================================ FILE: .github/workflows/piper.yml ================================================ name: Piper Example on: schedule: - cron: "0 0 * * *" push: paths: - ".github/workflows/piper.yml" - "wasmedge-piper/**" pull_request: paths: - ".github/workflows/piper.yml" - "wasmedge-piper/**" merge_group: jobs: build: runs-on: ubuntu-22.04 steps: - name: Install Dependencies for building WasmEdge run: | sudo apt-get update sudo apt-get install ninja-build - name: Checkout WasmEdge uses: actions/checkout@v4 with: repository: WasmEdge/WasmEdge path: WasmEdge - name: Install ONNX Runtime run: sudo bash utils/wasi-nn/install-onnxruntime.sh working-directory: WasmEdge - name: Build WasmEdge with WASI-NN Piper plugin run: | cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_USE_LLVM=OFF -DWASMEDGE_PLUGIN_WASI_NN_BACKEND=Piper cmake --build build working-directory: WasmEdge - name: Install Rust target for wasm run: rustup target add wasm32-wasip1 - name: Checkout WasmEdge-WASINN-examples uses: actions/checkout@v4 with: path: WasmEdge-WASINN-examples - name: Build wasm run: cargo build --target wasm32-wasip1 --release working-directory: WasmEdge-WASINN-examples/wasmedge-piper - name: Download model run: curl -LO https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx - name: Download config run: curl -LO https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx.json - name: Download espeak-ng-data run: | curl -LO https://github.com/rhasspy/piper/releases/download/2023.11.14-2/piper_linux_x86_64.tar.gz tar -xzf piper_linux_x86_64.tar.gz piper/espeak-ng-data --strip-components=1 rm piper_linux_x86_64.tar.gz - name: Execute run: WASMEDGE_PLUGIN_PATH=WasmEdge/build/plugins/wasi_nn WasmEdge/build/tools/wasmedge/wasmedge --dir .:. WasmEdge-WASINN-examples/wasmedge-piper/target/wasm32-wasip1/release/wasmedge-piper.wasm - name: Verify output run: test "$(file --brief welcome.wav)" == 'RIFF (little-endian) data, WAVE audio, Microsoft PCM, 16 bit, mono 22050 Hz' ================================================ FILE: .github/workflows/pytorch.yml ================================================ name: PyTorch examples on: schedule: - cron: "0 0 * * *" workflow_dispatch: inputs: logLevel: description: 'Log level' required: true default: 'info' push: branches: [ '*' ] paths: - ".github/workflows/pytorch.yml" - "pytorch-mobilenet-image/**" pull_request: branches: [ '*' ] paths: - ".github/workflows/pytorch.yml" - "pytorch-mobilenet-image/**" merge_group: jobs: build: runs-on: ubuntu-latest container: image: ubuntu:20.04 steps: - uses: actions/checkout@v4 - name: Install apt-get packages run: | ACCEPT_EULA=Y apt-get update ACCEPT_EULA=Y apt-get upgrade -y apt-get install -y wget git curl software-properties-common build-essential unzip env: DEBIAN_FRONTEND: noninteractive - name: Install Rust target for wasm uses: dtolnay/rust-toolchain@stable with: target: wasm32-wasip1 - name: Install WasmEdge + WASI-NN + PyTorch run: | VERSION=0.13.4 curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION --plugins wasi_nn-pytorch -p /usr/local export PYTORCH_VERSION="1.8.2" # For the Ubuntu 20.04 or above, use the libtorch with cxx11 abi. export PYTORCH_ABI="libtorch-cxx11-abi" curl -s -L -O --remote-name-all https://download.pytorch.org/libtorch/lts/1.8/cpu/${PYTORCH_ABI}-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip unzip -q "${PYTORCH_ABI}-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip" rm -f "${PYTORCH_ABI}-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip" export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$(pwd)/libtorch/lib - name: Example run: | export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$(pwd)/libtorch/lib cd pytorch-mobilenet-image/rust cargo build --target wasm32-wasip1 --release cd .. wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm wasmedge-wasinn-example-mobilenet-image-aot.wasm wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image-named-model.wasm wasmedge-wasinn-example-mobilenet-image-named-model-aot.wasm echo "Run without named model" wasmedge --dir .:. wasmedge-wasinn-example-mobilenet-image-aot.wasm mobilenet.pt input.jpg echo "Run with named model" wasmedge --dir .:. --nn-preload demo:PyTorch:CPU:mobilenet.pt wasmedge-wasinn-example-mobilenet-image-named-model-aot.wasm demo input.jpg ================================================ FILE: .github/workflows/tflite.yml ================================================ name: TFlite examples on: schedule: - cron: "0 0 * * *" workflow_dispatch: inputs: logLevel: description: 'Log level' required: true default: 'info' push: branches: [ '*' ] paths: - ".github/workflows/tflite.yml" - "tflite-birds_v1-image/**" pull_request: branches: [ '*' ] paths: - ".github/workflows/tflite.yml" - "tflite-birds_v1-image/**" merge_group: jobs: build: runs-on: ubuntu-latest container: image: ubuntu:20.04 steps: - uses: actions/checkout@v4 - name: Install apt-get packages run: | ACCEPT_EULA=Y apt-get update ACCEPT_EULA=Y apt-get upgrade -y apt-get install -y wget git curl software-properties-common build-essential env: DEBIAN_FRONTEND: noninteractive - name: Install Rust target for wasm uses: dtolnay/rust-toolchain@stable with: target: wasm32-wasip1 - name: Install WasmEdge + WASI-NN + TFLite run: | VERSION=0.13.4 TFVERSION=2.12.0 curl -s -L -O --remote-name-all https://github.com/second-state/WasmEdge-tensorflow-deps/releases/download/TF-2.12.0-CC/WasmEdge-tensorflow-deps-TFLite-TF-$TFVERSION-CC-manylinux2014_x86_64.tar.gz tar -zxf WasmEdge-tensorflow-deps-TFLite-TF-$TFVERSION-CC-manylinux2014_x86_64.tar.gz rm -f WasmEdge-tensorflow-deps-TFLite-TF-$TFVERSION-CC-manylinux2014_x86_64.tar.gz mv libtensorflowlite_c.so /usr/local/lib mv libtensorflowlite_flex.so /usr/local/lib curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION --plugins wasi_nn-tensorflowlite -p /usr/local - name: Example run: | cd tflite-birds_v1-image/rust cargo build --target wasm32-wasip1 --release cd .. wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-tflite-bird-image.wasm wasmedge-wasinn-example-tflite-bird-image.wasm wasmedge --dir .:. wasmedge-wasinn-example-tflite-bird-image.wasm lite-model_aiy_vision_classifier_birds_V1_3.tflite bird.jpg ================================================ FILE: .gitignore ================================================ **/build **/target **/*.gguf openvino-mobilenet-image/mobilenet.bin openvino-mobilenet-image/mobilenet.xml openvino-mobilenet-raw/mobilenet.bin openvino-mobilenet-raw/mobilenet.xml openvino-mobilenet-raw/tensor-1x224x224x3-f32.bgr pytorch-yolo-image/*.pt pytorch-yolo-image/*.onnx wasmedge-ggml-llama/llama-2-7b-chat.ggmlv3.q4_0.bin .DS_Store Cargo.lock ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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WasmEdge WASI-NN Examples

High-level bindings for writing wasi-nn applications

CI status - llama CI status - pytorch CI status - tflite

### Introduction This project provides the examples of high-level [wasi-nn] bindings and WasmEdge-TensorFlow plug-ins on Rust programming language. Developers can refer to this project to write their machine learning application in a high-level language using the bindings, compile it to WebAssembly, and run it with a WebAssembly runtime that supports the [wasi-nn] proposal, such as [WasmEdge]. ### Prerequisites #### OpenVINO Installation Developers should install the [OpenVINO] first before build and run WasmEdge with wasi-nn and the examples. For this project, we use the version `2023.0.0`. Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plugin/wasi_nn) and [OpenVINO™](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_apt.html)(2023) ```bash wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB echo "deb https://apt.repos.intel.com/openvino/2023 ubuntu20 main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2023.list sudo apt update sudo apt-get -y install openvino ldconfig ``` [OpenVINO]: https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html #### Rust Installation For building the WASM files from rust source, please refer to the [Rust Official Site](https://www.rust-lang.org/tools/install) for the Rust installation. After the installation, developers should add the `wasm32-wasip1` target. ```bash rustup target add wasm32-wasip1 ``` #### Download the `wasi-nn` Rust Crate In Rust, download the [crate from crates.io](https://crates.io/crates/wasi-nn) by adding `wasi-nn = "0.4.0"` as a Cargo dependency. For using WasmEdge-TensorFlow plug-ins, please download the [crate from crates.io](https://crates.io/crates/wasmedge_tensorflow_interface) by adding `wasmedge_tensorflow_interface = "0.3.0"` as a Cargo dependency. #### WasmEdge Installation You can refer to [here to install WasmEdge](https://wasmedge.org/docs/start/install#install). For the examples with different wasi-nn backends or using the WasmEdge-Tensorflow plug-ins, please install with plug-ins and their dependencies: - [wasi-nn plug-in with OpenVINO backend](https://wasmedge.org/docs/start/install#wasi-nn-plug-in-with-openvino-backend) - [wasi-nn plug-in with PyTorch backend](https://wasmedge.org/docs/start/install#wasi-nn-plug-in-with-pytorch-backend) - [wasi-nn plug-in with TensorFlow-Lite backend](https://wasmedge.org/docs/start/install#wasi-nn-plug-in-with-tensorflow-lite-backend) - [WasmEdge-Image plug-in](https://wasmedge.org/docs/start/install#wasmedge-image-plug-in) - [WasmEdge-TensorFlow plug-in](https://wasmedge.org/docs/start/install#wasmedge-tensorflow-plug-in) - [WasmEdge-TensorFlow-Lite plug-in](https://wasmedge.org/docs/start/install#wasmedge-tensorflow-lite-plug-in) ### Examples [Mobilenet](mobilenet) ### Related Links - [WASI] - [wasi-nn] - [WasmEdge] - [WasmEdge-TensorFlow rust interface](https://crates.io/crates/wasmedge_tensorflow_interface) - [wasi-nn-guest](https://github.com/radu-matei/wasi-nn-guest) [WasmEdge]: https://wasmedge.org/ [wasi-nn]: https://github.com/WebAssembly/wasi-nn [WASI]: https://github.com/WebAssembly/WASI ### License This project is licensed under the Apache 2.0 license. See [LICENSE](LICENSE) for more details. ### Contribution Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project by you, as defined in the Apache-2.0 license, shall be licensed as above, without any additional terms or conditions. ================================================ FILE: openvino-mobilenet-image/README.md ================================================ # Mobilenet example with WasmEdge WASI-NN OpenVINO plugin This example demonstrates how to use WasmEdge WASI-NN OpenVINO plugin to perform an inference task with Mobilenet model. ## Set up the environment - Install `rustup` and `Rust` Go to the [official Rust webpage](https://www.rust-lang.org/tools/install) and follow the instructions to install `rustup` and `Rust`. > It is recommended to use Rust 1.68 or above in the stable channel. Then, add `wasm32-wasip1` target to the Rustup toolchain: ```bash rustup target add wasm32-wasip1 ``` - Clone the example repo ```bash git clone https://github.com/second-state/WasmEdge-WASINN-examples.git ``` - Install OpenVINO Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plugin/wasi_nn) and [OpenVINO™](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_apt.html)(2023) for the installation process. ```bash bash WasmEdge-WASINN-examples/scripts/install_openvino.sh ldconfig ``` - Install WasmEdge with Wasi-NN OpenVINO plugin ```bash export CMAKE_BUILD_TYPE=Release export VERSION=0.13.2 curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino ldconfig ``` ## Build and run `openvino-mobilenet-image` example - Download `MobileNet` model file ```bash cd openvino-mobilenet-image bash download_mobilenet.sh ``` - Build and run the example ```bash cd rust cargo build --target wasm32-wasip1 --release cd .. wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet.wasm mobilenet.xml mobilenet.bin input.jpg ``` If the commands above run successfully, you will get the output: ```bash Read graph XML, size in bytes: 143525 Read graph weights, size in bytes: 13956476 Loaded graph into wasi-nn with ID: 0 Created wasi-nn execution context with ID: 0 Read input tensor, size in bytes: 602112 Executed graph inference 1.) [954](0.9789)banana 2.) [940](0.0074)spaghetti squash 3.) [951](0.0014)lemon 4.) [969](0.0005)eggnog 5.) [942](0.0005)butternut squash ``` ================================================ FILE: openvino-mobilenet-image/download_mobilenet.sh ================================================ FIXTURE=https://github.com/intel/openvino-rs/raw/v0.3.3/crates/openvino/tests/fixtures/mobilenet TODIR=$1 if [ ! -f $TODIR/mobilenet.bin ]; then wget --no-clobber --directory-prefix=$TODIR $FIXTURE/mobilenet.bin fi if [ ! -f $TODIR/mobilenet.xml ]; then wget --no-clobber --directory-prefix=$TODIR $FIXTURE/mobilenet.xml fi ================================================ FILE: openvino-mobilenet-image/rust/Cargo.toml ================================================ [package] name = "wasmedge-wasinn-example-mobilenet-image" version = "0.1.0" authors = ["Second-State"] readme = "README.md" edition = "2021" publish = false [dependencies] image = { version = "0.23.14", default-features = false, features = ["gif", "jpeg", "ico", "png", "pnm", "tga", "tiff", "webp", "bmp", "hdr", "dxt", "dds", "farbfeld"] } wasi-nn = { version = "0.6.0" } [workspace] ================================================ FILE: openvino-mobilenet-image/rust/src/imagenet_classes.rs ================================================ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /* The code in this file is adapted from https://github.com/tensorflow/tfjs-models/blob/master/mobilenet/src/imagenet_classes.ts */ pub const IMAGENET_CLASSES: [&str; 1000] = [ "tench, Tinca tinca", "goldfish, Carassius auratus", "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "tiger shark, Galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, Struthio camelus", "brambling, Fringilla montifringilla", "goldfinch, Carduelis carduelis", "house finch, linnet, Carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "robin, American robin, Turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, American eagle, Haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, Strix nebulosa", "European fire salamander, Salamandra salamandra", "common newt, Triturus vulgaris", "eft", "spotted salamander, Ambystoma maculatum", "axolotl, mud puppy, Ambystoma mexicanum", "bullfrog, Rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "loggerhead, loggerhead turtle, Caretta caretta", "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, Iguana iguana", "American chameleon, anole, Anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, Chlamydosaurus kingi", "alligator lizard", "Gila monster, Heloderma suspectum", "green lizard, Lacerta viridis", "African chameleon, Chamaeleo chamaeleon", "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "African crocodile, Nile crocodile, Crocodylus niloticus", "American alligator, Alligator mississipiensis", "triceratops", "thunder snake, worm snake, Carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, Hypsiglena torquata", "boa constrictor, Constrictor constrictor", "rock python, rock snake, Python sebae", "Indian cobra, Naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "diamondback, diamondback rattlesnake, Crotalus adamanteus", "sidewinder, horned rattlesnake, Crotalus cerastes", "trilobite", "harvestman, daddy longlegs, Phalangium opilio", "scorpion", "black and gold garden spider, Argiope aurantia", "barn spider, Araneus cavaticus", "garden spider, Aranea diademata", "black widow, Latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, Bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "African grey, African gray, Psittacus erithacus", "macaw", "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, Mergus serrator", "goose", "black swan, Cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "wombat", "jelly fish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "Dungeness crab, Cancer magister", "rock crab, Cancer irroratus", "fiddler crab", "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "American lobster, Northern lobster, Maine lobster, Homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, Ciconia ciconia", "black stork, Ciconia nigra", "spoonbill", "flamingo", "little blue heron, Egretta caerulea", "American egret, great white heron, Egretta albus", "bittern", "crane", "limpkin, Aramus pictus", "European gallinule, Porphyrio porphyrio", "American coot, marsh hen, mud hen, water hen, Fulica americana", "bustard", "ruddy turnstone, Arenaria interpres", "red-backed sandpiper, dunlin, Erolia alpina", "redshank, Tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, Aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "dugong, Dugong dugon", "sea lion", "Chihuahua", "Japanese spaniel", "Maltese dog, Maltese terrier, Maltese", "Pekinese, Pekingese, Peke", "Shih-Tzu", "Blenheim spaniel", "papillon", "toy terrier", "Rhodesian ridgeback", "Afghan hound, Afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "Walker hound, Walker foxhound", "English foxhound", "redbone", "borzoi, Russian wolfhound", "Irish wolfhound", "Italian greyhound", "whippet", "Ibizan hound, Ibizan Podenco", "Norwegian elkhound, elkhound", "otterhound, otter hound", "Saluki, gazelle hound", "Scottish deerhound, deerhound", "Weimaraner", "Staffordshire bullterrier, Staffordshire bull terrier", "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "Bedlington terrier", "Border terrier", "Kerry blue terrier", "Irish terrier", "Norfolk terrier", "Norwich terrier", "Yorkshire terrier", "wire-haired fox terrier", "Lakeland terrier", "Sealyham terrier, Sealyham", "Airedale, Airedale terrier", "cairn, cairn terrier", "Australian terrier", "Dandie Dinmont, Dandie Dinmont terrier", "Boston bull, Boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "Scotch terrier, Scottish terrier, Scottie", "Tibetan terrier, chrysanthemum dog", "silky terrier, Sydney silky", "soft-coated wheaten terrier", "West Highland white terrier", "Lhasa, Lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "Labrador retriever", "Chesapeake Bay retriever", "German short-haired pointer", "vizsla, Hungarian pointer", "English setter", "Irish setter, red setter", "Gordon setter", "Brittany spaniel", "clumber, clumber spaniel", "English springer, English springer spaniel", "Welsh springer spaniel", "cocker spaniel, English cocker spaniel, cocker", "Sussex spaniel", "Irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "Old English sheepdog, bobtail", "Shetland sheepdog, Shetland sheep dog, Shetland", "collie", "Border collie", "Bouvier des Flandres, Bouviers des Flandres", "Rottweiler", "German shepherd, German shepherd dog, German police dog, alsatian", "Doberman, Doberman pinscher", "miniature pinscher", "Greater Swiss Mountain dog", "Bernese mountain dog", "Appenzeller", "EntleBucher", "boxer", "bull mastiff", "Tibetan mastiff", "French bulldog", "Great Dane", "Saint Bernard, St Bernard", "Eskimo dog, husky", "malamute, malemute, Alaskan malamute", "Siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "Leonberg", "Newfoundland, Newfoundland dog", "Great Pyrenees", "Samoyed, Samoyede", "Pomeranian", "chow, chow chow", "keeshond", "Brabancon griffon", "Pembroke, Pembroke Welsh corgi", "Cardigan, Cardigan Welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "Mexican hairless", "timber wolf, grey wolf, gray wolf, Canis lupus", "white wolf, Arctic wolf, Canis lupus tundrarum", "red wolf, maned wolf, Canis rufus, Canis niger", "coyote, prairie wolf, brush wolf, Canis latrans", "dingo, warrigal, warragal, Canis dingo", "dhole, Cuon alpinus", "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "hyena, hyaena", "red fox, Vulpes vulpes", "kit fox, Vulpes macrotis", "Arctic fox, white fox, Alopex lagopus", "grey fox, gray fox, Urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "Persian cat", "Siamese cat, Siamese", "Egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "lynx, catamount", "leopard, Panthera pardus", "snow leopard, ounce, Panthera uncia", "jaguar, panther, Panthera onca, Felis onca", "lion, king of beasts, Panthera leo", "tiger, Panthera tigris", "cheetah, chetah, Acinonyx jubatus", "brown bear, bruin, Ursus arctos", "American black bear, black bear, Ursus americanus, Euarctos americanus", "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "sloth bear, Melursus ursinus, Ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil\"s darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "Angora, Angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, Sciurus niger", "marmot", "beaver", "guinea pig, Cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, Sus scrofa", "wild boar, boar, Sus scrofa", "warthog", "hippopotamus, hippo, river horse, Hippopotamus amphibius", "ox", "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "ibex, Capra ibex", "hartebeest", "impala, Aepyceros melampus", "gazelle", "Arabian camel, dromedary, Camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, Mustela putorius", "black-footed ferret, ferret, Mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, Bradypus tridactylus", "orangutan, orang, orangutang, Pongo pygmaeus", "gorilla, Gorilla gorilla", "chimpanzee, chimp, Pan troglodytes", "gibbon, Hylobates lar", "siamang, Hylobates syndactylus, Symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, Erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, Nasalis larvatus", "marmoset", "capuchin, ringtail, Cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, Ateles geoffroyi", "squirrel monkey, Saimiri sciureus", "Madagascar cat, ring-tailed lemur, Lemur catta", "indri, indris, Indri indri, Indri brevicaudatus", "Indian elephant, Elephas maximus", "African elephant, Loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "rock beauty, Holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, Lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge\"s robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, Biro", "Band Aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter\"s kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "cassette", "cassette player", "castle", "catamaran", "CD player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "Christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "Crock Pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "Dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "French horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "iPod", "iron, smoothing iron", "jack-o\"-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, T-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "Loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler\"s loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "Model T", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, CRO", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj\"s, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "Petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter\"s plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber\"s helper", "Polaroid camera, Polaroid Land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter\"s wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, RV, R.V.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, CRT screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider\"s web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, U-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "Windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, Virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "French loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "Granny Smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady\"s slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]; ================================================ FILE: openvino-mobilenet-image/rust/src/main.rs ================================================ use image::io::Reader; use image::DynamicImage; use std::env; use wasi_nn; mod imagenet_classes; use wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, TensorType}; pub fn main() -> Result<(), Box> { let args: Vec = env::args().collect(); let model_xml_path: &str = &args[1]; let model_bin_path: &str = &args[2]; let image_name: &str = &args[3]; print!("Load graph ..."); let graph = GraphBuilder::new(GraphEncoding::Openvino, ExecutionTarget::CPU) .build_from_files([model_xml_path, model_bin_path])?; println!("done"); print!("Init execution context ..."); let mut context = graph.init_execution_context()?; println!("done"); print!("Set input tensor ..."); let input_dims = vec![1, 3, 224, 224]; let tensor_data = image_to_tensor(image_name.to_string(), 224, 224); context.set_input(0, TensorType::F32, &input_dims, tensor_data)?; println!("done"); print!("Perform graph inference ..."); context.compute()?; println!("done"); print!("Retrieve the output ..."); // Copy output to abuffer. let mut output_buffer = vec![0f32; 1001]; let size_in_bytes = context.get_output(0, &mut output_buffer)?; println!("done"); println!("The size of the output buffer is {} bytes", size_in_bytes); let results = sort_results(&output_buffer); for i in 0..5 { println!( " {}.) [{}]({:.4}){}", i + 1, results[i].0, results[i].1, imagenet_classes::IMAGENET_CLASSES[results[i].0] ); } Ok(()) } // Sort the buffer of probabilities. The graph places the match probability for each class at the // index for that class (e.g. the probability of class 42 is placed at buffer[42]). Here we convert // to a wrapping InferenceResult and sort the results. fn sort_results(buffer: &[f32]) -> Vec { let mut results: Vec = buffer .iter() .skip(1) .enumerate() .map(|(c, p)| InferenceResult(c, *p)) .collect(); results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); results } // Take the image located at 'path', open it, resize it to height x width, and then converts // the pixel precision to FP32. The resulting BGR pixel vector is then returned. fn image_to_tensor(path: String, height: u32, width: u32) -> Vec { let pixels = Reader::open(path).unwrap().decode().unwrap(); let dyn_img: DynamicImage = pixels.resize_exact(width, height, image::imageops::Triangle); let bgr_img = dyn_img.to_bgr8(); // Get an array of the pixel values let raw_u8_arr: &[u8] = &bgr_img.as_raw()[..]; // Transpose from [height, width, 3] to [3, height, width] let mut transposed: Vec = vec![0; raw_u8_arr.len()]; for ch in 0..3 { for y in 0..height { for x in 0..width { let loc = y * height + x; transposed[(ch * width * height + loc) as usize] = raw_u8_arr[(loc * 3 + ch) as usize]; } } } // Create an array to hold the f32 value of those pixels let bytes_required = transposed.len() * 4; let mut u8_f32_arr: Vec = vec![0; bytes_required]; for i in 0..transposed.len() { // Read the number as a f32 and break it into u8 bytes let u8_f32: f32 = transposed[i] as f32; let u8_bytes = u8_f32.to_ne_bytes(); for j in 0..4 { u8_f32_arr[(i * 4) + j] = u8_bytes[j]; } } return u8_f32_arr; } // A wrapper for class ID and match probabilities. #[derive(Debug, PartialEq)] struct InferenceResult(usize, f32); ================================================ FILE: openvino-mobilenet-raw/README.md ================================================ # Mobilenet example with WasmEdge WASI-NN OpenVINO plugin This example demonstrates how to use WasmEdge WASI-NN OpenVINO plugin to perform an inference task with Mobilenet model. ## Set up the environment - Install `rustup` and `Rust` Go to the [official Rust webpage](https://www.rust-lang.org/tools/install) and follow the instructions to install `rustup` and `Rust`. > It is recommended to use Rust 1.68 or above in the stable channel. Then, add `wasm32-wasip1` target to the Rustup toolchain: ```bash rustup target add wasm32-wasip1 ``` - Clone the example repo ```bash git clone https://github.com/second-state/WasmEdge-WASINN-examples.git ``` - Install OpenVINO Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plugin/wasi_nn) and [OpenVINO™](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_apt.html)(2023) for the installation process. ```bash bash WasmEdge-WASINN-examples/scripts/install_openvino.sh ldconfig ``` - Install WasmEdge with Wasi-NN OpenVINO plugin ```bash export CMAKE_BUILD_TYPE=Release export VERSION=0.13.2 curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino ldconfig ``` ## Build and run `openvino-mobilenet-raw` example - Download `MobileNet` model file ```bash cd openvino-mobilenet-raw bash download_mobilenet.sh ``` - Build and run the example ```bash cd rust cargo build --target wasm32-wasip1 --release cd .. wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm mobilenet.xml mobilenet.bin input.jpg ``` If the commands above run successfully, you will get the output: ```bash Read graph XML, size in bytes: 143525 Read graph weights, size in bytes: 13956476 Loaded graph into wasi-nn with ID: 0 Created wasi-nn execution context with ID: 0 Read input tensor, size in bytes: 602112 Executed graph inference 1.) [963](0.7113)pizza, pizza pie 2.) [762](0.0707)restaurant, eating house, eating place, eatery 3.) [909](0.0364)wok 4.) [926](0.0155)hot pot, hotpot 5.) [567](0.0153)frying pan, frypan, skillet ``` ================================================ FILE: openvino-mobilenet-raw/download_mobilenet.sh ================================================ FIXTURE=https://github.com/intel/openvino-rs/raw/v0.3.3/crates/openvino/tests/fixtures/mobilenet TODIR=$1 if [ ! -f $TODIR/mobilenet.bin ]; then wget --no-clobber --directory-prefix=$TODIR $FIXTURE/mobilenet.bin fi if [ ! -f $TODIR/mobilenet.xml ]; then wget --no-clobber --directory-prefix=$TODIR $FIXTURE/mobilenet.xml fi if [ ! -f $TODIR/tensor-1x224x224x3-f32.bgr ]; then wget --no-clobber --directory-prefix=$TODIR $FIXTURE/tensor-1x224x224x3-f32.bgr fi ================================================ FILE: openvino-mobilenet-raw/rust/Cargo.toml ================================================ [package] name = "wasmedge-wasinn-example-mobilenet" version = "0.1.0" authors = ["Second-State"] readme = "README.md" edition = "2021" publish = false [dependencies] wasi-nn = { version = "0.6.0" } [workspace] ================================================ FILE: openvino-mobilenet-raw/rust/src/imagenet_classes.rs ================================================ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /* The code in this file is adapted from https://github.com/tensorflow/tfjs-models/blob/master/mobilenet/src/imagenet_classes.ts */ pub const IMAGENET_CLASSES: [&str; 1000] = [ "tench, Tinca tinca", "goldfish, Carassius auratus", "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "tiger shark, Galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, Struthio camelus", "brambling, Fringilla montifringilla", "goldfinch, Carduelis carduelis", "house finch, linnet, Carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "robin, American robin, Turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, American eagle, Haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, Strix nebulosa", "European fire salamander, Salamandra salamandra", "common newt, Triturus vulgaris", "eft", "spotted salamander, Ambystoma maculatum", "axolotl, mud puppy, Ambystoma mexicanum", "bullfrog, Rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "loggerhead, loggerhead turtle, Caretta caretta", "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, Iguana iguana", "American chameleon, anole, Anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, Chlamydosaurus kingi", "alligator lizard", "Gila monster, Heloderma suspectum", "green lizard, Lacerta viridis", "African chameleon, Chamaeleo chamaeleon", "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "African crocodile, Nile crocodile, Crocodylus niloticus", "American alligator, Alligator mississipiensis", "triceratops", "thunder snake, worm snake, Carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, Hypsiglena torquata", "boa constrictor, Constrictor constrictor", "rock python, rock snake, Python sebae", "Indian cobra, Naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "diamondback, diamondback rattlesnake, Crotalus adamanteus", "sidewinder, horned rattlesnake, Crotalus cerastes", "trilobite", "harvestman, daddy longlegs, Phalangium opilio", "scorpion", "black and gold garden spider, Argiope aurantia", "barn spider, Araneus cavaticus", "garden spider, Aranea diademata", "black widow, Latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, Bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "African grey, African gray, Psittacus erithacus", "macaw", "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, Mergus serrator", "goose", "black swan, Cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "wombat", "jelly fish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "Dungeness crab, Cancer magister", "rock crab, Cancer irroratus", "fiddler crab", "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "American lobster, Northern lobster, Maine lobster, Homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, Ciconia ciconia", "black stork, Ciconia nigra", "spoonbill", "flamingo", "little blue heron, Egretta caerulea", "American egret, great white heron, Egretta albus", "bittern", "crane", "limpkin, Aramus pictus", "European gallinule, Porphyrio porphyrio", "American coot, marsh hen, mud hen, water hen, Fulica americana", "bustard", "ruddy turnstone, Arenaria interpres", "red-backed sandpiper, dunlin, Erolia alpina", "redshank, Tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, Aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "dugong, Dugong dugon", "sea lion", "Chihuahua", "Japanese spaniel", "Maltese dog, Maltese terrier, Maltese", "Pekinese, Pekingese, Peke", "Shih-Tzu", "Blenheim spaniel", "papillon", "toy terrier", "Rhodesian ridgeback", "Afghan hound, Afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "Walker hound, Walker foxhound", "English foxhound", "redbone", "borzoi, Russian wolfhound", "Irish wolfhound", "Italian greyhound", "whippet", "Ibizan hound, Ibizan Podenco", "Norwegian elkhound, elkhound", "otterhound, otter hound", "Saluki, gazelle hound", "Scottish deerhound, deerhound", "Weimaraner", "Staffordshire bullterrier, Staffordshire bull terrier", "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "Bedlington terrier", "Border terrier", "Kerry blue terrier", "Irish terrier", "Norfolk terrier", "Norwich terrier", "Yorkshire terrier", "wire-haired fox terrier", "Lakeland terrier", "Sealyham terrier, Sealyham", "Airedale, Airedale terrier", "cairn, cairn terrier", "Australian terrier", "Dandie Dinmont, Dandie Dinmont terrier", "Boston bull, Boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "Scotch terrier, Scottish terrier, Scottie", "Tibetan terrier, chrysanthemum dog", "silky terrier, Sydney silky", "soft-coated wheaten terrier", "West Highland white terrier", "Lhasa, Lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "Labrador retriever", "Chesapeake Bay retriever", "German short-haired pointer", "vizsla, Hungarian pointer", "English setter", "Irish setter, red setter", "Gordon setter", "Brittany spaniel", "clumber, clumber spaniel", "English springer, English springer spaniel", "Welsh springer spaniel", "cocker spaniel, English cocker spaniel, cocker", "Sussex spaniel", "Irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "Old English sheepdog, bobtail", "Shetland sheepdog, Shetland sheep dog, Shetland", "collie", "Border collie", "Bouvier des Flandres, Bouviers des Flandres", "Rottweiler", "German shepherd, German shepherd dog, German police dog, alsatian", "Doberman, Doberman pinscher", "miniature pinscher", "Greater Swiss Mountain dog", "Bernese mountain dog", "Appenzeller", "EntleBucher", "boxer", "bull mastiff", "Tibetan mastiff", "French bulldog", "Great Dane", "Saint Bernard, St Bernard", "Eskimo dog, husky", "malamute, malemute, Alaskan malamute", "Siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "Leonberg", "Newfoundland, Newfoundland dog", "Great Pyrenees", "Samoyed, Samoyede", "Pomeranian", "chow, chow chow", "keeshond", "Brabancon griffon", "Pembroke, Pembroke Welsh corgi", "Cardigan, Cardigan Welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "Mexican hairless", "timber wolf, grey wolf, gray wolf, Canis lupus", "white wolf, Arctic wolf, Canis lupus tundrarum", "red wolf, maned wolf, Canis rufus, Canis niger", "coyote, prairie wolf, brush wolf, Canis latrans", "dingo, warrigal, warragal, Canis dingo", "dhole, Cuon alpinus", "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "hyena, hyaena", "red fox, Vulpes vulpes", "kit fox, Vulpes macrotis", "Arctic fox, white fox, Alopex lagopus", "grey fox, gray fox, Urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "Persian cat", "Siamese cat, Siamese", "Egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "lynx, catamount", "leopard, Panthera pardus", "snow leopard, ounce, Panthera uncia", "jaguar, panther, Panthera onca, Felis onca", "lion, king of beasts, Panthera leo", "tiger, Panthera tigris", "cheetah, chetah, Acinonyx jubatus", "brown bear, bruin, Ursus arctos", "American black bear, black bear, Ursus americanus, Euarctos americanus", "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "sloth bear, Melursus ursinus, Ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil\"s darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "Angora, Angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, Sciurus niger", "marmot", "beaver", "guinea pig, Cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, Sus scrofa", "wild boar, boar, Sus scrofa", "warthog", "hippopotamus, hippo, river horse, Hippopotamus amphibius", "ox", "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "ibex, Capra ibex", "hartebeest", "impala, Aepyceros melampus", "gazelle", "Arabian camel, dromedary, Camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, Mustela putorius", "black-footed ferret, ferret, Mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, Bradypus tridactylus", "orangutan, orang, orangutang, Pongo pygmaeus", "gorilla, Gorilla gorilla", "chimpanzee, chimp, Pan troglodytes", "gibbon, Hylobates lar", "siamang, Hylobates syndactylus, Symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, Erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, Nasalis larvatus", "marmoset", "capuchin, ringtail, Cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, Ateles geoffroyi", "squirrel monkey, Saimiri sciureus", "Madagascar cat, ring-tailed lemur, Lemur catta", "indri, indris, Indri indri, Indri brevicaudatus", "Indian elephant, Elephas maximus", "African elephant, Loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "rock beauty, Holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, Lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge\"s robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, Biro", "Band Aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter\"s kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "cassette", "cassette player", "castle", "catamaran", "CD player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "Christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "Crock Pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "Dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "French horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "iPod", "iron, smoothing iron", "jack-o\"-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, T-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "Loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler\"s loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "Model T", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, CRO", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj\"s, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "Petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter\"s plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber\"s helper", "Polaroid camera, Polaroid Land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter\"s wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, RV, R.V.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, CRT screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider\"s web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, U-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "Windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, Virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "French loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "Granny Smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady\"s slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]; ================================================ FILE: openvino-mobilenet-raw/rust/src/main.rs ================================================ use std::env; use std::fs; use wasi_nn; mod imagenet_classes; use wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, TensorType}; pub fn main() -> Result<(), Box> { let args: Vec = env::args().collect(); let model_xml_path: &str = &args[1]; let model_bin_path: &str = &args[2]; let tensor_name: &str = &args[3]; print!("Load graph ..."); let graph = GraphBuilder::new(GraphEncoding::Openvino, ExecutionTarget::CPU) .build_from_files([model_xml_path, model_bin_path])?; println!("done"); print!("Init execution context ..."); let mut context = graph.init_execution_context()?; println!("done"); // Load a tensor that precisely matches the graph input tensor (see // `fixture/frozen_inference_graph.xml`). print!("Set input tensor ..."); let input_dims = vec![1, 3, 224, 224]; let tensor_data = fs::read(tensor_name).unwrap(); context.set_input(0, TensorType::F32, &input_dims, tensor_data)?; println!("done"); print!("Perform graph inference ..."); context.compute()?; println!("done"); print!("Retrieve the output ..."); // Copy output to abuffer. let mut output_buffer = vec![0f32; 1001]; let size_in_bytes = context.get_output(0, &mut output_buffer)?; println!("done"); println!("The size of the output buffer is {} bytes", size_in_bytes); let results = sort_results(&output_buffer); for i in 0..5 { println!( " {}.) [{}]({:.4}){}", i + 1, results[i].0, results[i].1, imagenet_classes::IMAGENET_CLASSES[results[i].0] ); } Ok(()) } // Sort the buffer of probabilities. The graph places the match probability for each class at the // index for that class (e.g. the probability of class 42 is placed at buffer[42]). Here we convert // to a wrapping InferenceResult and sort the results. fn sort_results(buffer: &[f32]) -> Vec { let mut results: Vec = buffer .iter() .skip(1) .enumerate() .map(|(c, p)| InferenceResult(c, *p)) .collect(); results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); results } // A wrapper for class ID and match probabilities. #[derive(Debug, PartialEq)] struct InferenceResult(usize, f32); ================================================ FILE: openvino-road-segmentation-adas/README.md ================================================ # OpenVINO Road Segmentation ADAS Example on WasmEdge Runtime ## Overview In this example, we'll use `WasmEdge wasi-nn interfaces` to demonstrates a popular task used in the CV-based `Advanced Driver Assist Systems (ADAS)`: road segmentation. | | | | --------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | | | The model files and the images used for this demonstration are from the [Intel openvino_notebooks repo](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/003-hello-segmentation/README.md) on Github. ## Set up the environment - Install `rustup` and `Rust` Go to the [official Rust webpage](https://www.rust-lang.org/tools/install) and follow the instructions to install `rustup` and `Rust`. > It is recommended to use Rust 1.68 or above in the stable channel. Then, add `wasm32-wasip1` target to the Rustup toolchain: ```bash rustup target add wasm32-wasip1 ``` - Clone the example repo ```bash git clone https://github.com/second-state/WasmEdge-WASINN-examples.git ``` - Install OpenVINO Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plugin/wasi_nn) and [OpenVINO™](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_apt.html)(2023) for the installation process. ```bash bash WasmEdge-WASINN-examples/scripts/install_openvino.sh ldconfig ``` - Install WasmEdge with Wasi-NN OpenVINO plugin ```bash export CMAKE_BUILD_TYPE=Release export VERSION=0.13.2 curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -v $VERSION -p /usr/local --plugins wasi_nn-openvino ldconfig ``` ## Build and run `openvino-road-segmentation-adas` example - Download `MobileNet` model file ```bash cd openvino-road-segmentation-adas ``` - Build and run the example ```bash cd openvino-road-seg-adas cargo build --target wasm32-wasip1 --release cd .. wasmedge --dir .:. ./openvino-road-seg-adas/target/wasm32-wasip1/release/openvino-road-seg-adas.wasm \ ./model/road-segmentation-adas-0001.xml \ ./model/road-segmentation-adas-0001.bin \ ./image/empty_road_mapillary.jpg ``` If the commands run successfully, an output tensor will be generated and saved to `wasinn-openvino-inference-output-1x4x512x896xf32.tensor`. ## Visualize the inference result To visualize the input image and the inference output tensor, you can use the `visualize_inference_result.ipynb` file. You may have to modify some file paths in the file, if the files used in the notebook are dumped in different directories. The following picture shows the inference result of road segmentation task. ![road segmentation result](image/segmentation_result.png) ================================================ FILE: openvino-road-segmentation-adas/model/road-segmentation-adas-0001.xml ================================================ 1 3 512 896 16 3 3 3 1 3 512 896 16 3 3 3 1 16 256 448 1 16 1 1 1 16 256 448 1 16 1 1 1 16 256 448 1 16 256 448 1 16 256 448 8 16 1 1 1 16 256 448 8 16 1 1 1 8 256 448 1 8 1 1 1 8 256 448 1 8 1 1 1 8 256 448 1 8 256 448 1 8 256 448 8 1 1 3 3 1 8 256 448 8 1 1 3 3 1 8 256 448 1 8 1 1 1 8 256 448 1 8 1 1 1 8 256 448 1 8 256 448 1 8 256 448 16 8 1 1 1 8 256 448 16 8 1 1 1 16 256 448 1 16 1 1 1 16 256 448 1 16 1 1 1 16 256 448 1 16 256 448 1 16 256 448 1 16 256 448 1 16 256 448 1 16 256 448 8 16 1 1 1 16 256 448 8 16 1 1 1 8 256 448 1 8 1 1 1 8 256 448 1 8 1 1 1 8 256 448 1 8 256 448 1 8 256 448 8 1 1 3 3 1 8 256 448 8 1 1 3 3 1 8 256 448 1 8 1 1 1 8 256 448 1 8 1 1 1 8 256 448 1 8 256 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1 48 64 112 1 48 64 112 1 48 64 112 1 48 64 112 1 48 64 112 1 48 64 112 1 48 64 112 1 48 16 28 16 48 1 1 1 48 16 28 16 48 1 1 1 16 16 28 1 16 1 1 1 16 16 28 1 16 1 1 1 16 16 28 1 16 16 28 1 16 16 28 1 16 16 28 4 1 1 1 4 1 1 1 2 1 2 1 2 1 2 1 2 2 2 1 16 16 28 2 2 2 1 16 64 112 48 16 1 1 1 16 64 112 48 16 1 1 1 48 64 112 1 48 1 1 1 48 64 112 1 48 1 1 1 48 64 112 1 48 64 112 1 48 64 112 1 48 64 112 1 48 64 112 1 48 64 112 24 48 1 1 1 48 64 112 24 48 1 1 1 24 64 112 1 24 1 1 1 24 64 112 1 24 1 1 1 24 64 112 1 24 64 112 1 24 64 112 1 24 64 112 4 1 1 1 4 1 1 1 2 1 2 1 2 1 2 1 2 2 2 1 24 64 112 2 2 2 1 24 128 224 24 48 1 1 1 48 128 224 24 48 1 1 1 24 128 224 1 24 1 1 1 24 128 224 1 24 1 1 1 24 128 224 1 24 128 224 1 24 128 224 1 24 128 224 1 24 128 224 1 24 128 224 16 24 1 1 1 24 128 224 16 24 1 1 1 16 128 224 1 16 1 1 1 16 128 224 1 16 1 1 1 16 128 224 1 16 128 224 1 16 128 224 1 16 128 224 4 1 1 1 4 1 1 1 2 1 2 1 2 1 2 1 2 2 2 1 16 128 224 2 2 2 1 16 256 448 16 32 1 1 1 32 256 448 16 32 1 1 1 16 256 448 1 16 1 1 1 16 256 448 1 16 1 1 1 16 256 448 1 16 256 448 1 16 256 448 1 16 256 448 1 16 256 448 1 16 256 448 16 16 1 1 1 16 256 448 16 16 1 1 1 16 256 448 1 16 1 1 1 16 256 448 1 16 1 1 1 16 256 448 1 16 256 448 1 16 256 448 16 1 1 3 3 1 16 256 448 16 1 1 3 3 1 16 256 448 1 16 1 1 1 16 256 448 1 16 1 1 1 16 256 448 1 16 256 448 1 16 256 448 4 16 1 1 1 16 256 448 4 16 1 1 1 4 256 448 1 4 1 1 1 4 256 448 1 4 1 1 1 4 256 448 1 4 256 448 4 1 1 1 4 1 1 1 2 1 2 1 2 1 2 1 2 2 2 1 4 256 448 2 2 2 1 4 512 896 1 4 512 896 1 4 512 896 1 4 512 896 ================================================ FILE: openvino-road-segmentation-adas/openvino-road-seg-adas/Cargo.toml ================================================ [package] authors = ["Sam Liu "] edition = "2021" name = "openvino-road-seg-adas" version = "0.2.0" [dependencies] wasi-nn = "0.6.0" image = { version = "0.23.14", default-features = false, features = ["gif", "jpeg", "ico", "png", "pnm", "tga", "tiff", "webp", "bmp", "hdr", "dxt", "dds", "farbfeld"] } ================================================ FILE: openvino-road-segmentation-adas/openvino-road-seg-adas/src/main.rs ================================================ use image::{io::Reader, DynamicImage}; use std::env; use wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, TensorType}; fn main() -> Result<(), Box> { let args: Vec = env::args().collect(); let model_xml_path: &str = &args[1]; let model_bin_path: &str = &args[2]; let image_name: &str = &args[3]; print!("Load graph ..."); let graph = GraphBuilder::new(GraphEncoding::Openvino, ExecutionTarget::CPU) .build_from_files([model_xml_path, model_bin_path])?; println!("done"); print!("Init execution context ..."); let mut context = graph.init_execution_context()?; println!("done"); print!("Set input tensor ..."); let input_dims = vec![1, 3, 512, 896]; let tensor_data = image_to_tensor(image_name.to_string(), 512, 896); context.set_input(0, TensorType::F32, &input_dims, tensor_data)?; println!("done"); print!("Perform graph inference ..."); context.compute()?; println!("done"); print!("Retrieve the output ..."); // Copy output to abuffer. let mut output_buffer = vec![0u8; 1 * 4 * 512 * 896 * 4]; let size_in_bytes = context.get_output(0, &mut output_buffer)?; println!("done"); println!("The size of the output buffer is {} bytes", size_in_bytes); println!("Dump result ..."); dump( "wasinn-openvino-inference-output-1x4x512x896xf32.tensor", output_buffer.as_slice(), )?; Ok(()) } /// Dump data to the specified binary file. fn dump( path: impl AsRef, buffer: impl AsRef<[T]>, ) -> Result<(), Box> { println!("\tdump tensor to {:?}", path.as_ref()); let dst_slice: &[u8] = unsafe { std::slice::from_raw_parts( buffer.as_ref().as_ptr() as *const u8, buffer.as_ref().len() * std::mem::size_of::(), ) }; println!("\tThe size of bytes: {}", dst_slice.len()); std::fs::write(path, &dst_slice).expect("failed to write file"); println!("*** done ***"); Ok(()) } // Take the image located at 'path', open it, resize it to height x width, and then converts // the pixel precision to FP32. The resulting BGR pixel vector is then returned. fn image_to_tensor(path: String, height: u32, width: u32) -> Vec { let pixels = Reader::open(path).unwrap().decode().unwrap(); let dyn_img: DynamicImage = pixels.resize_exact(width, height, image::imageops::Triangle); let bgr_img = dyn_img.to_bgr8(); // Get an array of the pixel values let raw_u8_arr: &[u8] = &bgr_img.as_raw()[..]; // Create an array to hold the f32 value of those pixels let bytes_required = raw_u8_arr.len() * 4; let mut u8_f32_arr: Vec = vec![0; bytes_required]; for i in 0..raw_u8_arr.len() { // Read the number as a f32 and break it into u8 bytes let u8_f32: f32 = raw_u8_arr[i] as f32; let u8_bytes = u8_f32.to_ne_bytes(); for j in 0..4 { u8_f32_arr[(i * 4) + j] = u8_bytes[j]; } } return u8_f32_arr; } ================================================ FILE: openvino-road-segmentation-adas/visualize_inference_result.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "import cv2\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the original image" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "image(BGR) shape(HWC): (1080, 1920, 3), dtype=uint8, size: 6220800\n", "rgb_image(RGB) from image shape(HWC): (1080, 1920, 3), dtype: uint8, size： 6220800\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# The segmentation network expects images in BGR format\n", "image = cv2.imread(\"image/empty_road_mapillary.jpg\")\n", "print(f\"image(BGR) shape(HWC): {image.shape}, dtype={image.dtype}, size: {image.size}\")\n", "\n", "rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", "print(f\"rgb_image(RGB) from image shape(HWC): {rgb_image.shape}, dtype: {rgb_image.dtype}, size： {rgb_image.size}\")\n", "image_h, image_w, _ = image.shape\n", "\n", "plt.imshow(rgb_image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the result tensor returned by WasmEdge wasi-nn OpenVINO backend" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data size: 1835008\n", "resized_data: (1, 4, 512, 896), dtype: float32\n", "segmentation_mask shape: (1, 512, 896), dtype: int64\n", "[[0 0 0 ... 0 0 0]\n", " [0 0 0 ... 0 0 0]\n", " [0 0 0 ... 0 0 0]\n", " ...\n", " [2 2 2 ... 1 1 1]\n", " [2 2 2 ... 1 1 1]\n", " [2 2 2 ... 1 1 1]]\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# parse the output file generated by wasi-nn\n", "data = np.fromfile(\"tensor/wasinn-openvino-inference-output-1x4x512x896xf32.tensor\", dtype=np.float32)\n", "print(f\"data size: {data.size}\")\n", "resized_data = np.resize(data, (1,4,512,896))\n", "print(f\"resized_data: {resized_data.shape}, dtype: {resized_data.dtype}\")\n", "\n", "# Prepare data for visualization\n", "segmentation_mask = np.argmax(resized_data, axis=1)\n", "print(f\"segmentation_mask shape: {segmentation_mask.shape}, dtype: {segmentation_mask.dtype}\")\n", "print(f\"{segmentation_mask[0]}\")\n", "plt.imshow(segmentation_mask[0])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare Data for Visualization" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# The following function is from https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/utils/notebook_utils.py\n", "def segmentation_map_to_image(\n", " result: np.ndarray, colormap: np.ndarray, remove_holes=False\n", ") -> np.ndarray:\n", " \"\"\"\n", " Convert network result of floating point numbers to an RGB image with\n", " integer values from 0-255 by applying a colormap.\n", "\n", " :param result: A single network result after converting to pixel values in H,W or 1,H,W shape.\n", " :param colormap: A numpy array of shape (num_classes, 3) with an RGB value per class.\n", " :param remove_holes: If True, remove holes in the segmentation result.\n", " :return: An RGB image where each pixel is an int8 value according to colormap.\n", " \"\"\"\n", " if len(result.shape) != 2 and result.shape[0] != 1:\n", " raise ValueError(\n", " f\"Expected result with shape (H,W) or (1,H,W), got result with shape {result.shape}\"\n", " )\n", "\n", " if len(np.unique(result)) > colormap.shape[0]:\n", " raise ValueError(\n", " f\"Expected max {colormap[0]} classes in result, got {len(np.unique(result))} \"\n", " \"different output values. Please make sure to convert the network output to \"\n", " \"pixel values before calling this function.\"\n", " )\n", " elif result.shape[0] == 1:\n", " result = result.squeeze(0)\n", "\n", " result = result.astype(np.uint8)\n", "\n", " contour_mode = cv2.RETR_EXTERNAL if remove_holes else cv2.RETR_TREE\n", " mask = np.zeros((result.shape[0], result.shape[1], 3), dtype=np.uint8)\n", " for label_index, color in enumerate(colormap):\n", " label_index_map = result == label_index\n", " label_index_map = label_index_map.astype(np.uint8) * 255\n", " contours, hierarchies = cv2.findContours(\n", " label_index_map, contour_mode, cv2.CHAIN_APPROX_SIMPLE\n", " )\n", " cv2.drawContours(\n", " mask,\n", " contours,\n", " contourIdx=-1,\n", " color=color.tolist(),\n", " thickness=cv2.FILLED,\n", " )\n", "\n", " return mask\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# Define colormap, each color represents a class\n", "colormap = np.array([[68, 1, 84], [48, 103, 141], [53, 183, 120], [199, 216, 52]])\n", "\n", "# Define the transparency of the segmentation mask on the photo\n", "alpha = 0.3\n", "\n", "# Use function from notebook_utils.py to transform mask to an RGB image\n", "mask = segmentation_map_to_image(segmentation_mask, colormap)\n", "image_w, image_h = 1920, 1080\n", "resized_mask = cv2.resize(mask, (image_w, image_h))\n", "\n", "# Create image with mask put on\n", "image_with_mask = cv2.addWeighted(resized_mask, alpha, rgb_image, 1 - alpha, 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize data" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Define titles with images\n", "data = {\"Base Photo\": rgb_image, \"Segmentation\": mask, \"Masked Photo\": image_with_mask}\n", "\n", "# Create subplot to visualize images\n", "f, axs = plt.subplots(1, len(data.items()), figsize=(15, 10))\n", "\n", "# Fill subplot\n", "for ax, (name, image) in zip(axs, data.items()):\n", " ax.axis('off')\n", " ax.set_title(name)\n", " ax.imshow(image)\n", "\n", "# Display image\n", "plt.show(f)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.13 ('openvino-notebooks')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "c7da5a941a2cb947c67b857c7ab77e6d34cda7fbae04686d2c0f532b73a4a77a" } } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: openvinogenai-raw/README.md ================================================ # Deepseek example with WasmEdge WASI-NN OpenVINO GenAI plugin This example demonstrates how to use WasmEdge WASI-NN OpenVINO GenAI plugin to perform an inference task with Deepseek model. ## Set up the environment - Install `rustup` and `Rust` Go to the [official Rust webpage](https://www.rust-lang.org/tools/install) and follow the instructions to install `rustup` and `Rust`. > It is recommended to use Rust 1.68 or above in the stable channel. Then, add `wasm32-wasip1` target to the Rustup toolchain: ```bash rustup target add wasm32-wasip1 ``` - Clone the example repo ```bash git clone https://github.com/second-state/WasmEdge-WASINN-examples.git ``` - Install OpenVINO GenAI Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plugin/wasi_nn) and [OpenVINO™ GenAI](https://docs.openvino.ai/2025/get-started/install-openvino/install-openvino-genai.html) for the installation process. ```bash # ensure OpenVINO environment initialized source setupvars.sh ``` - Build WasmEdge with Wasi-NN OpenVINO GenAI plugin from source ```bash docker run -v $(pwd):/code -it --rm wasmedge/wasmedge:ubuntu-build-clang-plugins-deps bash cd /code cmake -Bbuild -GNinja -DWASMEDGE_PLUGIN_WASI_NN_BACKEND=openvinogenai . ``` ## Build and run `openvinogenai-deepseek-raw` example - Download `Deepseek` model file ([huggingface](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)) - Convert the model by optimum-intel ```bash python3 -m venv .venv . .venv/bin/activate pip install --upgrade --upgrade-strategy eager "optimum[openvino]" optimum-cli export openvino --model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --task text-generation-with-past --weight-format int4 --group-size 128 --ratio 1.0 --sym DeepSeek-R1-Distill-Qwen-1.5B/INT4_compressed_weights ``` - Adjust the DeepSeek chat template OpenVINO GenAI may not accept the default `chat_template` in `openvino_tokienizer.xml`. Replace it with a valid template: ```xml ``` You can refer this information and use the template in ```llm_config.py``` : [Openvino: LLM reasoning with DeepSeek-R1 distilled models](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/deepseek-r1) - Build the example ```bash cargo build --target wasm32-wasip1 --release ``` - Run the example ```bash wasmedge ./target/wasm32-wasip1/release/openvinogenai-deepseek-raw.wasm path_to_model_xml_folder ``` You will get the output: ```console Load graph ...done Init execution context ...done Set input tensor ...done Generating ...done Get the result ...Retrieve the output ...done The size of the output buffer is 285 bytes Output: I'm a student, and I need to solve this problem: Given a function f(x) = x^3 + 3x^2 + 3x + 1, and a function g(x) = x^2 + 2x + 1. I need to find the number of real roots of f(x) and g(x). Also, I need to find the number of real roots of f(x) + g(x). Please explain step by step. I'm a done ``` ================================================ FILE: openvinogenai-raw/rust/Cargo.toml ================================================ [package] name = "openvinogenai-deepseek-raw" version = "0.1.0" authors = ["Second-State"] readme = "README.md" edition = "2021" publish = false [dependencies] serde_json = "1.0" wasmedge-wasi-nn = {git = "https://github.com/LFsWang/wasmedge-wasi-nn", branch = "ggml"} hound = "3.4" [workspace] ================================================ FILE: openvinogenai-raw/rust/src/main.rs ================================================ use std::env; use std::io::{self, Write}; use wasmedge_wasi_nn; use wasmedge_wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, TensorType}; pub fn main() -> Result<(), Box> { let args: Vec = env::args().collect(); let model_type: &str = "LLMPipeline"; let model_xml_path: &str = &args[1]; let plugin_config: &str = ""; print!("Load graph ..."); let graph = GraphBuilder::new(GraphEncoding::OpenvinoGenAI, ExecutionTarget::CPU) .build_from_bytes([model_type, model_xml_path, plugin_config])?; println!("done"); print!("Init execution context ..."); let mut context = graph.init_execution_context()?; println!("done"); print!("Set input tensor ..."); let tensor_data = "Hello, how are you?".as_bytes().to_vec(); let input_dims = vec![tensor_data.len()]; context.set_input(0, TensorType::U8, &input_dims, tensor_data)?; println!("done"); print!("Generating ..."); context.compute()?; println!("done"); print!("Get the result ..."); print!("Retrieve the output ..."); // Copy output to abuffer. let mut output_buffer = vec![0u8; 1001]; let size_in_bytes = context.get_output(0, &mut output_buffer)?; println!("done"); println!("The size of the output buffer is {} bytes", size_in_bytes); let string_output = String::from_utf8(output_buffer.iter().map(|&c| c as u8).collect()).unwrap(); println!("Output: {}", string_output); println!("done"); io::stdout().flush().unwrap(); Ok(()) } ================================================ FILE: pytorch-mobilenet-image/README.md ================================================ # Mobilenet Example For WASI-NN with PyTorch Backend This package is a high-level Rust bindings for [wasi-nn] example of Mobilenet with PyTorch backend. [wasi-nn]: https://github.com/WebAssembly/wasi-nn ## Dependencies This crate depends on the `wasi-nn` in the `Cargo.toml`: ```toml [dependencies] wasi-nn = "0.6.0" ``` ## Build Compile the application to WebAssembly: ```bash cargo build --target=wasm32-wasip1 --release ``` Because here, we will demonstrate two ways of using wasi-nn. So the output WASM files will be at [`target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm`](wasmedge-wasinn-example-mobilenet-image.wasm) and [`target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image-named-model.wasm`](wasmedge-wasinn-example-mobilenet-image-named-model.wasm). To speed up the image processing, we can enable the AOT mode in WasmEdge with: ```bash wasmedgec rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm wasmedge-wasinn-example-mobilenet-image-aot.wasm wasmedgec rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image-named-model.wasm wasmedge-wasinn-example-mobilenet-image-named-model-aot.wasm ``` ## Run ### Generate Model First generate the fixture of the pre-trained mobilenet with the script: ```bash pip3 install torch==1.8.2 torchvision==0.9.2 pillow --extra-index-url https://download.pytorch.org/whl/lts/1.8/cpu # generate the model fixture python3 gen_mobilenet_model.py ``` (Or you can use the pre-generated one at [`mobilenet.pt`](mobilenet.pt)) ### Test Image The testing image `input.jpg` is downloaded from with license Apache-2.0 ### Generate Tensor If you want to generate the [raw tensor](image-1x3x224x224.rgb), you can run: ```bash python3 gen_tensor.py input.jpg image-1x3x224x224.rgb ``` ### Execute Users should [install the WasmEdge with WASI-NN PyTorch backend plug-in](https://wasmedge.org/docs/start/install#wasi-nn-plug-in-with-pytorch-backend). Execute the WASM with the `wasmedge` with PyTorch supporting: - Case 1: ```bash wasmedge --dir .:. wasmedge-wasinn-example-mobilenet-image.wasm mobilenet.pt input.jpg ``` You will get the output: ```console Loaded graph into wasi-nn with ID: 0 Created wasi-nn execution context with ID: 0 Read input tensor, size in bytes: 602112 Executed graph inference 1.) [954](20.6681)banana 2.) [940](12.1483)spaghetti squash 3.) [951](11.5748)lemon 4.) [950](10.4899)orange 5.) [953](9.4834)pineapple, ananas ``` - Case 2: Apply named model feature > requirement wasi-nn >= 0.5.0 and WasmEdge-plugin-wasi_nn-(*) >= 0.13.4 and > --nn-preload argument format follow ::: ```bash wasmedge --dir .:. --nn-preload demo:PyTorch:CPU:mobilenet.pt wasmedge-wasinn-example-mobilenet-image-named-model.wasm demo input.jpg ``` You will get the same output: ```console Loaded graph into wasi-nn with ID: 0 Created wasi-nn execution context with ID: 0 Read input tensor, size in bytes: 602112 Executed graph inference 1.) [954](20.6681)banana 2.) [940](12.1483)spaghetti squash 3.) [951](11.5748)lemon 4.) [950](10.4899)orange 5.) [953](9.4834)pineapple, ananas ``` ================================================ FILE: pytorch-mobilenet-image/gen_mobilenet_model.py ================================================ import os import torch from torch import jit with torch.no_grad(): fake_input = torch.rand(1, 3, 224, 224) model = torch.hub.load('pytorch/vision:v0.10.0', 'mobilenet_v2', pretrained=True) model.eval() out1 = model(fake_input).squeeze() sm = torch.jit.script(model) if not os.path.exists("mobilenet.pt"): sm.save("mobilenet.pt") load_sm = jit.load("mobilenet.pt") out2 = load_sm(fake_input).squeeze() print(out1[:5], out2[:5]) ================================================ FILE: pytorch-mobilenet-image/gen_tensor.py ================================================ import sys import struct from PIL import Image from torchvision import transforms if __name__ == '__main__': input_image = Image.open(sys.argv[1]) preprocess = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) # create a mini-batch as expected by the model input_batch = input_tensor.unsqueeze(0) with open(sys.argv[2], 'wb') as f: order_data = input_batch.reshape(-1) for d in order_data: d = d.item() f.write(struct.pack('f', d)) ================================================ FILE: pytorch-mobilenet-image/mobilenet.pt ================================================ [File too large to display: 13.7 MB] ================================================ FILE: pytorch-mobilenet-image/rust/Cargo.toml ================================================ [package] name = "wasmedge-wasinn-example-mobilenet-image" version = "0.1.0" authors = ["Second-State"] readme = "README.md" edition = "2021" publish = false [dependencies] image = { version = "0.23.14", default-features = false, features = ["gif", "jpeg", "ico", "png", "pnm", "tga", "tiff", "webp", "bmp", "hdr", "dxt", "dds", "farbfeld"] } wasi-nn = { version = "0.6.0" } [[bin]] name = "wasmedge-wasinn-example-mobilenet-image" path = "src/main.rs" [[bin]] name = "wasmedge-wasinn-example-mobilenet-image-named-model" path = "src/named_model.rs" [workspace] ================================================ FILE: pytorch-mobilenet-image/rust/src/imagenet_classes.rs ================================================ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /* The code in this file is adapted from https://github.com/tensorflow/tfjs-models/blob/master/mobilenet/src/imagenet_classes.ts */ pub const IMAGENET_CLASSES: [&str; 1000] = [ "tench, Tinca tinca", "goldfish, Carassius auratus", "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "tiger shark, Galeocerdo cuvieri", "hammerhead, hammerhead shark", "electric ray, crampfish, numbfish, torpedo", "stingray", "cock", "hen", "ostrich, Struthio camelus", "brambling, Fringilla montifringilla", "goldfinch, Carduelis carduelis", "house finch, linnet, Carpodacus mexicanus", "junco, snowbird", "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "robin, American robin, Turdus migratorius", "bulbul", "jay", "magpie", "chickadee", "water ouzel, dipper", "kite", "bald eagle, American eagle, Haliaeetus leucocephalus", "vulture", "great grey owl, great gray owl, Strix nebulosa", "European fire salamander, Salamandra salamandra", "common newt, Triturus vulgaris", "eft", "spotted salamander, Ambystoma maculatum", "axolotl, mud puppy, Ambystoma mexicanum", "bullfrog, Rana catesbeiana", "tree frog, tree-frog", "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "loggerhead, loggerhead turtle, Caretta caretta", "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "mud turtle", "terrapin", "box turtle, box tortoise", "banded gecko", "common iguana, iguana, Iguana iguana", "American chameleon, anole, Anolis carolinensis", "whiptail, whiptail lizard", "agama", "frilled lizard, Chlamydosaurus kingi", "alligator lizard", "Gila monster, Heloderma suspectum", "green lizard, Lacerta viridis", "African chameleon, Chamaeleo chamaeleon", "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "African crocodile, Nile crocodile, Crocodylus niloticus", "American alligator, Alligator mississipiensis", "triceratops", "thunder snake, worm snake, Carphophis amoenus", "ringneck snake, ring-necked snake, ring snake", "hognose snake, puff adder, sand viper", "green snake, grass snake", "king snake, kingsnake", "garter snake, grass snake", "water snake", "vine snake", "night snake, Hypsiglena torquata", "boa constrictor, Constrictor constrictor", "rock python, rock snake, Python sebae", "Indian cobra, Naja naja", "green mamba", "sea snake", "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "diamondback, diamondback rattlesnake, Crotalus adamanteus", "sidewinder, horned rattlesnake, Crotalus cerastes", "trilobite", "harvestman, daddy longlegs, Phalangium opilio", "scorpion", "black and gold garden spider, Argiope aurantia", "barn spider, Araneus cavaticus", "garden spider, Aranea diademata", "black widow, Latrodectus mactans", "tarantula", "wolf spider, hunting spider", "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse, partridge, Bonasa umbellus", "prairie chicken, prairie grouse, prairie fowl", "peacock", "quail", "partridge", "African grey, African gray, Psittacus erithacus", "macaw", "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "lorikeet", "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "drake", "red-breasted merganser, Mergus serrator", "goose", "black swan, Cygnus atratus", "tusker", "echidna, spiny anteater, anteater", "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "wallaby, brush kangaroo", "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "wombat", "jelly fish", "sea anemone, anemone", "brain coral", "flatworm, platyhelminth", "nematode, nematode worm, roundworm", "conch", "snail", "slug", "sea slug, nudibranch", "chiton, coat-of-mail shell, sea cradle, polyplacophore", "chambered nautilus, pearly nautilus, nautilus", "Dungeness crab, Cancer magister", "rock crab, Cancer irroratus", "fiddler crab", "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "American lobster, Northern lobster, Maine lobster, Homarus americanus", "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "crayfish, crawfish, crawdad, crawdaddy", "hermit crab", "isopod", "white stork, Ciconia ciconia", "black stork, Ciconia nigra", "spoonbill", "flamingo", "little blue heron, Egretta caerulea", "American egret, great white heron, Egretta albus", "bittern", "crane", "limpkin, Aramus pictus", "European gallinule, Porphyrio porphyrio", "American coot, marsh hen, mud hen, water hen, Fulica americana", "bustard", "ruddy turnstone, Arenaria interpres", "red-backed sandpiper, dunlin, Erolia alpina", "redshank, Tringa totanus", "dowitcher", "oystercatcher, oyster catcher", "pelican", "king penguin, Aptenodytes patagonica", "albatross, mollymawk", "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "dugong, Dugong dugon", "sea lion", "Chihuahua", "Japanese spaniel", "Maltese dog, Maltese terrier, Maltese", "Pekinese, Pekingese, Peke", "Shih-Tzu", "Blenheim spaniel", "papillon", "toy terrier", "Rhodesian ridgeback", "Afghan hound, Afghan", "basset, basset hound", "beagle", "bloodhound, sleuthhound", "bluetick", "black-and-tan coonhound", "Walker hound, Walker foxhound", "English foxhound", "redbone", "borzoi, Russian wolfhound", "Irish wolfhound", "Italian greyhound", "whippet", "Ibizan hound, Ibizan Podenco", "Norwegian elkhound, elkhound", "otterhound, otter hound", "Saluki, gazelle hound", "Scottish deerhound, deerhound", "Weimaraner", "Staffordshire bullterrier, Staffordshire bull terrier", "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "Bedlington terrier", "Border terrier", "Kerry blue terrier", "Irish terrier", "Norfolk terrier", "Norwich terrier", "Yorkshire terrier", "wire-haired fox terrier", "Lakeland terrier", "Sealyham terrier, Sealyham", "Airedale, Airedale terrier", "cairn, cairn terrier", "Australian terrier", "Dandie Dinmont, Dandie Dinmont terrier", "Boston bull, Boston terrier", "miniature schnauzer", "giant schnauzer", "standard schnauzer", "Scotch terrier, Scottish terrier, Scottie", "Tibetan terrier, chrysanthemum dog", "silky terrier, Sydney silky", "soft-coated wheaten terrier", "West Highland white terrier", "Lhasa, Lhasa apso", "flat-coated retriever", "curly-coated retriever", "golden retriever", "Labrador retriever", "Chesapeake Bay retriever", "German short-haired pointer", "vizsla, Hungarian pointer", "English setter", "Irish setter, red setter", "Gordon setter", "Brittany spaniel", "clumber, clumber spaniel", "English springer, English springer spaniel", "Welsh springer spaniel", "cocker spaniel, English cocker spaniel, cocker", "Sussex spaniel", "Irish water spaniel", "kuvasz", "schipperke", "groenendael", "malinois", "briard", "kelpie", "komondor", "Old English sheepdog, bobtail", "Shetland sheepdog, Shetland sheep dog, Shetland", "collie", "Border collie", "Bouvier des Flandres, Bouviers des Flandres", "Rottweiler", "German shepherd, German shepherd dog, German police dog, alsatian", "Doberman, Doberman pinscher", "miniature pinscher", "Greater Swiss Mountain dog", "Bernese mountain dog", "Appenzeller", "EntleBucher", "boxer", "bull mastiff", "Tibetan mastiff", "French bulldog", "Great Dane", "Saint Bernard, St Bernard", "Eskimo dog, husky", "malamute, malemute, Alaskan malamute", "Siberian husky", "dalmatian, coach dog, carriage dog", "affenpinscher, monkey pinscher, monkey dog", "basenji", "pug, pug-dog", "Leonberg", "Newfoundland, Newfoundland dog", "Great Pyrenees", "Samoyed, Samoyede", "Pomeranian", "chow, chow chow", "keeshond", "Brabancon griffon", "Pembroke, Pembroke Welsh corgi", "Cardigan, Cardigan Welsh corgi", "toy poodle", "miniature poodle", "standard poodle", "Mexican hairless", "timber wolf, grey wolf, gray wolf, Canis lupus", "white wolf, Arctic wolf, Canis lupus tundrarum", "red wolf, maned wolf, Canis rufus, Canis niger", "coyote, prairie wolf, brush wolf, Canis latrans", "dingo, warrigal, warragal, Canis dingo", "dhole, Cuon alpinus", "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "hyena, hyaena", "red fox, Vulpes vulpes", "kit fox, Vulpes macrotis", "Arctic fox, white fox, Alopex lagopus", "grey fox, gray fox, Urocyon cinereoargenteus", "tabby, tabby cat", "tiger cat", "Persian cat", "Siamese cat, Siamese", "Egyptian cat", "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "lynx, catamount", "leopard, Panthera pardus", "snow leopard, ounce, Panthera uncia", "jaguar, panther, Panthera onca, Felis onca", "lion, king of beasts, Panthera leo", "tiger, Panthera tigris", "cheetah, chetah, Acinonyx jubatus", "brown bear, bruin, Ursus arctos", "American black bear, black bear, Ursus americanus, Euarctos americanus", "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "sloth bear, Melursus ursinus, Ursus ursinus", "mongoose", "meerkat, mierkat", "tiger beetle", "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "ground beetle, carabid beetle", "long-horned beetle, longicorn, longicorn beetle", "leaf beetle, chrysomelid", "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant, emmet, pismire", "grasshopper, hopper", "cricket", "walking stick, walkingstick, stick insect", "cockroach, roach", "mantis, mantid", "cicada, cicala", "leafhopper", "lacewing, lacewing fly", "dragonfly, darning needle, devil\"s darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "damselfly", "admiral", "ringlet, ringlet butterfly", "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "cabbage butterfly", "sulphur butterfly, sulfur butterfly", "lycaenid, lycaenid butterfly", "starfish, sea star", "sea urchin", "sea cucumber, holothurian", "wood rabbit, cottontail, cottontail rabbit", "hare", "Angora, Angora rabbit", "hamster", "porcupine, hedgehog", "fox squirrel, eastern fox squirrel, Sciurus niger", "marmot", "beaver", "guinea pig, Cavia cobaya", "sorrel", "zebra", "hog, pig, grunter, squealer, Sus scrofa", "wild boar, boar, Sus scrofa", "warthog", "hippopotamus, hippo, river horse, Hippopotamus amphibius", "ox", "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "bison", "ram, tup", "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "ibex, Capra ibex", "hartebeest", "impala, Aepyceros melampus", "gazelle", "Arabian camel, dromedary, Camelus dromedarius", "llama", "weasel", "mink", "polecat, fitch, foulmart, foumart, Mustela putorius", "black-footed ferret, ferret, Mustela nigripes", "otter", "skunk, polecat, wood pussy", "badger", "armadillo", "three-toed sloth, ai, Bradypus tridactylus", "orangutan, orang, orangutang, Pongo pygmaeus", "gorilla, Gorilla gorilla", "chimpanzee, chimp, Pan troglodytes", "gibbon, Hylobates lar", "siamang, Hylobates syndactylus, Symphalangus syndactylus", "guenon, guenon monkey", "patas, hussar monkey, Erythrocebus patas", "baboon", "macaque", "langur", "colobus, colobus monkey", "proboscis monkey, Nasalis larvatus", "marmoset", "capuchin, ringtail, Cebus capucinus", "howler monkey, howler", "titi, titi monkey", "spider monkey, Ateles geoffroyi", "squirrel monkey, Saimiri sciureus", "Madagascar cat, ring-tailed lemur, Lemur catta", "indri, indris, Indri indri, Indri brevicaudatus", "Indian elephant, Elephas maximus", "African elephant, Loxodonta africana", "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "barracouta, snoek", "eel", "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "rock beauty, Holocanthus tricolor", "anemone fish", "sturgeon", "gar, garfish, garpike, billfish, Lepisosteus osseus", "lionfish", "puffer, pufferfish, blowfish, globefish", "abacus", "abaya", "academic gown, academic robe, judge\"s robe", "accordion, piano accordion, squeeze box", "acoustic guitar", "aircraft carrier, carrier, flattop, attack aircraft carrier", "airliner", "airship, dirigible", "altar", "ambulance", "amphibian, amphibious vehicle", "analog clock", "apiary, bee house", "apron", "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "assault rifle, assault gun", "backpack, back pack, knapsack, packsack, rucksack, haversack", "bakery, bakeshop, bakehouse", "balance beam, beam", "balloon", "ballpoint, ballpoint pen, ballpen, Biro", "Band Aid", "banjo", "bannister, banister, balustrade, balusters, handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel, cask", "barrow, garden cart, lawn cart, wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "bathing cap, swimming cap", "bath towel", "bathtub, bathing tub, bath, tub", "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "beacon, lighthouse, beacon light, pharos", "beaker", "bearskin, busby, shako", "beer bottle", "beer glass", "bell cote, bell cot", "bib", "bicycle-built-for-two, tandem bicycle, tandem", "bikini, two-piece", "binder, ring-binder", "binoculars, field glasses, opera glasses", "birdhouse", "boathouse", "bobsled, bobsleigh, bob", "bolo tie, bolo, bola tie, bola", "bonnet, poke bonnet", "bookcase", "bookshop, bookstore, bookstall", "bottlecap", "bow", "bow tie, bow-tie, bowtie", "brass, memorial tablet, plaque", "brassiere, bra, bandeau", "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "breastplate, aegis, egis", "broom", "bucket, pail", "buckle", "bulletproof vest", "bullet train, bullet", "butcher shop, meat market", "cab, hack, taxi, taxicab", "caldron, cauldron", "candle, taper, wax light", "cannon", "canoe", "can opener, tin opener", "cardigan", "car mirror", "carousel, carrousel, merry-go-round, roundabout, whirligig", "carpenter\"s kit, tool kit", "carton", "car wheel", "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "cassette", "cassette player", "castle", "catamaran", "CD player", "cello, violoncello", "cellular telephone, cellular phone, cellphone, cell, mobile phone", "chain", "chainlink fence", "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "chain saw, chainsaw", "chest", "chiffonier, commode", "chime, bell, gong", "china cabinet, china closet", "Christmas stocking", "church, church building", "cinema, movie theater, movie theatre, movie house, picture palace", "cleaver, meat cleaver, chopper", "cliff dwelling", "cloak", "clog, geta, patten, sabot", "cocktail shaker", "coffee mug", "coffeepot", "coil, spiral, volute, whorl, helix", "combination lock", "computer keyboard, keypad", "confectionery, confectionary, candy store", "container ship, containership, container vessel", "convertible", "corkscrew, bottle screw", "cornet, horn, trumpet, trump", "cowboy boot", "cowboy hat, ten-gallon hat", "cradle", "crane", "crash helmet", "crate", "crib, cot", "Crock Pot", "croquet ball", "crutch", "cuirass", "dam, dike, dyke", "desk", "desktop computer", "dial telephone, dial phone", "diaper, nappy, napkin", "digital clock", "digital watch", "dining table, board", "dishrag, dishcloth", "dishwasher, dish washer, dishwashing machine", "disk brake, disc brake", "dock, dockage, docking facility", "dogsled, dog sled, dog sleigh", "dome", "doormat, welcome mat", "drilling platform, offshore rig", "drum, membranophone, tympan", "drumstick", "dumbbell", "Dutch oven", "electric fan, blower", "electric guitar", "electric locomotive", "entertainment center", "envelope", "espresso maker", "face powder", "feather boa, boa", "file, file cabinet, filing cabinet", "fireboat", "fire engine, fire truck", "fire screen, fireguard", "flagpole, flagstaff", "flute, transverse flute", "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster", "freight car", "French horn, horn", "frying pan, frypan, skillet", "fur coat", "garbage truck, dustcart", "gasmask, respirator, gas helmet", "gas pump, gasoline pump, petrol pump, island dispenser", "goblet", "go-kart", "golf ball", "golfcart, golf cart", "gondola", "gong, tam-tam", "gown", "grand piano, grand", "greenhouse, nursery, glasshouse", "grille, radiator grille", "grocery store, grocery, food market, market", "guillotine", "hair slide", "hair spray", "half track", "hammer", "hamper", "hand blower, blow dryer, blow drier, hair dryer, hair drier", "hand-held computer, hand-held microcomputer", "handkerchief, hankie, hanky, hankey", "hard disc, hard disk, fixed disk", "harmonica, mouth organ, harp, mouth harp", "harp", "harvester, reaper", "hatchet", "holster", "home theater, home theatre", "honeycomb", "hook, claw", "hoopskirt, crinoline", "horizontal bar, high bar", "horse cart, horse-cart", "hourglass", "iPod", "iron, smoothing iron", "jack-o\"-lantern", "jean, blue jean, denim", "jeep, landrover", "jersey, T-shirt, tee shirt", "jigsaw puzzle", "jinrikisha, ricksha, rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat, laboratory coat", "ladle", "lampshade, lamp shade", "laptop, laptop computer", "lawn mower, mower", "lens cap, lens cover", "letter opener, paper knife, paperknife", "library", "lifeboat", "lighter, light, igniter, ignitor", "limousine, limo", "liner, ocean liner", "lipstick, lip rouge", "Loafer", "lotion", "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "loupe, jeweler\"s loupe", "lumbermill, sawmill", "magnetic compass", "mailbag, postbag", "mailbox, letter box", "maillot", "maillot, tank suit", "manhole cover", "maraca", "marimba, xylophone", "mask", "matchstick", "maypole", "maze, labyrinth", "measuring cup", "medicine chest, medicine cabinet", "megalith, megalithic structure", "microphone, mike", "microwave, microwave oven", "military uniform", "milk can", "minibus", "miniskirt, mini", "minivan", "missile", "mitten", "mixing bowl", "mobile home, manufactured home", "Model T", "modem", "monastery", "monitor", "moped", "mortar", "mortarboard", "mosque", "mosquito net", "motor scooter, scooter", "mountain bike, all-terrain bike, off-roader", "mountain tent", "mouse, computer mouse", "mousetrap", "moving van", "muzzle", "nail", "neck brace", "necklace", "nipple", "notebook, notebook computer", "obelisk", "oboe, hautboy, hautbois", "ocarina, sweet potato", "odometer, hodometer, mileometer, milometer", "oil filter", "organ, pipe organ", "oscilloscope, scope, cathode-ray oscilloscope, CRO", "overskirt", "oxcart", "oxygen mask", "packet", "paddle, boat paddle", "paddlewheel, paddle wheel", "padlock", "paintbrush", "pajama, pyjama, pj\"s, jammies", "palace", "panpipe, pandean pipe, syrinx", "paper towel", "parachute, chute", "parallel bars, bars", "park bench", "parking meter", "passenger car, coach, carriage", "patio, terrace", "pay-phone, pay-station", "pedestal, plinth, footstall", "pencil box, pencil case", "pencil sharpener", "perfume, essence", "Petri dish", "photocopier", "pick, plectrum, plectron", "pickelhaube", "picket fence, paling", "pickup, pickup truck", "pier", "piggy bank, penny bank", "pill bottle", "pillow", "ping-pong ball", "pinwheel", "pirate, pirate ship", "pitcher, ewer", "plane, carpenter\"s plane, woodworking plane", "planetarium", "plastic bag", "plate rack", "plow, plough", "plunger, plumber\"s helper", "Polaroid camera, Polaroid Land camera", "pole", "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "poncho", "pool table, billiard table, snooker table", "pop bottle, soda bottle", "pot, flowerpot", "potter\"s wheel", "power drill", "prayer rug, prayer mat", "printer", "prison, prison house", "projectile, missile", "projector", "puck, hockey puck", "punching bag, punch bag, punching ball, punchball", "purse", "quill, quill pen", "quilt, comforter, comfort, puff", "racer, race car, racing car", "racket, racquet", "radiator", "radio, wireless", "radio telescope, radio reflector", "rain barrel", "recreational vehicle, RV, R.V.", "reel", "reflex camera", "refrigerator, icebox", "remote control, remote", "restaurant, eating house, eating place, eatery", "revolver, six-gun, six-shooter", "rifle", "rocking chair, rocker", "rotisserie", "rubber eraser, rubber, pencil eraser", "rugby ball", "rule, ruler", "running shoe", "safe", "safety pin", "saltshaker, salt shaker", "sandal", "sarong", "sax, saxophone", "scabbard", "scale, weighing machine", "school bus", "schooner", "scoreboard", "screen, CRT screen", "screw", "screwdriver", "seat belt, seatbelt", "sewing machine", "shield, buckler", "shoe shop, shoe-shop, shoe store", "shoji", "shopping basket", "shopping cart", "shovel", "shower cap", "shower curtain", "ski", "ski mask", "sleeping bag", "slide rule, slipstick", "sliding door", "slot, one-armed bandit", "snorkel", "snowmobile", "snowplow, snowplough", "soap dispenser", "soccer ball", "sock", "solar dish, solar collector, solar furnace", "sombrero", "soup bowl", "space bar", "space heater", "space shuttle", "spatula", "speedboat", "spider web, spider\"s web", "spindle", "sports car, sport car", "spotlight, spot", "stage", "steam locomotive", "steel arch bridge", "steel drum", "stethoscope", "stole", "stone wall", "stopwatch, stop watch", "stove", "strainer", "streetcar, tram, tramcar, trolley, trolley car", "stretcher", "studio couch, day bed", "stupa, tope", "submarine, pigboat, sub, U-boat", "suit, suit of clothes", "sundial", "sunglass", "sunglasses, dark glasses, shades", "sunscreen, sunblock, sun blocker", "suspension bridge", "swab, swob, mop", "sweatshirt", "swimming trunks, bathing trunks", "swing", "switch, electric switch, electrical switch", "syringe", "table lamp", "tank, army tank, armored combat vehicle, armoured combat vehicle", "tape player", "teapot", "teddy, teddy bear", "television, television system", "tennis ball", "thatch, thatched roof", "theater curtain, theatre curtain", "thimble", "thresher, thrasher, threshing machine", "throne", "tile roof", "toaster", "tobacco shop, tobacconist shop, tobacconist", "toilet seat", "torch", "totem pole", "tow truck, tow car, wrecker", "toyshop", "tractor", "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "tray", "trench coat", "tricycle, trike, velocipede", "trimaran", "tripod", "triumphal arch", "trolleybus, trolley coach, trackless trolley", "trombone", "tub, vat", "turnstile", "typewriter keyboard", "umbrella", "unicycle, monocycle", "upright, upright piano", "vacuum, vacuum cleaner", "vase", "vault", "velvet", "vending machine", "vestment", "viaduct", "violin, fiddle", "volleyball", "waffle iron", "wall clock", "wallet, billfold, notecase, pocketbook", "wardrobe, closet, press", "warplane, military plane", "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "washer, automatic washer, washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle", "wig", "window screen", "window shade", "Windsor tie", "wine bottle", "wing", "wok", "wooden spoon", "wool, woolen, woollen", "worm fence, snake fence, snake-rail fence, Virginia fence", "wreck", "yawl", "yurt", "web site, website, internet site, site", "comic book", "crossword puzzle, crossword", "street sign", "traffic light, traffic signal, stoplight", "book jacket, dust cover, dust jacket, dust wrapper", "menu", "plate", "guacamole", "consomme", "hot pot, hotpot", "trifle", "ice cream, icecream", "ice lolly, lolly, lollipop, popsicle", "French loaf", "bagel, beigel", "pretzel", "cheeseburger", "hotdog, hot dog, red hot", "mashed potato", "head cabbage", "broccoli", "cauliflower", "zucchini, courgette", "spaghetti squash", "acorn squash", "butternut squash", "cucumber, cuke", "artichoke, globe artichoke", "bell pepper", "cardoon", "mushroom", "Granny Smith", "strawberry", "orange", "lemon", "fig", "pineapple, ananas", "banana", "jackfruit, jak, jack", "custard apple", "pomegranate", "hay", "carbonara", "chocolate sauce, chocolate syrup", "dough", "meat loaf, meatloaf", "pizza, pizza pie", "potpie", "burrito", "red wine", "espresso", "cup", "eggnog", "alp", "bubble", "cliff, drop, drop-off", "coral reef", "geyser", "lakeside, lakeshore", "promontory, headland, head, foreland", "sandbar, sand bar", "seashore, coast, seacoast, sea-coast", "valley, vale", "volcano", "ballplayer, baseball player", "groom, bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady\"s slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "corn", "acorn", "hip, rose hip, rosehip", "buckeye, horse chestnut, conker", "coral fungus", "agaric", "gyromitra", "stinkhorn, carrion fungus", "earthstar", "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "bolete", "ear, spike, capitulum", "toilet tissue, toilet paper, bathroom tissue" ]; ================================================ FILE: pytorch-mobilenet-image/rust/src/main.rs ================================================ use image; use std::env; use std::fs::File; use std::io::Read; use wasi_nn; mod imagenet_classes; pub fn main() { let args: Vec = env::args().collect(); let model_bin_name: &str = &args[1]; let image_name: &str = &args[2]; let graph = wasi_nn::GraphBuilder::new( wasi_nn::GraphEncoding::Pytorch, wasi_nn::ExecutionTarget::CPU, ) .build_from_files([model_bin_name]) .unwrap(); println!("Loaded graph into wasi-nn with ID: {:?}", graph); let mut context = graph.init_execution_context().unwrap(); println!("Created wasi-nn execution context with ID: {:?}", context); // Load a tensor that precisely matches the graph input tensor (see let tensor_data = image_to_tensor(image_name.to_string(), 224, 224); println!("Read input tensor, size in bytes: {}", tensor_data.len()); context .set_input(0, wasi_nn::TensorType::F32, &[1, 3, 224, 224], &tensor_data) .unwrap(); // Execute the inference. context.compute().unwrap(); println!("Executed graph inference"); // Retrieve the output. let mut output_buffer = vec![0f32; 1000]; context.get_output(0, &mut output_buffer).unwrap(); let results = sort_results(&output_buffer); for i in 0..5 { println!( " {}.) [{}]({:.4}){}", i + 1, results[i].0, results[i].1, imagenet_classes::IMAGENET_CLASSES[results[i].0] ); } } // Sort the buffer of probabilities. The graph places the match probability for each class at the // index for that class (e.g. the probability of class 42 is placed at buffer[42]). Here we convert // to a wrapping InferenceResult and sort the results. fn sort_results(buffer: &[f32]) -> Vec { let mut results: Vec = buffer .iter() .enumerate() .map(|(c, p)| InferenceResult(c, *p)) .collect(); results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); results } // Take the image located at 'path', open it, resize it to height x width, and then converts // the pixel precision to FP32. The resulting BGR pixel vector is then returned. fn image_to_tensor(path: String, height: u32, width: u32) -> Vec { let mut file_img = File::open(path).unwrap(); let mut img_buf = Vec::new(); file_img.read_to_end(&mut img_buf).unwrap(); let img = image::load_from_memory(&img_buf).unwrap().to_rgb8(); let resized = image::imageops::resize(&img, height, width, ::image::imageops::FilterType::Triangle); let mut flat_img: Vec = Vec::new(); for rgb in resized.pixels() { flat_img.push((rgb[0] as f32 / 255. - 0.485) / 0.229); flat_img.push((rgb[1] as f32 / 255. - 0.456) / 0.224); flat_img.push((rgb[2] as f32 / 255. - 0.406) / 0.225); } let bytes_required = flat_img.len() * 4; let mut u8_f32_arr: Vec = vec![0; bytes_required]; for c in 0..3 { for i in 0..(flat_img.len() / 3) { // Read the number as a f32 and break it into u8 bytes let u8_f32: f32 = flat_img[i * 3 + c] as f32; let u8_bytes = u8_f32.to_ne_bytes(); for j in 0..4 { u8_f32_arr[((flat_img.len() / 3 * c + i) * 4) + j] = u8_bytes[j]; } } } return u8_f32_arr; } // A wrapper for class ID and match probabilities. #[derive(Debug, PartialEq)] struct InferenceResult(usize, f32); ================================================ FILE: pytorch-mobilenet-image/rust/src/named_model.rs ================================================ use image; use std::env; use std::fs::File; use std::io::Read; use wasi_nn; mod imagenet_classes; pub fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; let image_name: &str = &args[2]; let graph = wasi_nn::GraphBuilder::new( wasi_nn::GraphEncoding::Autodetec, wasi_nn::ExecutionTarget::CPU, ) .build_from_cache(model_name) .unwrap(); println!("Loaded graph into wasi-nn with ID: {:?}", graph); let mut context = graph.init_execution_context().unwrap(); println!("Created wasi-nn execution context with ID: {:?}", context); // Load a tensor that precisely matches the graph input tensor (see let tensor_data = image_to_tensor(image_name.to_string(), 224, 224); println!("Read input tensor, size in bytes: {}", tensor_data.len()); context .set_input(0, wasi_nn::TensorType::F32, &[1, 3, 224, 224], &tensor_data) .unwrap(); // Execute the inference. context.compute().unwrap(); println!("Executed graph inference"); // Retrieve the output. let mut output_buffer = vec![0f32; 1000]; context.get_output(0, &mut output_buffer).unwrap(); let results = sort_results(&output_buffer); for i in 0..5 { println!( " {}.) [{}]({:.4}){}", i + 1, results[i].0, results[i].1, imagenet_classes::IMAGENET_CLASSES[results[i].0] ); } } // Sort the buffer of probabilities. The graph places the match probability for each class at the // index for that class (e.g. the probability of class 42 is placed at buffer[42]). Here we convert // to a wrapping InferenceResult and sort the results. fn sort_results(buffer: &[f32]) -> Vec { let mut results: Vec = buffer .iter() .enumerate() .map(|(c, p)| InferenceResult(c, *p)) .collect(); results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); results } // Take the image located at 'path', open it, resize it to height x width, and then converts // the pixel precision to FP32. The resulting BGR pixel vector is then returned. fn image_to_tensor(path: String, height: u32, width: u32) -> Vec { let mut file_img = File::open(path).unwrap(); let mut img_buf = Vec::new(); file_img.read_to_end(&mut img_buf).unwrap(); let img = image::load_from_memory(&img_buf).unwrap().to_rgb8(); let resized = image::imageops::resize(&img, height, width, ::image::imageops::FilterType::Triangle); let mut flat_img: Vec = Vec::new(); for rgb in resized.pixels() { flat_img.push((rgb[0] as f32 / 255. - 0.485) / 0.229); flat_img.push((rgb[1] as f32 / 255. - 0.456) / 0.224); flat_img.push((rgb[2] as f32 / 255. - 0.406) / 0.225); } let bytes_required = flat_img.len() * 4; let mut u8_f32_arr: Vec = vec![0; bytes_required]; for c in 0..3 { for i in 0..(flat_img.len() / 3) { // Read the number as a f32 and break it into u8 bytes let u8_f32: f32 = flat_img[i * 3 + c] as f32; let u8_bytes = u8_f32.to_ne_bytes(); for j in 0..4 { u8_f32_arr[((flat_img.len() / 3 * c + i) * 4) + j] = u8_bytes[j]; } } } return u8_f32_arr; } // A wrapper for class ID and match probabilities. #[derive(Debug, PartialEq)] struct InferenceResult(usize, f32); ================================================ FILE: pytorch-yolo-image/README.md ================================================ # YOLOV8 detection Example For WASI-NN with PyTorch Backend Code for a working example of object detection using the yolov8 model. ### Requirements `pip3 install torchvision ultralytics` To get the Pytorch YOLOV8 model please use the `get_model.py` script provided. Alternatively: pre-trained weights can be downloaded directly from github and will need to be converted to torchscript https://github.com/ultralytics/assets/releases ### Build + Run To build (from the `/rust` directory): `cargo build --release --target wasm32-wasip1` To compile the WASM AOT (Ahead of Time) - this results in a much more performant binary ` wasmedge compile ./target/wasm32-wasip1/release/wasmedge-wasinn-example-yolo-image.wasm ./target/wasm32-wasip1/release/wasmedge-wasinn-example-yolo-image.wasm ` To Run (from root `/` directory of the example): ` wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-yolo-image.wasm ./yolov8n.torchscript ./input.jpg ` > **Note** > Non-Maximum supression has not been applied to the output, therefore there will be many results with very close pixel bounds ================================================ FILE: pytorch-yolo-image/get_model.py ================================================ # requires ultralytics to be installed from ultralytics import YOLO # Load a model, saving it to disk # Note there are different net sizes, n, m, l, x # Larger models are more accurate, but slower model = YOLO('yolov8n.pt') # Export the model as a torchscript file model.export(format='torchscript') ================================================ FILE: pytorch-yolo-image/rust/Cargo.toml ================================================ [package] name = "wasmedge-wasinn-example-yolo-image" version = "0.1.0" authors = ["Second-State"] readme = "README.md" edition = "2021" publish = false # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html [dependencies] image = { version = "0.24.7", default-features = false, features = [ "gif", "jpeg", "ico", "png", "pnm", "tga", "tiff", "webp", "bmp", "hdr", "dxt", "dds", "farbfeld", ] } wasi-nn = { version = "0.6.0" } ================================================ FILE: pytorch-yolo-image/rust/src/main.rs ================================================ use image::{self, GenericImage, RgbImage}; use std::env; use std::fs::File; use std::io::Read; use wasi_nn; use crate::yolo_classes::YOLO_CLASSES; mod yolo_classes; pub fn main() { let args: Vec = env::args().collect(); let model_bin_name: &str = &args[1]; let image_name: &str = &args[2]; println!("model_bin_name {}", model_bin_name); println!("image_name {}", image_name); let graph = wasi_nn::GraphBuilder::new( wasi_nn::GraphEncoding::Pytorch, wasi_nn::ExecutionTarget::CPU, ) .build_from_files([model_bin_name]) .unwrap(); println!("Loaded graph into wasi-nn with ID: {:?}", graph); let mut context = graph.init_execution_context().unwrap(); println!("Created wasi-nn execution context with ID: {:?}", context); // Load a tensor that precisely matches the graph input tensor dimensions // Graph expects 1,3,640,640 dimensional tensor let (raw_processed, resize_scale) = pre_process_image(image_name.to_string()); let tensor_data = raw_processed .into_iter() .flatten() .collect::>>() .into_iter() .flatten() .collect::>(); println!("Read input tensor, size in bytes: {}", tensor_data.len()); context .set_input( 0, wasi_nn::TensorType::F32, // Input &[1, 3, SIZE, SIZE], &tensor_data, ) .unwrap(); // // Execute the inference. context.compute().unwrap(); println!("Executed graph inference"); // Retrieve the output. // YOLO's output Tensor has a dimension of 1*84*8400 // 1 : Batch Size // 84 : x,y,w,h, P1, P2, P3, P4, // 8400 : number of total objects YoloV8 can detect per frame let mut output_buffer = vec![0f32; 1 * 84 * 8400]; context.get_output(0, &mut output_buffer).unwrap(); let confidence_threshold = 0.5; let results = post_process_results(&output_buffer, confidence_threshold, resize_scale); for result in results { println!("{:?}", result); } } // Function to normalize and resize image for YOLO const SIZE: usize = 640; const SIZE_U32: u32 = 640; type Channel = Vec>; struct ResizeScale(pub f32); fn pre_process_image(path: String) -> ([Channel; 3], ResizeScale) { let mut file_img = File::open(path).unwrap(); let mut img_buf = Vec::new(); file_img.read_to_end(&mut img_buf).unwrap(); let img = image::load_from_memory(&img_buf).unwrap().to_rgb8(); let (height, width); let length = img.width().max(img.height()); let scale: ResizeScale = ResizeScale(length as f32 / SIZE_U32 as f32); if img.width() > img.height() { // height is the shorter length height = SIZE_U32 * img.height() / img.width(); width = SIZE_U32; } else { // width is the shorter length width = SIZE_U32 * img.width() / img.height(); height = SIZE_U32; } let resized: image::ImageBuffer, Vec> = image::imageops::resize(&img, width, height, ::image::imageops::FilterType::Triangle); // We need the image to fit the 640 x 640 size, // and we want to keep the aspect ratio of the original image // So we fill the remaining pixels with black, let mut resized_640x640 = RgbImage::new(SIZE_U32, SIZE_U32); resized_640x640.copy_from(&resized, 0, 0).unwrap(); // Split intoChannels let mut red: Channel = vec![vec![0.0; SIZE]; SIZE]; let mut blue: Channel = vec![vec![0.0; SIZE]; SIZE]; let mut green: Channel = vec![vec![0.0; SIZE]; SIZE]; for (_, pixel) in resized_640x640.enumerate_rows() { for (x, y, rgb) in pixel { let x = x as usize; let y = y as usize; red[y][x] = rgb.0[0] as f32 / 255.0; green[y][x] = rgb.0[1] as f32 / 255.0; blue[y][x] = rgb.0[2] as f32 / 255.0; } } let final_tensor: [Vec>; 3]; final_tensor = [red, green, blue]; (final_tensor, scale) } // Function to process output tensor from YOLO fn post_process_results( buffer: &[f32], conf_threshold: f32, scale: ResizeScale, ) -> Vec { // Output buffer is in columar format // 84 rows x 8400 columns as a single Vec of f32 let mut columns = Vec::new(); for col_slice in buffer.chunks_exact(8400) { let col_vec = col_slice.to_vec(); columns.push(col_vec); } let rows = transpose(columns); // Row Format is // [x,y,w,h,p1,p2,p3...p80] // where: // x,y are the pixel locations of the top left corner of the bounding box, // w,h are the width and height of bounding box, // p1,p2..p80, are the class probabilities. let mut results = Vec::new(); for row in rows { // Get maximum likeliehood for each detection // Iterator of only class probabilities let mut prob_iter = row.clone().into_iter().skip(4); let max = prob_iter.clone().reduce(|a, b| a.max(b)).unwrap(); if max < conf_threshold { continue; } let index = prob_iter.position(|element| element == max).unwrap(); let class = YOLO_CLASSES.get(index).unwrap().to_string(); let raw_w = row[2]; let raw_h = row[3]; let x = ((row[0] - 0.5 * raw_w) * scale.0).round() as u32; let y = ((row[1] - 0.5 * raw_h) * scale.0).round() as u32; let w = (raw_w * scale.0).round() as u32; let h = (raw_h * scale.0).round() as u32; results.push(InferenceResult { x, y, width: w, height: h, probability: max, class, }); } results } // Output data is in the form 84 Rows x 8400 columns fn transpose(v: Vec>) -> Vec> { assert!(!v.is_empty()); let len = v[0].len(); let mut iters: Vec<_> = v.into_iter().map(|n| n.into_iter()).collect(); (0..len) .map(|_| { iters .iter_mut() .map(|n| n.next().unwrap()) .collect::>() }) .collect() } // Struct to return: // Location Top left pixel of bounding box, // Width + Height of bounding box // Probability and Class Name #[derive(Debug)] #[allow(dead_code)] struct InferenceResult { x: u32, y: u32, width: u32, height: u32, class: String, probability: f32, } ================================================ FILE: pytorch-yolo-image/rust/src/yolo_classes.rs ================================================ pub const YOLO_CLASSES: [&str; 80] = [ "person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", ]; ================================================ FILE: pytorch-yolo-image/yolov8n.torchscript ================================================ [File too large to display: 12.4 MB] ================================================ FILE: scripts/install_libtorch.sh ================================================ #!/usr/bin/env bash # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception # SPDX-FileCopyrightText: 2019-2022 Second State INC if [[ ! -n ${PYTORCH_VERSION} ]]; then PYTORCH_VERSION="1.8.2" fi DOWNLOAD_TO=$1 if [ ! -d $1/libtorch ]; then apt update apt upgrade -y apt-get install unzip wget -y curl -L -O https://download.pytorch.org/libtorch/lts/1.8/cpu/libtorch-cxx11-abi-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip echo "Run Checksum..." echo "b76d6dd4380e2233ce6f7654e672e13aae7c871231d223a4267ef018dcbfb616 libtorch-cxx11-abi-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip" | sha256sum -c unzip -q "libtorch-cxx11-abi-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip" rm -f "libtorch-cxx11-abi-shared-with-deps-${PYTORCH_VERSION}%2Bcpu.zip" echo "Complete install" fi ================================================ FILE: scripts/install_openvino.sh ================================================ #!/usr/bin/env bash set -e echo "Installing OpenVINO with version 2024.2.0" KEY_FILE=GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB wget https://apt.repos.intel.com/intel-gpg-keys/$KEY_FILE && \ apt-key add $KEY_FILE && \ rm -f $KEY_FILE echo "deb https://apt.repos.intel.com/openvino/2024 ubuntu24 main" | tee /etc/apt/sources.list.d/intel-openvino-2024.list apt update apt upgrade -y apt-get -y install openvino-2024.2.0 export PATH=/usr/lib/x86_64-linux-gnu:$PATH ldconfig ================================================ FILE: tflite-birds_v1-image/README.md ================================================ # Mobilenet Example For WASI-NN with Tensorflow Lite Backend This package is a high-level Rust bindings for [wasi-nn] example of Mobilenet with Tensorflow Lite backend. [wasi-nn]: https://github.com/WebAssembly/wasi-nn ## Dependencies This crate depends on the `wasi-nn` in the `Cargo.toml`: ```toml [dependencies] wasi-nn = "0.4.0" ``` ## Build Compile the application to WebAssembly: ```bash cd rust && cargo build --target=wasm32-wasip1 --release ``` The output WASM file will be at [`rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-tflite-bird-image.wasm`](wasmedge-wasinn-example-tflite-bird-image.wasm). To speed up the image processing, we can enable the AOT mode in WasmEdge with: ```bash wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-tflite-bird-image.wasm wasmedge-wasinn-example-tflite-bird-image.wasm ``` ## Run ### Test data The testing image is located at `./bird.jpg`: ![Aix galericulata](bird.jpg) The `tflite` model is located at `./lite-model_aiy_vision_classifier_birds_V1_3.tflite` ### Execute Users should [install the WasmEdge with WASI-NN TensorFlow-Lite backend plug-in](https://wasmedge.org/docs/start/install#wasi-nn-plug-in-with-tensorflow-lite-backend). Execute the WASM with the `wasmedge` with Tensorflow Lite supporting: ```bash wasmedge --dir .:. wasmedge-wasinn-example-tflite-bird-image.wasm lite-model_aiy_vision_classifier_birds_V1_3.tflite bird.jpg ``` You will get the output: ```console Read graph weights, size in bytes: 3561598 Loaded graph into wasi-nn with ID: 0 Created wasi-nn execution context with ID: 0 Read input tensor, size in bytes: 150528 Executed graph inference 1.) [166](198)Aix galericulata 2.) [158](2)Coccothraustes coccothraustes 3.) [34](1)Gallus gallus domesticus 4.) [778](1)Sitta europaea 5.) [819](1)Anas platyrhynchos ``` ================================================ FILE: tflite-birds_v1-image/rust/Cargo.toml ================================================ [package] name = "wasmedge-wasinn-example-tflite-bird-image" version = "0.1.0" authors = ["Second-State"] readme = "README.md" edition = "2021" publish = false [dependencies] image = { version = "0.23.14", default-features = false, features = ["gif", "jpeg", "ico", "png", "pnm", "tga", "tiff", "webp", "bmp", "hdr", "dxt", "dds", "farbfeld"] } wasi-nn = "0.6.0" [workspace] ================================================ FILE: tflite-birds_v1-image/rust/src/imagenet_classes.rs ================================================ /** * @license * Copyright 2019 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ /* The code in this file is adapted from https://github.com/tensorflow/tfjs-models/blob/master/mobilenet/src/imagenet_classes.ts */ pub const AIY_BIRDS_V1: [&str; 965] = [ "Haemorhous cassinii", "Aramus guarauna", "Rupornis magnirostris", "Cyanocitta cristata", "Cyanocitta stelleri", "Balearica regulorum", "Pyrocephalus rubinus", "Recurvirostra americana", "Ardeotis kori", "Pica nuttalli", "Perisoreus canadensis", "Antigone canadensis", "Parkesia noveboracensis", "Ardea herodias occidentalis", "Porzana carolina", "Anas platyrhynchos diazi", "Motacilla cinerea", "Empidonax difficilis", "Empidonax minimus", "Empidonax fulvifrons", "Empidonax traillii", "Empidonax hammondii", "Empidonax occidentalis", "Rallus limicola", "Grus grus", "Quiscalus major", "Branta leucopsis", "Cyanocorax yucatanicus", "Cyanocorax yncas", "Oceanites oceanicus", "Quiscalus niger", "Psilorhinus morio", "Megarynchus pitangua", "Gallinula tenebrosa", "Gallus gallus domesticus", "Numida meleagris", "Junco hyemalis caniceps", "Tyrannus vociferans", "Tyrannus tyrannus", "Tyrannus forficatus", "Tyrannus crassirostris", "Tyrannus verticalis", "Tyrannus savana", "Gallirallus australis", "Calocitta formosa", "Calocitta colliei", "Fulica americana", "Pachyramphus aglaiae", "Buteo lagopus", "Cygnus atratus", "Philesturnus rufusater", "Larus marinus", "Pitangus sulphuratus", "Anas rubripes", "Anthus novaeseelandiae novaeseelandiae", "Sayornis phoebe", "Sayornis nigricans", "Tityra semifasciata", "Dryocopus lineatus", "Porphyrio melanotus", "Anas bahamensis", "Egretta novaehollandiae", "Anas crecca carolinensis", "Alectoris chukar", "Tachybaptus dominicus", "Artemisiospiza belli", "Gallus gallus", "Cardinalis sinuatus", "Cardinalis cardinalis", "Melospiza lincolnii", "Podilymbus podiceps", "Melospiza georgiana", "Meleagris gallopavo", "Meleagris ocellata", "Lagopus lagopus", "Spizella atrogularis", "Pyrrhocorax graculus", "Spizella breweri", "Sialia currucoides", "Spizella pusilla", "Anas superciliosa", "Passerella iliaca", "Phoenicopterus ruber", "Sylvia atricapilla", "Vireo bellii", "Vireo plumbeus", "Vireo philadelphicus", "Vireo flavifrons", "Vireo olivaceus", "Zonotrichia querula", "Vireo huttoni", "Zonotrichia albicollis", "Zonotrichia atricapilla", "Emberiza citrinella", "Bucephala islandica", "Emberiza schoeniclus", "Vireo gilvus", "Serinus serinus", "Serinus mozambicus", "Lophura leucomelanos", "Euphonia elegantissima", "Euphonia hirundinacea", "Euphonia affinis", "Agelaius tricolor", "Momotus lessonii", "Zosterops japonicus", "Phalacrocorax pelagicus", "Icterus cucullatus", "Icterus graduacauda", "Motacilla aguimp", "Anas superciliosa × platyrhynchos", "Icterus pustulatus", "Icterus gularis", "Carduelis cannabina", "Catharus minimus", "Amaurornis phoenicurus", "Pavo cristatus", "Rallus crepitans", "Nestor meridionalis septentrionalis", "Pipilo chlorurus", "Pipilo maculatus", "Pipilo erythrophthalmus", "Cairina moschata domestica", "Phalacrocorax varius", "Picoides dorsalis", "Picoides nuttallii", "Picoides scalaris", "Callipepla gambelii", "Pyrrhula pyrrhula", "Phalacrocorax brasilianus", "Picoides pubescens", "Colinus virginianus", "Sporophila torqueola", "Picoides villosus", "Calidris pusilla", "Picoides arcticus", "Aythya marila", "Phaethon aethereus", "Sturnella magna", "Melopsittacus undulatus", "Aythya affinis", "Aythya novaeseelandiae", "Cyrtonyx montezumae", "Pelecanus occidentalis", "Cinclus cinclus", "Chen rossii", "Callipepla californica", "Quiscalus quiscula", "Quiscalus mexicanus", "Callipepla squamata", "Dryocopus pileatus", "Passerina caerulea", "Amazilia beryllina", "Geothlypis trichas", "Trogon melanocephalus", "Sylvia communis", "Pinicola enucleator", "Coccothraustes vespertinus", "Coccothraustes coccothraustes", "Vermivora chrysoptera", "Opisthocomus hoazin", "Setophaga coronata coronata", "Diglossa baritula", "Saltator coerulescens", "Saltator atriceps", "Sicalis flaveola", "Aix galericulata", "Junco hyemalis hyemalis", "Eupsittula canicularis", "Microcarbo melanoleucos", "Piranga rubra", "Piranga olivacea", "Piranga flava", "Amphispiza bilineata", "Larus dominicanus", "Piaya cayana", "Aimophila ruficeps", "Melanerpes lewis", "Melanerpes uropygialis", "Passerculus sandwichensis", "Melanerpes pucherani", "Salpinctes obsoletus", "Melanerpes carolinus", "Melanerpes chrysogenys", "Melanerpes formicivorus", "Colaptes auratus", "Basileuterus rufifrons", "Larus michahellis", "Ramphocelus passerinii", "Athene cunicularia", "Branta hutchinsii", "Fringilla montifringilla", "Fringilla coelebs", "Junco hyemalis", "Cuculus canorus", "Junco phaeonotus", "Ammodramus nelsoni", "Ammodramus savannarum", "Ammodramus maritimus", "Coccyzus erythropthalmus", "Coccyzus minor", "Coccyzus americanus", "Nucifraga columbiana", "Crotophaga sulcirostris", "Pooecetes gramineus", "Arremonops rufivirgatus", "Geococcyx californianus", "Geococcyx velox", "Coereba flaveola", "Passerina ciris", "Alectura lathami", "Passerina leclancherii", "Passerina amoena", "Icteria virens", "Crax rubra", "Penelope purpurascens", "Copsychus malabaricus", "Paroaria capitata", "Cyanerpes cyaneus", "Microcarbo melanoleucos brevirostris", "Sphyrapicus thyroideus", "Pheucticus ludovicianus", "Sphyrapicus ruber", "Pheucticus melanocephalus", "Sphyrapicus nuchalis", "Ortalis poliocephala", "Ortalis vetula", "Corvus albus", "Mniotilta varia", "Volatinia jacarina", "Thraupis palmarum", "Momotus mexicanus", "Euphagus cyanocephalus", "Cacatua galerita", "Junco hyemalis oreganus", "Pluvialis fulva", "Molothrus aeneus", "Molothrus ater", "Merops pusillus", "Merops apiaster", "Pteroglossus torquatus", "Loxia curvirostra", "Merops orientalis", "Loxia leucoptera", "Coracias garrulus", "Coracias caudatus", "Coracias benghalensis", "Xanthocephalus xanthocephalus", "Chondestes grammacus", "Dolichonyx oryzivorus", "Pandion haliaetus", "Phainopepla nitens", "Tyrannus couchii", "Phylloscopus collybita", "Circus cyaneus hudsonius", "Grallina cyanoleuca", "Plectrophenax nivalis", "Calamospiza melanocorys", "Regulus calendula", "Regulus ignicapilla", "Regulus regulus", "Regulus satrapa", "Tyrannus melancholicus", "Pelecanus conspicillatus", "Aulacorhynchus prasinus", "Todiramphus sanctus", "Icterus bullockii", "Spiza americana", "Tyrannus dominicensis", "Chlidonias niger", "Oriturus superciliosus", "Psarocolius montezuma", "Turdus pilaris", "Notiomystis cincta", "Sagittarius serpentarius", "Psittacula krameri", "Platycercus eximius", "Protonotaria citrea", "Megaceryle torquata", "Nestor meridionalis", "Amazona viridigenalis", "Amazona albifrons", "Amazona oratrix", "Saxicola rubetra", "Ara militaris", "Spizelloides arborea", "Ceryle rudis", "Chloroceryle aenea", "Chloroceryle amazona", "Myiozetetes similis", "Toxostoma curvirostre", "Megaceryle alcyon", "Eremophila alpestris", "Alisterus scapularis", "Cinnyris jugularis", "Xiphorhynchus flavigaster", "Jacana jacana", "Phalaropus fulicarius", "Streptopelia turtur", "Pica hudsonia", "Corvus cornix", "Myiopsitta monachus", "Streptopelia decaocto", "Piranga ludoviciana", "Ixobrychus exilis", "Auriparus flaviceps", "Geothlypis tolmiei", "Columba livia", "Setophaga citrina", "Setophaga cerulea", "Columba palumbus", "Piranga bidentata", "Patagioenas leucocephala", "Patagioenas fasciata", "Patagioenas flavirostris", "Setophaga discolor", "Chordeiles minor", "Chordeiles acutipennis", "Platalea leucorodia", "Phalaenoptilus nuttallii", "Haemorhous mexicanus", "Haemorhous purpureus", "Nyctidromus albicollis", "Campephilus guatemalensis", "Toxostoma rufum", "Leptotila verreauxi", "Nyctibius jamaicensis", "Anas penelope", "Buteo plagiatus", "Selasphorus calliope", "Megascops kennicottii", "Porphyrio poliocephalus", "Megascops asio", "Zenaida auriculata", "Hymenolaimus malacorhynchos", "Setophaga caerulescens", "Camptostoma imberbe", "Caracara plancus", "Columbina passerina", "Eupsittula nana", "Nestor notabilis", "Columbina talpacoti", "Geopelia striata", "Athene noctua", "Crotophaga ani", "Sialia sialis", "Platalea alba", "Bubo virginianus", "Petrochelidon fulva", "Progne subis", "Setophaga coronata auduboni", "Hirundo neoxena", "Hirundo rustica", "Calidris virgata", "Calidris pugnax", "Platalea regia", "Tachycineta thalassina", "Bostrychia hagedash", "Riparia riparia", "Eudocimus albus", "Plegadis falcinellus", "Plegadis chihi", "Corvus corax", "Stelgidopteryx serripennis", "Sula dactylatra", "Sula sula", "Sula leucogaster", "Morus serrator", "Eudyptula minor", "Megadyptes antipodes", "Scopus umbretta", "Troglodytes troglodytes", "Calidris canutus", "Lanius collurio", "Calidris bairdii", "Meleagris gallopavo intermedia", "Calidris mauri", "Calidris maritima", "Calidris alpina", "Calidris ferruginea", "Calidris melanotos", "Limnodromus griseus", "Malurus cyaneus", "Tringa nebularia", "Todiramphus sanctus vagans", "Tringa ochropus", "Tringa glareola", "Tringa melanoleuca", "Tringa flavipes", "Numenius arquata", "Numenius phaeopus", "Phalacrocorax carbo novaehollandiae", "Petroica macrocephala macrocephala", "Petroica australis longipes", "Prosthemadera novaeseelandiae novaeseelandiae", "Asio flammeus", "Rhipidura fuliginosa fuliginosa", "Scolopax minor", "Arenaria interpres", "Arenaria melanocephala", "Rhipidura fuliginosa placabilis", "Limosa limosa", "Limosa haemastica", "Limosa fedoa", "Phalaropus lobatus", "Bartramia longicauda", "Limosa lapponica", "Aegolius acadicus", "Actitis hypoleucos", "Trichoglossus haematodus", "Surnia ulula", "Polioptila caerulea", "Spizella passerina", "Pycnonotus jocosus", "Passerina cyanea", "Passerina versicolor", "Manorina melanocephala", "Ocyphaps lophotes", "Jabiru mycteria", "Pycnonotus cafer", "Anser cygnoides domesticus", "Picus viridis", "Phoebastria nigripes", "Struthio camelus", "Phoebastria immutabilis", "Fulmarus glacialis", "Francolinus pondicerianus", "Cynanthus latirostris", "Podiceps nigricollis", "Podiceps cristatus", "Podiceps auritus", "Dendrocopos major", "Podiceps grisegena", "Tachybaptus ruficollis", "Charadrius montanus", "Phalacrocorax auritus", "Phalacrocorax carbo", "Phalacrocorax penicillatus", "Aechmophorus clarkii", "Pelecanus onocrotalus", "Pelecanus erythrorhynchos", "Zenaida macroura", "Vanellus miles", "Larus occidentalis", "Trogon massena", "Larus thayeri", "Larus heermanni", "Larus livens", "Larus canus", "Larus glaucoides", "Larus delawarensis", "Trogon collaris", "Zenaida asiatica", "Larus fuscus", "Larus californicus", "Prosthemadera novaeseelandiae", "Trogon elegans", "Larus glaucescens", "Trogon citreolus", "Cepphus columba", "Himantopus leucocephalus", "Cepphus grylle", "Anthornis melanura", "Leptoptilos crumenifer", "Threskiornis moluccus", "Thraupis episcopus", "Geranoaetus albicaudatus", "Sterna paradisaea", "Sterna hirundo", "Sterna forsteri", "Lanius excubitor", "Pharomachrus mocinno", "Sterna striata", "Stercorarius parasiticus", "Stercorarius pomarinus", "Anas gracilis", "Rissa tridactyla", "Rynchops niger", "Alca torda", "Fratercula arctica", "Fratercula cirrhata", "Turdus merula", "Turdus plumbeus", "Turdus grayi", "Turdus migratorius", "Turdus viscivorus", "Cerorhinca monocerata", "Turdus philomelos", "Galbula ruficauda", "Jacana spinosa", "Saxicola rubicola", "Upupa epops", "Euphagus carolinus", "Gavia pacifica", "Mimus gilvus", "Passer italiae", "Gavia immer", "Gavia stellata", "Oenanthe oenanthe", "Fregata magnificens", "Fregata minor", "Falco subbuteo", "Falco mexicanus", "Falco femoralis", "Falco peregrinus", "Falco rufigularis", "Falco sparverius", "Strix varia", "Falco columbarius", "Gallinula chloropus", "Cardellina pusilla", "Catharus ustulatus", "Falco novaeseelandiae", "Catharus guttatus", "Catharus fuscescens", "Caracara cheriway", "Herpetotheres cachinnans", "Calcarius lapponicus", "Milvago chimachima", "Ciconia ciconia", "Mycteria americana", "Mycteria ibis", "Sialia mexicana", "Ephippiorhynchus senegalensis", "Cathartes aura", "Myadestes townsendi", "Cathartes burrovianus", "Sarcoramphus papa", "Coragyps atratus", "Strix nebulosa", "Anastomus lamelligerus", "Pluvialis squatarola", "Gymnogyps californianus", "Muscicapa striata", "Charadrius vociferus", "Charadrius wilsonia", "Charadrius melodus", "Phoenicurus phoenicurus", "Phoenicurus ochruros", "Charadrius dubius", "Eumomota superciliosa", "Anas querquedula", "Himantopus mexicanus", "Haematopus bachmani", "Pica pica", "Haematopus ostralegus", "Haematopus unicolor", "Vanellus vanellus", "Vanellus spinosus", "Vanellus armatus", "Luscinia svecica", "Columbina inca", "Recurvirostra avosetta", "Phasianus colchicus", "Pluvialis dominica", "Cinclus mexicanus", "Erithacus rubecula", "Melanerpes aurifrons", "Charadrius hiaticula", "Egretta gularis", "Egretta caerulea", "Egretta tricolor", "Egretta thula", "Egretta garzetta", "Egretta sacra", "Monticola solitarius", "Ardea cocoi", "Ardea cinerea", "Ardea herodias", "Nycticorax nycticorax", "Turdus rufopalliatus", "Ardeola ralloides", "Nyctanassa violacea", "Aramides cajaneus", "Bubulcus ibis", "Butorides virescens", "Porphyrio martinicus", "Botaurus lentiginosus", "Cochlearius cochlearius", "Anhinga novaehollandiae", "Anhinga rufa", "Melozone fusca", "Aquila chrysaetos", "Hylocichla mustelina", "Aphelocoma wollweberi", "Accipiter striatus", "Accipiter nisus", "Accipiter gentilis", "Egretta rufescens", "Bucephala clangula", "Anous stolidus", "Actitis macularius", "Ardea purpurea", "Todiramphus chloris", "Grus americana", "Himantopus himantopus", "Glaucidium gnoma", "Circus approximans", "Circus aeruginosus", "Phalacrocorax sulcirostris", "Buteo albonotatus", "Butorides striata", "Platalea ajaja", "Buteo brachyurus", "Ardea alba", "Buteo swainsoni", "Alectoris rufa", "Buteo lineatus", "Buteo jamaicensis", "Charadrius nivosus", "Tringa incana", "Tringa semipalmata", "Terathopius ecaudatus", "Gallinago delicata", "Buteogallus anthracinus", "Chroicocephalus philadelphia", "Circaetus gallicus", "Chroicocephalus novaehollandiae", "Chroicocephalus ridibundus", "Leucophaeus atricilla", "Leucophaeus pipixcan", "Onychoprion fuscatus", "Sternula antillarum", "Hydroprogne caspia", "Thalasseus maximus", "Thalasseus bergii", "Elanus leucurus", "Alopochen aegyptiaca", "Streptopelia senegalensis", "Gerygone igata", "Haliaeetus leucocephalus", "Cistothorus platensis", "Haliaeetus vocifer", "Bubo scandiacus", "Ciccaba virgata", "Bonasa umbellus", "Busarellus nigricollis", "Rostrhamus sociabilis", "Milvus milvus", "Gyps fulvus", "Ficedula hypoleuca", "Momotus coeruliceps", "Charadrius semipalmatus", "Haliastur indus", "Gyps africanus", "Ictinia mississippiensis", "Dives dives", "Baeolophus atricristatus", "Baeolophus bicolor", "Baeolophus inornatus", "Melospiza melodia", "Spinus spinus", "Motacilla alba", "Certhia americana", "Bucorvus leadbeateri", "Anthus pratensis", "Anthus rubescens", "Tachycineta albilinea", "Poecile carolinensis", "Poecile atricapillus", "Poecile gambeli", "Poecile rufescens", "Periparus ater", "Chlorophanes spiza", "Estrilda astrild", "Cyanistes caeruleus", "Sphyrapicus varius", "Petrochelidon pyrrhonota", "Troglodytes pacificus", "Troglodytes hiemalis", "Passer montanus", "Passer domesticus", "Aphelocoma californica", "Amazilia yucatanensis", "Amazilia tzacatl", "Dicrurus adsimilis", "Amazilia violiceps", "Lonchura punctulata", "Tigrisoma mexicanum", "Porphyrio melanotus melanotus", "Melanitta americana", "Spizella pallida", "Amazilia rutila", "Zenaida aurita", "Prunella modularis", "Falco tinnunculus", "Ixoreus naevius", "Archilochus colubris", "Lampornis clemenciae", "Myiarchus tuberculifer", "Setophaga ruticilla", "Myiarchus tyrannulus", "Zosterops lateralis", "Zonotrichia leucophrys", "Agelaius phoeniceus", "Parus major", "Zonotrichia capensis", "Oreothlypis peregrina", "Oreothlypis celata", "Oreothlypis ruficapilla", "Geothlypis philadelphia", "Geothlypis formosa", "Setophaga tigrina", "Setophaga americana", "Setophaga magnolia", "Setophaga castanea", "Setophaga fusca", "Setophaga petechia", "Setophaga striata", "Setophaga palmarum", "Setophaga pinus", "Setophaga coronata", "Setophaga dominica", "Campylopterus hemileucurus", "Vireo cassinii", "Setophaga nigrescens", "Setophaga townsendi", "Setophaga occidentalis", "Setophaga chrysoparia", "Setophaga virens", "Cardellina canadensis", "Cardellina rubra", "Aphelocoma coerulescens", "Melanerpes erythrocephalus", "Emberiza calandra", "Acanthis flammea", "Spinus pinus", "Thraupis abbas", "Spinus psaltria", "Spinus lawrencei", "Spinus tristis", "Threskiornis aethiopicus", "Gygis alba", "Tachycineta bicolor", "Larus dominicanus dominicanus", "Lonchura oryzivora", "Haematopus palliatus", "Chloris chloris", "Accipiter cooperii", "Vireo solitarius", "Selasphorus platycercus", "Chroicocephalus scopulinus", "Aechmophorus occidentalis", "Hemiphaga novaeseelandiae", "Xema sabini", "Ramphastos sulfuratus", "Dacelo novaeguineae", "Fulica atra", "Hirundo rustica erythrogaster", "Calypte costae", "Calypte anna", "Colaptes auratus cafer", "Sula nebouxii", "Copsychus saularis", "Selasphorus sasin", "Selasphorus rufus", "Calothorax lucifer", "Pycnonotus barbatus", "Larus glaucescens × occidentalis", "Aphelocoma woodhouseii", "Florisuga mellivora", "Haematopus finschi", "Falcipennis canadensis", "Archilochus alexandri", "Circus cyaneus", "Ardea alba modesta", "Myiarchus crinitus", "Buteo regalis", "Aeronautes saxatalis", "Chaetura vauxi", "Chaetura pelagica", "Setophaga pensylvanica", "Lanius ludovicianus", "Anhinga anhinga", "Acridotheres tristis", "Sitta pygmaea", "Actophilornis africanus", "Sitta canadensis", "Sitta europaea", "Sitta pusilla", "Apus apus", "Sturnus vulgaris", "Seiurus aurocapilla", "Motacilla flava", "Vireo griseus", "Toxostoma longirostre", "Calidris alba", "Toxostoma redivivum", "Gallinula galeata", "Calidris himantopus", "Eugenes fulgens", "Icterus wagleri", "Icterus parisorum", "Buteo platypterus", "Gallinago gallinago", "Icterus spurius", "Calidris minutilla", "Dumetella carolinensis", "Porphyrio hochstetteri", "Calidris fuscicollis", "Phalaropus tricolor", "Colaptes auratus auratus", "Melanotis caerulescens", "Tiaris olivaceus", "Oreoscoptes montanus", "Limnodromus scolopaceus", "Tringa solitaria", "Sitta carolinensis", "Tringa totanus", "Dendrocygna autumnalis", "Dendrocygna viduata", "Dendrocygna bicolor", "Icterus abeillei", "Vanellus chilensis", "Cygnus buccinator", "Cygnus cygnus", "Cygnus columbianus", "Cygnus olor", "Icterus galbula", "Anas platyrhynchos", "Anas acuta", "Anas crecca", "Anas cyanoptera", "Anas fulvigula", "Anas discors", "Anas strepera", "Anas clypeata", "Anas chlorotis", "Rhipidura leucophrys", "Anas americana", "Bucephala albeola", "Mergus serrator", "Mergus merganser", "Anser anser", "Anser albifrons", "Somateria mollissima", "Rallus obsoletus", "Platycercus elegans", "Melanitta fusca", "Milvus migrans", "Gelochelidon nilotica", "Ninox novaeseelandiae novaeseelandiae", "Melanitta perspicillata", "Aythya collaris", "Aythya ferina", "Aythya fuligula", "Numenius americanus", "Chen caerulescens", "Chamaea fasciata", "Branta canadensis", "Thalasseus elegans", "Branta sandvicensis", "Aythya americana", "Aix sponsa", "Lophodytes cucullatus", "Histrionicus histrionicus", "Aratinga nenday", "Psittacara holochlorus", "Cairina moschata", "Netta rufina", "Psittacara erythrogenys", "Sayornis saya", "Aythya valisineria", "Picoides albolarvatus", "Vermivora cyanoptera", "Corvus caurinus", "Asio otus", "Corvus brachyrhynchos", "Clangula hyemalis", "Chenonetta jubata", "Thalasseus sandvicensis", "Eolophus roseicapilla", "Colaptes rubiginosus", "Meleagris gallopavo silvestris", "Ptiliogonys cinereus", "Elanoides forficatus", "Carduelis carduelis", "Cassiculus melanicterus", "Dendragapus fuliginosus", "Psaltriparus minimus", "Aegithalos caudatus", "Zosterops lateralis lateralis", "Haliaeetus leucogaster", "Uria aalge", "Tringa semipalmata inornatus", "Alauda arvensis", "Galerida cristata", "Delichon urbicum", "Helmitheros vermivorum", "Hylocharis leucotis", "Mimus polyglottos", "Gymnorhina tibicen", "Alcedo atthis", "Streptopelia chinensis", "Bombycilla cedrorum", "Bombycilla garrulus", "Tadorna tadorna", "Petroica australis australis", "Amazona autumnalis", "Dendragapus obscurus", "Catherpes mexicanus", "Tadorna ferruginea", "Campylorhynchus brunneicapillus", "Campylorhynchus rufinucha", "Rhipidura fuliginosa", "Polioptila melanura", "Pelecanus occidentalis carolinensis", "Melozone aberti", "Melozone crissalis", "Thryothorus ludovicianus", "Parabuteo unicinctus", "Anas platyrhynchos domesticus", "Troglodytes aedon", "Buteo lineatus elegans", "Thamnophilus doliatus", "Thryomanes bewickii", "Tadorna variegata", "Myioborus miniatus", "Phalacrocorax varius varius", "Myioborus pictus", "Larus argentatus smithsonianus", "Parkesia motacilla", "Sula granti", "Ara macao", "Peucaea ruficauda", "Calidris subruficollis", "Phoenicopterus roseus", "Morus bassanus", "Pelecanus occidentalis californicus", "Glaucidium brasilianum", "Oxyura jamaicensis", "Myiarchus cinerascens", "Chloroceryle americana", "Sturnella neglecta", "Ramphastos ambiguus", "Paroaria coronata", "Buteo buteo", "Myiodynastes luteiventris", "Contopus sordidulus", "Corvus corone", "Cyanoramphus novaezelandiae", "Contopus virens", "Contopus pertinax", "Contopus cooperi", "Larus argentatus", "Halcyon smyrnensis", "Onychognathus morio", "Cyanocompsa parellina", "Cistothorus palustris", "Tyto alba", "Trogon caligatus", "Corvus monedula", "Corvus ossifragus", "Spheniscus demersus", "Corvus frugilegus", "Corvus splendens", "Branta bernicla", "Larus hyperboreus", "Columba livia domestica", "Garrulus glandarius", "Anser anser domesticus", "Mitrephanes phaeocercus", "Ardenna creatopus", "Ardenna gravis", "background", ]; ================================================ FILE: tflite-birds_v1-image/rust/src/main.rs ================================================ use image::io::Reader; use image::DynamicImage; use std::env; use std::error::Error; use std::fs; use wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, TensorType}; mod imagenet_classes; pub fn main() -> Result<(), Box> { let args: Vec = env::args().collect(); let model_bin_name: &str = &args[1]; let image_name: &str = &args[2]; let weights = fs::read(model_bin_name)?; println!("Read graph weights, size in bytes: {}", weights.len()); let graph = GraphBuilder::new(GraphEncoding::TensorflowLite, ExecutionTarget::CPU) .build_from_bytes(&[&weights])?; let mut ctx = graph.init_execution_context()?; println!("Loaded graph into wasi-nn with ID: {}", graph); // Load a tensor that precisely matches the graph input tensor (see let tensor_data = image_to_tensor(image_name.to_string(), 224, 224); println!("Read input tensor, size in bytes: {}", tensor_data.len()); // Pass tensor data into the TFLite runtime ctx.set_input(0, TensorType::U8, &[1, 224, 224, 3], &tensor_data)?; // Execute the inference. ctx.compute()?; // Retrieve the output. let mut output_buffer = vec![0u8; imagenet_classes::AIY_BIRDS_V1.len()]; _ = ctx.get_output(0, &mut output_buffer)?; // Sort the result with the highest probability result first let results = sort_results(&output_buffer); for i in 0..5 { println!( " {}.) [{}]({:.4}){}", i + 1, results[i].0, results[i].1, imagenet_classes::AIY_BIRDS_V1[results[i].0] ); } Ok(()) } // Sort the buffer of probabilities. The graph places the match probability for each class at the // index for that class (e.g. the probability of class 42 is placed at buffer[42]). Here we convert // to a wrapping InferenceResult and sort the results. fn sort_results(buffer: &[u8]) -> Vec { let mut results: Vec = buffer .iter() .enumerate() .map(|(c, p)| InferenceResult(c, *p)) .collect(); results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); results } // Take the image located at 'path', open it, resize it to height x width. The resulting RGB // pixel vector is then returned. fn image_to_tensor(path: String, height: u32, width: u32) -> Vec { let pixels = Reader::open(path).unwrap().decode().unwrap(); let dyn_img: DynamicImage = pixels.resize_exact(width, height, image::imageops::Triangle); let bgr_img = dyn_img.to_rgb8(); // Get an array of the pixel values let raw_u8_arr: &[u8] = &bgr_img.as_raw()[..]; return raw_u8_arr.to_vec(); } // A wrapper for class ID and match probabilities. #[derive(Debug, PartialEq)] struct InferenceResult(usize, u8); ================================================ FILE: wasmedge-chatTTS/.gitignore ================================================ asset config ================================================ FILE: wasmedge-chatTTS/Cargo.toml ================================================ [package] name = "wasmedge-chattts" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = {git = "https://github.com/second-state/wasmedge-wasi-nn.git", branch = "ggml"} hound = "3.4" ================================================ FILE: wasmedge-chatTTS/README.md ================================================ # ChatTTS example with WasmEdge WASI-NN ChatTTS plugin This example demonstrates how to use the WasmEdge WASI-NN ChatTTS plugin to generate speech from text. ChatTTS is a text-to-speech model designed specifically for dialogue scenarios such as LLM assistant. This example will use the WasmEdge WASI-NN ChatTTS plugin to run the ChatTTS to generate speech. ## Install WasmEdge with WASI-NN ChatTTS plugin The ChatTTS backend relies on ChatTTS and Python library, we recommend the following commands to install the dependencies. ``` bash sudo apt update sudo apt upgrade sudo apt install python3-dev pip install chattts==0.1.1 ``` Then build and install WasmEdge from source. ``` bash cd cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_PLUGIN_WASI_NN_BACKEND="chatTTS" cmake --build build # For the WASI-NN plugin, you should install this project. cmake --install build ``` Then you will have an executable `wasmedge` runtime under `/usr/local/bin` and the WASI-NN with ChatTTS backend plug-in under `/usr/local/lib/wasmedge/libwasmedgePluginWasiNN.so` after installation. ## Build wasm Run the following command to build wasm, the output WASM file will be at `target/wasm32-wasip1/release/` ```bash cargo build --target wasm32-wasip1 --release ``` ## Execute Execute the WASM with the `wasmedge`. ``` bash wasmedge --dir .:. ./target/wasm32-wasip1/release/wasmedge-chattts.wasm ``` Then you will generate the `output1.wav` file. It is the wav file of the input text. ## Advanced Options The `config_data` is used to adjust the configuration of the ChatTTS. Supports the following options: - `prompt`: Generate the special token in the text to synthesize. - `spk_emb`: Sampled speaker (Using `random` for random speaker). - `temperature`: Custom temperature. - `top_k`: Top P decode. - `top_p`: Top K decode. ``` rust let config_data = serde_json::to_string(&json!({"prompt": "[oral_2][laugh_0][break_6]", "spk_emb": "random", "temperature": 0.5, "top_k": 0, "top_p": 0.9})) .unwrap() .as_bytes() .to_vec(); ```
[demo.webm](https://github.com/user-attachments/assets/377e0487-9107-41db-9c22-31962ce53f88)
================================================ FILE: wasmedge-chatTTS/src/main.rs ================================================ use hound; use serde_json::json; use wasmedge_wasi_nn::{ self, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> Vec { const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 4096; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let bytes_written = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); return output_buffer[..bytes_written].to_vec(); } fn main() { let prompt = "It is test sentence [uv_break] for chat T T S."; let tensor_data = prompt.as_bytes().to_vec(); let config_data = serde_json::to_string(&json!({"prompt": "[oral_2][laugh_0][break_6]", "spk_emb": "random", "temperature": 0.5, "top_k": 0, "top_p": 0.9})) .unwrap() .as_bytes() .to_vec(); let empty_vec: Vec> = Vec::new(); let graph = GraphBuilder::new(GraphEncoding::ChatTTS, ExecutionTarget::CPU) .build_from_bytes(empty_vec) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); context .set_input(1, TensorType::U8, &[config_data.len()], &config_data) .expect("Failed to set input"); context.compute().expect("Failed to compute"); let output_bytes = get_data_from_context(&context, 0); let spec = hound::WavSpec { channels: 1, sample_rate: 24000, bits_per_sample: 32, sample_format: hound::SampleFormat::Float, }; let mut writer = hound::WavWriter::create("output1.wav", spec).unwrap(); let samples: Vec = output_bytes .chunks_exact(4) .map(|b| f32::from_le_bytes([b[0], b[1], b[2], b[3]])) .collect(); for sample in samples { writer.write_sample(sample).unwrap(); } writer.finalize().unwrap(); graph.unload().expect("Failed to free resource"); } ================================================ FILE: wasmedge-ggml/README.md ================================================ # Llama Example For WASI-NN with GGML Backend [See it in action!](https://x.com/juntao/status/1705588244602114303) ## Requirement ### Install WasmEdge + WASI-NN ggml plugin WASI-NN ggml plugin only supports Linux and macOS. ```bash curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- --plugin wasi_nn-ggml # After installing the wasmedge, you have to activate the environment. # Assuming you use zsh (the default shell on macOS), you will need to run the following command source $HOME/.zshenv # Assuming you use bash (the default shell on Ubuntu), you will need to run the following command source $HOME/.bashrc ``` The supported matrix: | OS | Arch | GPU | Framework | Comments | | - | - | - | - | - | | Linux | amd64 | NVIDIA GPU | cuda 12.x | When CUDA is detected, we will install the cuda enabled plugin by default | | Linux | amd64 | - | - | - | | Linux | arm64 | NVIDIA GPU | cuda 11.x | This is built on Jetson AGX Orin | | Linux | arm64 | - | - | - | | macOS | Intel | - | - | Metal is not working very well on AMD GPU | | macOS | Apple Silicon | Mx | Metal | - | We are planing to support in the near future: | OS | Arch | GPU | Framework | Comments | | - | - | - | - | - | | Linux | amd64 | AMD GPU | OpenCL | Work in progress | This version is verified on the following platforms: 1. Nvidia Jetson AGX Orin 64GB developer kit 2. Intel i7-10700 + Nvidia GTX 1080 8G GPU 2. AWS EC2 `g5.xlarge` + Nvidia A10G 24G GPU + Amazon deep learning base Ubuntu 20.04 ## Prepare WASM application ### (Recommend) Use the pre-built one bundled in this repo We built a wasm of this example under the folder, check these files: - `chatml/wasmedge-ggml-chatml.wasm` - `embedding/wasmedge-ggml-llama-embedding.wasm` - `llama/wasmedge-ggml-llama.wasm` - `llama-stream/wasmedge-ggml-llama-stream.wasm` ### (Optional) Build from source If you want to do some modifications, you can build from source. Compile the application to WebAssembly: ```bash cd llama cargo build --target wasm32-wasip1 --release ``` The output WASM file will be at `target/wasm32-wasip1/release/`. ```bash cp target/wasm32-wasip1/release/wasmedge-ggml-llama.wasm ./wasmedge-ggml-llama.wasm ``` ## Get Model In this example, we are going to use llama2 7b chat model in GGUF format. Download llama model: ```bash curl -LO https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf ``` ## Execute Execute the WASM with the `wasmedge` using the named model feature to preload a large model: ```bash wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \ wasmedge-ggml-llama.wasm default ``` ### GPU acceleration #### macOS macOS will use the Metal framework by default. llama.cpp supports `n-gpu-layers` now, please make sure you set the `n-gpu-layers` to offload the tensor layers into GPU. Modify the following code to ensure the tensor layers of the model is offloaded into GPU: ```rust // llama2-7b-chat provides 35 GPU layers. So, we have to set a value that is large or equal to 35. options.insert("n-gpu-layers", Value::from(35)); ``` #### Linux + CUDA Due to the various GPU hardware, it's hard to set a default value of `n-gpu-layers`. Modify the following code to ensure the tensor layers of the model is offloaded into GPU: ```rust // llama2-7b-chat provides 35 GPU layers. So, we have to set a value that is large or equal to 35. options.insert("n-gpu-layers", Value::from(35)); ``` Remember to re-generate the wasm file after changing the code. After executing, you may need to wait a moment for the input prompt to appear. You can enter your question once you see the `Question:` prompt: ```console Question: What's the capital of the United States? Answer: The capital of the United States is Washington, D.C. (District of Columbia). Question: What about France? Answer: The capital of France is Paris. Question: I have two apples, each costing 5 dollars. What is the total cost of these apples? Answer: The total cost of the two apples is $10. Question: What if I have 3 apples? Answer: The total cost of 3 apples would be 15 dollars. Each apple costs 5 dollars, so 3 apples would cost 3 x 5 = 15 dollars. ``` ## Parameters Supported parameters include: - `enable-log`: Set it to true to enable logging. - `enable-debug-log`: Set it to true to enable debug log. - `stream-stdout`: Set it to true to print the inferred tokens to standard output. - `ctx-size`: Set the context size, the same as the `--ctx-size` parameter in llama.cpp. - `n-predict`: Set the number of tokens to predict, the same as the `--n-predict` parameter in llama.cpp. - `n-gpu-layers`: Set the number of layers to store in VRAM, the same as the `--n-gpu-layers` parameter in llama.cpp. - `reverse-prompt`: Set it to the token at which you want to halt the generation. Similar to the `--reverse-prompt` parameter in llama.cpp. - `batch-size`: Set the batch size number for prompt processing, the same as the `--batch-size` parameter in llama.cpp. - `temp`: Set the temperature for the generation, the same as the `--temp` parameter in llama.cpp. - `repeat-penalty`: Set the repeat penalty for the generation, the same as the `--repeat-penalty` parameter in llama.cpp. - `threads`: Set the number of threads for the inference, the same as the `--threads` parameter in llama.cpp. - `mmproj`: Set the path to the multimodal projector file for llava, the same as the `--mmproj` parameter in llama.cpp. - `image`: Set the path to the image file for llava, the same as the `--image` parameter in llama.cpp. - `grammar`: Specify a grammar to constrain model output to a specific format, the same as the `--grammar` parameter in llama.cpp. - `json-schema`: Specify a JSON schema to constrain model output, the same as the `--json-schema` parameter in llama.cpp. (For more detailed instructions on usage or default values for the parameters, please refer to [WasmEdge](https://github.com/WasmEdge/WasmEdge/blob/master/plugins/wasi_nn/ggml.cpp).) ## Performance Welcome to submit PR to upload the TPS <3 | WasmEdge | Hardware | OS | Model | TPS | | - | - | - | - | - | | 0.13.5 | i7-10700 + NVIDIA GTX 1080 8G | Ubuntu 22.04 | llama-2-7b-chat.Q5\_K\_M | 31.73 | | 0.13.5 | M2 Max 64GB | macOS 13.6 | llama-2-7b-chat.Q5\_K\_M | 42.08 | | 0.13.5 | M3 Max 64GB | macOS 14.1.1 | llama-2-7b-chat.Q5\_K\_M | 49.39 | | 0.13.5 | AWS g5.xlarge A10G 24G | Amazon Deep Learning base Ubuntu 20.04 | llama-2-7b-chat.Q5\_K\_M | 71.48 | | 0.13.5 | i7-13700K + NVIDIA RTX 4090 24G | Windows 11 WSL2 Ubuntu 22.04 | llama-2-7b-chat.Q5\_K\_M | 111.90 | ## Errors - After running `apt update && apt install -y libopenblas-dev`, you may encounter the following error: ```bash ... E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied) E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root? ``` This indicates that you are not logged in as `root`. Please try installing again using the `sudo` command: ```bash sudo apt update && sudo apt install -y libopenblas-dev ``` - After running the `wasmedge` command, you may receive the following error: ```bash [2023-10-02 14:30:31.227] [error] loading failed: invalid path, Code: 0x20 [2023-10-02 14:30:31.227] [error] load library failed:libblas.so.3: cannot open shared object file: No such file or directory [2023-10-02 14:30:31.227] [error] loading failed: invalid path, Code: 0x20 [2023-10-02 14:30:31.227] [error] load library failed:libblas.so.3: cannot open shared object file: No such file or directory unknown option: nn-preload ``` This suggests that your plugin installation was not successful. To resolve this issue, please attempt to install your desired plugin again. ## Details ### Metadata Currently, the WASI-NN ggml plugin supports several ways to set the metadata for the inference. 1. From the graph builder When constructing the graph, you can set the metadata by using the `config` method. ```rust wasmedge_wasi_nn::GraphBuilder::new(...) .config(serde_json::to_string(&options).unwrap()) .build_from_cache(...) .unwrap(); ``` > [!NOTE] > The config will only be set when constructing the graph using `build_from_cache`. > Due to the file size limitation, you **SHOULD** use `build_from_cache` with `--nn-preload` to load the large model file. 2. From the input tensor When setting input to the context, specify the index with 1 for the metadata. This setting will overwrite the metadata set in the graph builder. If you modify the `n-gpu-layers` parameter, the model will be reloaded. ```rust context .set_input( 1, wasmedge_wasi_nn::TensorType::U8, &[1], serde_json::to_string(&options).expect("Failed to serialize options").as_bytes().to_vec(), ) .unwrap(); ``` (For more detailed instructions on usage or default values for the parameters, please refer to [WasmEdge](https://github.com/WasmEdge/WasmEdge/blob/master/plugins/wasi_nn/ggml.cpp).) ### Embedding Usage Once the `embedding` mode is on, the return output will be a JSON object in the following format: ```json { "n_embedding": 4096, "embedding": [...] } ``` ### Token Usage You can use `get_output()` with index 1 to get the token usage of input and output text. The token usage is a JSON string with the following format: ```json { "input_tokens": 78, "output_tokens": 31 } ``` Users should be aware of the context size as well as the number of tokens used to avoid exceeding the limit. If the number of tokens exceeds the context size, the WASI-NN ggml plugin will return a RuntimeError. ## Credit The WASI-NN ggml plugin embedded [`llama.cpp`](git://github.com/ggerganov/llama.cpp.git) as its backend. For details about the version used, please refer to [WasmEdge/LICENSE.spdx](https://github.com/WasmEdge/WasmEdge/blob/master/LICENSE.spdx). ================================================ FILE: wasmedge-ggml/basic/Cargo.toml ================================================ [package] name = "wasmedge-ggml-basic" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/basic/README.md ================================================ # Basic Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example for the models without prompt templates. ## StarCoder2 ### Get the Model This example is for the models without prompt template. For example, the `StarCoder2` model. Download the model: ```bash curl -LO https://huggingface.co/second-state/StarCoder2-7B-GGUF/resolve/main/starcoder2-7b-Q5_K_M.gguf ``` ### Parameters > [!NOTE] > Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. - For GPU offloading, please adjust the `n-gpu-layers` options to the number of layers that you want to offload to the GPU. - When using the `StarCoder2` model, the `n-predict` option can be used to adjust the number of predictions. Since the inference may not stop as expected, it is recommended to set a limit for the number of predictions. ### Execute Execute the WASM with the `wasmedge` using the named model feature to preload a large model: ```console $ wasmedge --dir .:. \ --env n_predict=100 \ --nn-preload default:GGML:AUTO:starcoder2-7b-Q5_K_M.gguf \ wasmedge-ggml-basic.wasm default USER: def print_hello_world(): ASSISTANT: print("Hello World!") def main(): print_hello_world() if __name__ == "__main__": main()/README.md # python-learning This repository is for learning Python. ## References * [Python 3.8.0 Documentation](https://docs.python.org/3/) ``` ## Grok-1 ### Get the Model Download the split grok-1 model: ```bash curl -LO https://huggingface.co/Arki05/Grok-1-GGUF/resolve/main/grok-1-Q2_K-split-00001-of-00009.gguf curl -LO https://huggingface.co/Arki05/Grok-1-GGUF/resolve/main/grok-1-Q2_K-split-00002-of-00009.gguf curl -LO https://huggingface.co/Arki05/Grok-1-GGUF/resolve/main/grok-1-Q2_K-split-00003-of-00009.gguf curl -LO https://huggingface.co/Arki05/Grok-1-GGUF/resolve/main/grok-1-Q2_K-split-00004-of-00009.gguf curl -LO https://huggingface.co/Arki05/Grok-1-GGUF/resolve/main/grok-1-Q2_K-split-00005-of-00009.gguf curl -LO https://huggingface.co/Arki05/Grok-1-GGUF/resolve/main/grok-1-Q2_K-split-00006-of-00009.gguf curl -LO https://huggingface.co/Arki05/Grok-1-GGUF/resolve/main/grok-1-Q2_K-split-00007-of-00009.gguf curl -LO https://huggingface.co/Arki05/Grok-1-GGUF/resolve/main/grok-1-Q2_K-split-00008-of-00009.gguf curl -LO https://huggingface.co/Arki05/Grok-1-GGUF/resolve/main/grok-1-Q2_K-split-00009-of-00009.gguf ``` ### Execute Since the `ggml` plugin supports the split `gguf` model, you can set the `nn-preload` option with the initial split model. The plugin will then automatically load the remaining split models from the same directory. ```console $ wasmedge --dir .:. \ --env n_predict=100 \ --nn-preload default:GGML:AUTO:grok-1-Q2_K-split-00001-of-00009.gguf \ wasmedge-ggml-basic.wasm default 'hello' ``` ## TriLM & BitNet Models After the following pull requests are merged, the `TriLM` and `BitNet` models will be supported by the `ggml` plugin with model type `TQ1_0` and `TQ2_0`: - https://github.com/ggerganov/llama.cpp/pull/7931 - https://github.com/ggerganov/llama.cpp/pull/8151 ### Get the Model Download the `TriLM` model: ```bash curl -LO https://huggingface.co/Green-Sky/TriLM_3.9B-GGUF/resolve/main/TriLM_3.9B_Unpacked-4.0B-TQ2_0.gguf ``` ### Execute ```console $ wasmedge --dir .:. \ --env n_predict=100 \ --nn-preload default:GGML:AUTO:TriLM_3.9B_Unpacked-4.0B-TQ2_0.gguf \ wasmedge-ggml-basic.wasm default ``` ================================================ FILE: wasmedge-ggml/basic/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()).expect("invalid enable-log value (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("ctx_size") { options["ctx-size"] = serde_json::from_str(val.as_str()).expect("invalid ctx-size value (unsigned integer)") } else { options["ctx-size"] = serde_json::from_str("512").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl (unsigned integer)") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } if let Ok(val) = env::var("n_predict") { options["n-predict"] = serde_json::from_str(val.as_str()).expect("invalid n-predict (unsigned integer)") } else { options["n-predict"] = serde_json::from_str("512").unwrap() } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } #[allow(dead_code)] fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let mut options = get_options_from_env(); // Set the stream-stdout option to true to make the response more interactive. options["stream-stdout"] = serde_json::from_str("true").unwrap(); // We set the temperature to 0.2 in this example to make the response more consistent. options["temp"] = Value::from(0.1); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); if let Some(true) = options["stream-stdout"].as_bool() { println!(); } else { println!("{}", output.trim()); } std::process::exit(0); } loop { println!("USER:"); let input = read_input(); // Set prompt to the input tensor. set_data_to_context(&mut context, input.as_bytes().to_vec()).expect("Failed to set input"); // Execute the inference. println!("ASSISTANT:"); match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let output = get_output_from_context(&context); if let Some(true) = options["stream-stdout"].as_bool() { println!(); } else { println!("{}", output.trim()); } } } ================================================ FILE: wasmedge-ggml/chatml/Cargo.toml ================================================ [package] name = "wasmedge-ggml-chatml" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/chatml/README.md ================================================ # `chatml` ## Execute ````console $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf \ wasmedge-ggml-chatml.wasm default USER: List 3 most popular books. The information should include their title, author, published year, and genre. Format the answer into JSON objects. ASSISTANT: [ { "title": "To Kill a Mockingbird", "author": "Harper Lee", "published_year": 1960, "genre": "Fiction" }, { "title": "The Great Gatsby", "author": "F. Scott Fitzgerald", "published_year": 1925, "genre": "Fiction" }, { "title": "Pride and Prejudice", "author": "Jane Austen", "published_year": 1813, "genre": "Romance" } ] USER: Implement a HTTP API service in python to show these results. ASSISTANT: Here is a simple implementation using Flask, a Python web framework: ```python from flask import Flask, jsonify app = Flask(__name__) books = [ { "title": "To Kill a Mockingbird", "author": "Harper Lee", "published_year": 1960, "genre": "Fiction" }, { "title": "The Great Gatsby", "author": "F. Scott Fitzgerald", "published_year": 1925, "genre": "Fiction" }, { "title": "Pride and Prejudice", "author": "Jane Austen", "published_year": 1813, "genre": "Romance" } ] @app.route('/') def home(): return "Welcome to the Book API!" @app.route('/books', methods=['GET']) def get_books(): return jsonify(books) if __name__ == '__main__': app.run(debug=True) ``` This script creates a simple web service with a single endpoint `/books` that returns the list of books in JSON format when accessed with an HTTP GET request. ```` ================================================ FILE: wasmedge-ggml/chatml/src/main.rs ================================================ use serde_json::{json, Value}; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } if let Ok(val) = env::var("ctx_size") { options["ctx-size"] = serde_json::from_str(val.as_str()).expect("invalid ctx-size value (unsigned integer") } if let Ok(val) = env::var("reverse_prompt") { options["reverse-prompt"] = json!(val.as_str()) } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } #[allow(dead_code)] fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // We also support setting the options via input tensor with index 1. // Uncomment the line below to run the example, Check our README for more details. // set_metadata_to_context( // &mut context, // serde_json::to_string(&options) // .expect("Failed to serialize options") // .as_bytes() // .to_vec(), // ) // .expect("Failed to set metadata"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); std::process::exit(0); } let mut saved_prompt = String::new(); let system_prompt = String::from("You are a helpful, respectful and honest assistant. Always answer as short as possible, while being safe." ); loop { println!("USER:"); let input = read_input(); if saved_prompt.is_empty() { saved_prompt = format!("<|im_start|>system\n{}<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n", system_prompt, input); } else { saved_prompt = format!( "{}<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n", saved_prompt, input ); } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let mut output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{}{}<|im_end|>\n", saved_prompt, output); } } } ================================================ FILE: wasmedge-ggml/command-r/Cargo.toml ================================================ [package] name = "wasmedge-ggml-command-r" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/command-r/README.md ================================================ # Command-R Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This example will focus on the Command-R prompt template. ## Parameters > [!NOTE] > Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. ## Get Model Here we use the `c4ai-command-r-plus-GGUF` model as an example. You can download the model from the Hugging Face model hub. ```bash curl -LO https://huggingface.co/pmysl/c4ai-command-r-plus-GGUF/resolve/main/command-r-plus-Q5_K_M-00001-of-00002.gguf curl -LO https://huggingface.co/pmysl/c4ai-command-r-plus-GGUF/resolve/main/command-r-plus-Q5_K_M-00002-of-00002.gguf ``` ## Execute In this example, we use the system prompt with the definition of avaiable tools from [Example Rendered Tool Use Prompt](https://huggingface.co/CohereForAI/c4ai-command-r-plus). ````console $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:command-r-plus-Q5_K_M-00001-of-00002.gguf \ ./wasmedge-ggml-command-r.wasm default USER: Whats the biggest penguin in the world? ASSISTANT: Action: ```json [ { "tool_name": "internet_search", "parameters": { "query": "biggest penguin species" } } ] ``` ```` ================================================ FILE: wasmedge-ggml/command-r/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } options["ctx-size"] = serde_json::from_str("1024").unwrap(); options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // We also support setting the options via input tensor with index 1. // Uncomment the line below to run the example, Check our README for more details. // set_metadata_to_context( // &mut context, // serde_json::to_string(&options) // .expect("Failed to serialize options") // .as_bytes() // .to_vec(), // ) // .expect("Failed to set metadata"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); std::process::exit(0); } let mut saved_prompt = String::new(); let system_tool_prompt = r#" # Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling. ## Available Tools Here is a list of tools that you have available to you: ```python def internet_search(query: str) -> List[Dict]: """Returns a list of relevant document snippets for a textual query retrieved from the internet Args: query (str): Query to search the internet with """ pass ``` ```python def directly_answer() -> List[Dict]: """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history """ pass ``` "#; let system_instruction_prompt = r#" Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example: ```json [ { "tool_name": title of the tool in the specification, "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters } ]``` "#; loop { println!("USER:"); let input = read_input(); // if saved_prompt.is_empty() { saved_prompt = format!( "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{}<|END_OF_TURN_TOKEN|>\ <|USER_TOKEN|>{}<|END_OF_TURN_TOKEN|>\ <|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{}<|END_OF_TURN_TOKEN|>\ <|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", system_tool_prompt, input, system_instruction_prompt ); } else { saved_prompt = format!("{} <|START_OF_TURN_TOKEN|><|USER_TOKEN|>{}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", saved_prompt, input); } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let mut output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{} {} <|END_OF_TURN_TOKEN|>", saved_prompt, output); } } } ================================================ FILE: wasmedge-ggml/embedding/Cargo.toml ================================================ [package] name = "wasmedge-ggml-llama-embedding" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/embedding/README.md ================================================ # Embedding Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example of generating embeddings. ## Get the Model In this example, we are going to use the pre-converted `all-MiniLM-L6-v2` model. Download the model: ```bash curl -LO https://huggingface.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF/resolve/main/all-MiniLM-L6-v2-ggml-model-f16.gguf ``` ## Parameters > [!NOTE] > Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. ## Execute Execute the WASM with the `wasmedge` using the named model feature to preload a large model: ```console $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf \ wasmedge-ggml-llama-embedding.wasm default Prompt: What's the capital of the United States? Raw Embedding Output: {"n_embedding": 384, "embedding": [0.5426152349,-0.03840282559,-0.03644151986,0.3677068651,-0.115977712...(omitted)...,-0.003531290218]} Interact with Embedding: N_Embd: 384 Show the first 5 elements: embd[0] = 0.5426152349 embd[1] = -0.03840282559 embd[2] = -0.03644151986 embd[3] = 0.3677068651 embd[4] = -0.115977712 ``` ================================================ FILE: wasmedge-ggml/embedding/src/main.rs ================================================ use serde_json::{json, Value}; use std::env; use std::io::{self}; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()).unwrap() } if let Ok(val) = env::var("ctx_size") { options["ctx-size"] = serde_json::from_str(val.as_str()).unwrap() } if let Ok(val) = env::var("batch_size") { options["batch-size"] = serde_json::from_str(val.as_str()).unwrap() } if let Ok(val) = env::var("threads") { options["threads"] = serde_json::from_str(val.as_str()).unwrap() } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 embedding size and each embedding number is length 20, // and add 128 bytes for other information such as "n_embedding" of other symbols. const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 20 + 128; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context.get_output(index, &mut output_buffer).unwrap(); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); String::from_utf8_lossy(&output_buffer[..output_size]).to_string() } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).unwrap() } fn get_embd_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 0)).unwrap() } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; let mut options = get_options_from_env(); options["embedding"] = serde_json::Value::Bool(true); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(options.to_string()) .build_from_cache(model_name) .expect("Create GraphBuilder Failed, please check the model name or options"); let mut context = graph .init_execution_context() .expect("Init Context Failed, please check the model"); // We also support setting the options via input tensor with index 1. // Uncomment the line below to run the example, Check our README for more details. // // set_metadata_to_context(&mut context, options.to_string().as_bytes().to_vec()).unwrap(); // If there is a third argument, use it as the prompt and enter non-interactive mode. // Otherwise, enter interactive mode. if args.len() >= 3 { let prompt = &args[2]; println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .unwrap(); println!("Raw Embedding Output:"); context.compute().unwrap(); let output = get_output_from_context(&context); println!("{}", output.trim()); let embd = get_embd_from_context(&context); println!("Interact with Embedding:"); let n_embd = embd["n_embedding"].as_u64().unwrap(); println!("N_Embd: {}", n_embd); println!("Show the first 5 elements:"); for idx in 0..5 { println!("embd[{}] = {}", idx, embd["embedding"][idx as usize]); } std::process::exit(0); } loop { println!("Prompt:"); let input = read_input(); // Set prompt to the input tensor. set_data_to_context(&mut context, input.as_bytes().to_vec()).unwrap(); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); if let Some(true) = options["enable-log"].as_bool() { println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); } match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full"); } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long"); } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let output = get_output_from_context(&context); println!("Raw Embedding Output: {}", output.trim()); let embd = get_embd_from_context(&context); println!("Interact with Embedding:"); let n_embd = embd["n_embedding"].as_u64().unwrap(); println!("N_Embd: {}", n_embd); println!("Show the first 5 elements:"); for idx in 0..5 { println!("embd[{}] = {}", idx, embd["embedding"][idx as usize]); } // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); if let Some(true) = options["enable-log"].as_bool() { println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); } } } ================================================ FILE: wasmedge-ggml/gemma/Cargo.toml ================================================ [package] name = "wasmedge-ggml-gemma" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/gemma/README.md ================================================ # `gemma` ## Get Model ```console wget https://huggingface.co/second-state/Gemma-2b-it-GGUF/resolve/main/gemma-2b-it-Q4_K_M.gguf ``` ## Execute ````console $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:gemma-2b-it-Q4_K_M.gguf \ ./wasmedge-ggml-gemma.wasm default USER: Write a merge sort in Rust for me ASSISTANT: ```rust fn merge_sort(arr: &mut [i32]) { if arr.len() <= 1 { return; } let mid = arr.len() / 2; let (left, right) = arr.split_at_mut(mid); merge_sort(left); merge_sort(right); merge(arr, left, right); } fn merge(arr: &mut [i32], left: &mut [i32], right: &mut [i32]) { let mut merged = vec![]; for i in 0..left.len() { merged.push(*left[i]); } for j in 0..right.len() { merged.push(*right[j]); } arr.clear(); for item in merged { arr.push(item); } } ``` **Usage:** ```rust let mut numbers = vec![5, 2, 8, 3, 1, 9, 4]; merge_sort(&mut numbers); println!("{:?}", numbers); // Output: [1, 2, 3, 4, 5, 8, 9] ``` **Explanation:** The `merge` function takes three slices as input and merges them into a single sorted slice. It works by iterating over the two input slices and adding each element to the merged slice until one of the slices is empty. * `left` contains the first half of the elements. * `right` contains the second half of the elements. * The merged slice is stored in `arr`. **Time complexity:** Merge sort has a time complexity of O(n log n), where n is the length of the input vector. This is because the function divides the problem into smaller subproblems until each subproblem contains only one element. USER: How about in C++? ASSISTANT: ```c++ #include #include using namespace std; void merge(int arr[], int left[], int right[]) { int n = sizeof(arr) / 2; int i = 0, j = 0, k = 0; while (i < n && j < left[n]) { if (left[i] <= right[j]) { arr[k++] = left[i++]; } else { arr[k++] = right[j++]; } } while (i < n) { arr[k++] = left[i++]; } while (j < left[n]) { arr[k++] = right[j++]; } } void merge_sort(vector& arr) { if (arr.size() <= 1) { return; } int mid = arr.size() / 2; merge_sort(arr.data() + mid); merge_sort(arr.data() + mid * 2); merge(arr.data(), arr.data() + mid, arr.data() + 2 * mid); } int main() { vector numbers = {5, 2, 8, 3, 1, 9, 4}; merge_sort(numbers); cout << "{:?}", numbers) << endl; return 0; } ``` **Output:** ``` {:?}", [1, 2, 3, 4, 5, 8, 9] ``` ```` ================================================ FILE: wasmedge-ggml/gemma/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()).expect("invalid enable-log value (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("ctx_size") { options["ctx-size"] = serde_json::from_str(val.as_str()).expect("invalid ctx-size value (unsigned integer)") } else { options["ctx-size"] = serde_json::from_str("4096").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl (unsigned integer)") } else { options["n-gpu-layers"] = serde_json::from_str("100").unwrap() } options["stream-stdout"] = serde_json::from_str("true").unwrap(); options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } #[allow(dead_code)] fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // We also support setting the options via input tensor with index 1. // Uncomment the line below to run the example, Check our README for more details. // set_metadata_to_context( // &mut context, // serde_json::to_string(&options) // .expect("Failed to serialize options") // .as_bytes() // .to_vec(), // ) // .expect("Failed to set metadata"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); std::process::exit(0); } let mut saved_prompt = String::new(); loop { println!("USER:"); let input = read_input(); if saved_prompt.is_empty() { saved_prompt = format!( "user {} model", input ); } else { saved_prompt = format!( "{} user {} model", saved_prompt, input ); } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Get the number of input tokens and llama.cpp versions. // let input_metadata = get_metadata_from_context(&context); // println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); // println!( // "[INFO] llama_build_number: {}", // input_metadata["llama_build_number"] // ); // println!( // "[INFO] Number of input tokens: {}", // input_metadata["input_tokens"] // ); // Execute the inference. let mut reset_prompt = false; println!("ASSISTANT:"); match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let mut output = get_output_from_context(&context); if let Some(true) = options["stream-stdout"].as_bool() { println!(); } else { println!("{}", output.trim()); } // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{} {}", saved_prompt, output); } // Retrieve the output metadata. // let metadata = get_metadata_from_context(&context); // println!( // "[INFO] Number of input tokens: {}", // metadata["input_tokens"] // ); // println!( // "[INFO] Number of output tokens: {}", // metadata["output_tokens"] // ); } } ================================================ FILE: wasmedge-ggml/gemma-3/Cargo.toml ================================================ [package] name = "wasmedge-ggml-gemma-3" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" [[bin]] name = "wasmedge-ggml-gemma-3" path = "src/main.rs" [[bin]] name = "wasmedge-ggml-gemma-3-base64" path = "src/base64.rs" ================================================ FILE: wasmedge-ggml/gemma-3/README.md ================================================ # Gemma-3 Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example of the Gemma-3 model. ## Get Gemma-3 Model ```bash curl -LO https://huggingface.co/second-state/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-Q5_K_M.gguf curl -LO https://huggingface.co/second-state/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-mmproj-f16.gguf ``` ## Prepare the Image ```bash curl -LO https://llava-vl.github.io/static/images/monalisa.jpg ``` ## Execute (with image file) > [!NOTE] > You may see some warnings stating `key clip.vision.* not found in file.`. These are expected and can be ignored. ```console $ wasmedge --dir .:. \ --env mmproj=gemma-3-4b-it-mmproj-f16.gguf \ --env image=monalisa.jpg \ --nn-preload default:GGML:AUTO:gemma-3-4b-it-Q5_K_M.gguf \ wasmedge-ggml-gemme-3.wasm default ``` ## Execute (with base64 encoded image) > [!NOTE] > You may see some warnings stating `key clip.vision.* not found in file.`. These are expected and can be ignored. ```console $ wasmedge --dir .:. \ --env mmproj=gemma-3-4b-it-mmproj-f16.gguf \ --nn-preload default:GGML:AUTO:gemma-3-4b-it-Q5_K_M.gguf \ wasmedge-ggml-gemme-3-base64.wasm default ``` ## Results ``` USER: Describe this image ASSISTANT: Okay, let's describe the image. The image is a portrait of a man, almost certainly **Leonardo da Vinci's *Mona Lisa*.** Here's a breakdown of the key features and overall impression: * **Subject:** A woman, believed to be Lisa del Giocondo, is the central figure. She is seated, turned slightly to the viewer, with her hands folded in her lap. * **Composition:** She's positioned in a pyramidal form, creating a sense of stability and balance. * **Expression:** Her most famous feature is her enigmatic smile. It's subtle, almost ambiguous, and seems to shift depending on the angle of observation. This is a key part of the painting's enduring mystery. * **Technique:** Da Vinci employed his signature *sfumato* technique - a subtle blending of colors and tones that creates a soft, hazy effect, particularly around her eyes and mouth. This contributes to the dreamlike quality of the painting. * **Background:** A landscape is visible in the background, seemingly a hazy, distant vista of mountains and water. The landscape is atmospheric and slightl y blurred, further drawing attention to the figure. * **Color Palette:** The painting employs a muted, earthy color palette – browns, greens, golds, and blues - giving it a timeless and serene quality. * **Overall Impression:** The *Mona Lisa* is a masterpiece of Renaissance art. It's renowned for its realism, psychological depth, and technical brilliance. It exudes an aura of mystery and beauty, which is why it's so widely recognized and studied. **Do you want me to delve deeper into a specific aspect of the image, such as:** * The historical context of the painting? * The techniques Da Vinci used? * The theories surrounding her smile? USER: The techniques Da Vinci used? ASSISTANT: Okay, let’s dive deeper into the techniques Leonardo da Vinci employed to create the *Mona Lisa*. He was a meticulous and innovative artist, and the painting showcases several groundbreaking techniques he developed and perfected. Here’s a breakdown of the key ones: **1. Sfumato:** * **What it is:** This is arguably the *most* famous technique associated with the *Mona Lisa*. “Sfumato” is an Italian word meaning “smoked” or “blurred.” It’s a subtle, almost imperceptible blending of colors and tones that creates a soft, hazy effect, particularly around the edges of forms and features. * **How he used it:** Da Vinci achieved this by applying incredibly thin layers of oil paint – often just a glaze – and meticulously blending them without harsh lines. He’d work with a tiny brush, gradually building up the tones to create a sense of depth and softness. You see it most prominently around her eyes and mouth, contributing to the elusive quality of her smile. **2. Chiaroscuro:** * **What it is:** Chiaroscuro (Italian for "light-dark") is the use of strong contrasts between light and dark to create dramatic effects. * **How he used it:** Da Vinci uses chiaroscuro to model Mona Lisa’s face and body, giving her a three-dimensional appearance. The subtle gradations of light and shadow define her features and create a sense of volume. **3. Layering and Glazing:** * **What it is:** This technique involves applying many thin, translucent layers of paint (glazes) over a dry underpainting. * **How he used it:** Da Vinci built up the colors of the *Mona Lisa* through numerous thin glazes. Each layer subtly alters the color and tone of the layer beneath it. This created a luminous quality and depth of color that was far more vibrant than previous painting techniques. It also helped to create the *sfumato* effect. **4. Aerial Perspective (Atmospheric Perspective):** * **What it is:** This technique uses variations in color and detail to create the illusion of depth in a landscape. Objects further away appear paler, less detailed, and bluer. * **How he used it:** The background landscape in the *Mona Lisa* demonstrates aerial perspective beautifully. The mountains and the distant water are rendered with muted colors and softened details, suggesting their ``` ================================================ FILE: wasmedge-ggml/gemma-3/src/base64.rs ================================================ use serde_json::Value; use std::collections::HashMap; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> HashMap<&'static str, Value> { let mut options = HashMap::new(); // Required parameters for llava if let Ok(val) = env::var("mmproj") { options.insert("mmproj", Value::from(val.as_str())); } else { eprintln!("Failed to get mmproj model."); std::process::exit(1); } // Optional parameters if let Ok(val) = env::var("enable_log") { options.insert("enable-log", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("enable-log", Value::from(false)); } if let Ok(val) = env::var("enable_debug_log") { options.insert( "enable-debug-log", serde_json::from_str(val.as_str()).unwrap(), ); } else { options.insert("enable-debug-log", Value::from(false)); } if let Ok(val) = env::var("ctx_size") { options.insert("ctx-size", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("ctx-size", Value::from(4096)); } if let Ok(val) = env::var("n_gpu_layers") { options.insert("n-gpu-layers", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("n-gpu-layers", Value::from(0)); } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); String::from_utf8_lossy(&output_buffer[..output_size]).to_string() } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // You could also set the options manually like this: // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); return; } let mut saved_prompt = String::new(); // This image is converted from monalisa.jpg let image_base64_bytes = 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"; let image_tag = format!( "", image_base64_bytes ); loop { println!("USER:"); let input = read_input(); // Gemma-3 prompt format: 'user\nDescribe this imagemodel\n' if saved_prompt.is_empty() { saved_prompt = format!( "user\n{}{}\nmodel\n", image_tag, input ); } else { saved_prompt = format!( "{}user\n{}\nmodel\n", saved_prompt, input ); } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); std::process::exit(1); } } // Retrieve the output. let mut output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{}{}\n", saved_prompt, output); } } } ================================================ FILE: wasmedge-ggml/gemma-3/src/main.rs ================================================ use serde_json::Value; use std::collections::HashMap; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> HashMap<&'static str, Value> { let mut options = HashMap::new(); // Required parameters for llava if let Ok(val) = env::var("mmproj") { options.insert("mmproj", Value::from(val.as_str())); } else { eprintln!("Failed to get mmproj model."); std::process::exit(1); } if let Ok(val) = env::var("image") { options.insert("image", Value::from(val.as_str())); } else { eprintln!("Failed to get the target image."); std::process::exit(1); } // Optional parameters if let Ok(val) = env::var("enable_log") { options.insert("enable-log", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("enable-log", Value::from(false)); } if let Ok(val) = env::var("enable_debug_log") { options.insert( "enable-debug-log", serde_json::from_str(val.as_str()).unwrap(), ); } else { options.insert("enable-debug-log", Value::from(false)); } if let Ok(val) = env::var("ctx_size") { options.insert("ctx-size", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("ctx-size", Value::from(4096)); } if let Ok(val) = env::var("n_gpu_layers") { options.insert("n-gpu-layers", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("n-gpu-layers", Value::from(0)); } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); String::from_utf8_lossy(&output_buffer[..output_size]).to_string() } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // You could also set the options manually like this: // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); return; } let mut saved_prompt = String::new(); let image_placeholder = ""; loop { println!("USER:"); let input = read_input(); // Gemma-3 prompt format: 'user\nDescribe this image\nmodel\n' if saved_prompt.is_empty() { saved_prompt = format!( "user\n{}{}\nmodel\n", image_placeholder, input ); } else { saved_prompt = format!( "{}user\n{}\nmodel\n", saved_prompt, input ); } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); std::process::exit(1); } } // Retrieve the output. let mut output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{}{}\n", saved_prompt, output); } } } ================================================ FILE: wasmedge-ggml/gemma-4/Cargo.toml ================================================ [package] name = "wasmedge-ggml-gemma-4" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.8.0" [[bin]] name = "wasmedge-ggml-gemma-4" path = "src/main.rs" ================================================ FILE: wasmedge-ggml/gemma-4/README.md ================================================ # Gemma-4 Example For WASI-NN With GGML Backend ## Requirements - WasmEdge with the WASI-NN GGML plugin installed - Rust target `wasm32-wasip1` - A Gemma 4 GGUF model file and its matching multimodal projector (`mmproj`) file (option) ## Get A Gemma-4 Model Download a Gemma 4 multimodal GGUF model and the matching `mmproj` file from Hugging Face or your own model store, then place them in this directory. Example filenames: ```text gemma-4-*.gguf mmproj-gemma-4-*.gguf ``` Replace the filenames used below with the exact files you downloaded. In this example, we use the two following command to download GGUF model and `mmproj` file. ```bash curl -LO https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF/resolve/main/mmproj-F16.gguf curl -LO https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF/resolve/main/gemma-4-E4B-it-Q4_K_M.gguf ``` ## Prepare The Image ```bash curl -LO https://llava-vl.github.io/static/images/monalisa.jpg ``` ## Build The Wasm Binary ```bash rustup target add wasm32-wasip1 cargo build --release --target wasm32-wasip1 ``` The output binary will be: ```text target/wasm32-wasip1/release/wasmedge-ggml-gemma-4.wasm ``` ## Execute ```console $ ${WASMEDGE:-wasmedge} --dir .:. \ --nn-preload default:GGML:AUTO:gemma-4-E4B-it-Q4_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-gemma-4.wasm default ``` ## Optional Environment Variables - `system_prompt`: Override the default system prompt. Default: `You are a helpful assistant.` - `enable_thinking`: Enable or disable Gemma 4 thinking mode. Default: `true` - `enable_log`: Enable WASI-NN backend logging. Default: `false` - `enable_debug_log`: Enable verbose backend logging. Default: `false` - `ctx_size`: Context size. Default: `4096` - `n_gpu_layers`: Number of layers offloaded to GPU. Default: `0` Example: ```console $ ${WASMEDGE:-wasmedge} --dir .:. \ --env mmproj=mmproj-F16.gguf \ --env image=monalisa.jpg \ --env system_prompt="You are a concise visual assistant." \ --env enable_thinking=false \ --nn-preload default:GGML:AUTO:gemma-4-E4B-it-Q4_K_M.gguf \ target/wasm32-wasip1/release/wasmedge-ggml-gemma-4.wasm default "Summarize the image in one sentence." ``` ## Prompt Format This example formats requests for Gemma 4 as: ```text <|turn>system <|think|>{system prompt} <|turn>user {user prompt} <|turn>model ``` When `enable_thinking=false`, the `<|think|>` prefix is omitted from the system turn. When multimodal input is enabled via `mmproj` and `image`, the prompt must contain the literal `` marker so the number of image markers matches the number of input bitmaps expected by the WasmEdge GGML backend. ================================================ FILE: wasmedge-ggml/gemma-4/src/main.rs ================================================ use serde_json::Value; use std::collections::HashMap; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; const MULTIMODAL_IMAGE_MARKER: &str = ""; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> HashMap<&'static str, Value> { let mut options = HashMap::new(); let mmproj = env::var("mmproj").ok(); let image = env::var("image").ok(); match (mmproj, image) { (Some(mmproj), Some(image)) => { options.insert("mmproj", Value::from(mmproj)); options.insert("image", Value::from(image)); } (None, None) => {} (Some(_), None) => { eprintln!("The `image` environment variable is required when `mmproj` is set."); std::process::exit(1); } (None, Some(_)) => { eprintln!("The `mmproj` environment variable is required when `image` is set."); std::process::exit(1); } } // Optional parameters if let Ok(val) = env::var("enable_log") { options.insert("enable-log", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("enable-log", Value::from(false)); } if let Ok(val) = env::var("enable_debug_log") { options.insert( "enable-debug-log", serde_json::from_str(val.as_str()).unwrap(), ); } else { options.insert("enable-debug-log", Value::from(false)); } if let Ok(val) = env::var("ctx_size") { options.insert("ctx-size", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("ctx-size", Value::from(4096)); } if let Ok(val) = env::var("n_gpu_layers") { options.insert("n-gpu-layers", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("n-gpu-layers", Value::from(0)); } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); String::from_utf8_lossy(&output_buffer[..output_size]).to_string() } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn get_system_prompt_from_env() -> String { env::var("system_prompt").unwrap_or_else(|_| "You are a helpful assistant.".to_string()) } fn get_enable_thinking_from_env() -> bool { env::var("enable_thinking") .ok() .and_then(|v| serde_json::from_str::(&v).ok()) .unwrap_or(true) } fn build_gemma4_system_turn(system_prompt: &str, enable_thinking: bool) -> String { if enable_thinking { format!("<|turn>system\n<|think|>{}\n", system_prompt) } else { format!("<|turn>system\n{}\n", system_prompt) } } fn build_gemma4_user_turn(user_content: &str) -> String { format!("<|turn>user\n{}\n<|turn>model\n", user_content) } fn build_user_content(user_input: &str, multimodal_enabled: bool) -> String { if multimodal_enabled { format!("{}\n{}", MULTIMODAL_IMAGE_MARKER, user_input) } else { user_input.to_string() } } fn strip_gemma4_thoughts(text: &str) -> String { let mut cleaned = text.trim().to_string(); let thought_start_tags = ["<|channel|>thought", "<|channel>thought"]; let thought_end_tags = ["<|channel|>", ""]; loop { let Some(start) = thought_start_tags .iter() .filter_map(|tag| cleaned.find(tag)) .min() else { break; }; let search_start = start + 1; let Some(rel_end) = thought_end_tags .iter() .filter_map(|tag| cleaned[search_start..].find(tag).map(|idx| (idx, tag.len()))) .min_by_key(|(idx, _)| *idx) else { cleaned.truncate(start); break; }; let end = search_start + rel_end.0 + rel_end.1; cleaned.replace_range(start..end, ""); } cleaned.trim().to_string() } fn strip_gemma4_turn_suffix(text: &str) -> String { text.trim() .strip_suffix("") .unwrap_or(text.trim()) .trim() .to_string() } fn parse_gemma4_output(text: &str) -> (String, String) { let without_thoughts = strip_gemma4_thoughts(text); let visible_output = strip_gemma4_turn_suffix(&without_thoughts); let prompt_output = if visible_output.is_empty() { String::new() } else { format!("{}\n", visible_output) }; (visible_output, prompt_output) } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // You could also set the options manually like this: // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); let system_prompt = get_system_prompt_from_env(); let enable_thinking = get_enable_thinking_from_env(); let system_turn = build_gemma4_system_turn(&system_prompt, enable_thinking); let multimodal_enabled = options.contains_key("mmproj") && options.contains_key("image"); // Non-interactive mode if args.len() >= 3 { let user_input = &args[2]; let prompt = format!( "{}{}", system_turn, build_gemma4_user_turn(&build_user_content(user_input, multimodal_enabled)) ); println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); let (visible_output, _) = parse_gemma4_output(&output); println!("{}", visible_output); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); return; } let mut saved_prompt = system_turn.clone(); loop { println!("USER:"); let input = read_input(); let user_turn = build_gemma4_user_turn(&build_user_content(&input, multimodal_enabled)); saved_prompt.push_str(&user_turn); // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); std::process::exit(1); } } // Retrieve the output. let output = get_output_from_context(&context); let (visible_output, prompt_output) = parse_gemma4_output(&output); println!("ASSISTANT:\n{}", visible_output); // Update the saved prompt. if reset_prompt { saved_prompt = system_turn.clone(); } else { saved_prompt.push_str(&prompt_output); } } } ================================================ FILE: wasmedge-ggml/grammar/Cargo.toml ================================================ [package] name = "wasmedge-ggml-grammar" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/grammar/README.md ================================================ # Grammar Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example of using grammar in ggml. ## Get the Model In this example, we are going to use the [llama-2-7b](https://huggingface.co/TheBloke/Llama-2-7B-GGUF) model. Please note that we are not using a fine-tuned chat model. ```bash curl -LO https://huggingface.co/TheBloke/Llama-2-7B-GGUF/resolve/main/llama-2-7b.Q5_K_M.gguf ``` ## Parameters > [!NOTE] > Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. In this example, we are going to use the `grammar` option to constrain the model to generate the JSON output in a specific format. You can check [the documents at llama.cpp](https://github.com/ggerganov/llama.cpp/tree/master/grammars) for more details about grammars. ## Execute In this example, we are going to use the `n_predict` option to avoid the model from generating too many outputs. ```console $ wasmedge --dir .:. \ --env n_predict=99 \ --nn-preload default:GGML:AUTO:llama-2-7b.Q5_K_M.gguf \ wasmedge-ggml-grammar.wasm default USER: JSON object with 5 country names as keys and their capitals as values: ASSISTANT: {"US": "Washington", "UK": "London", "Germany": "Berlin", "France": "Paris", "Italy": "Rome"} ``` ================================================ FILE: wasmedge-ggml/grammar/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } if let Ok(val) = env::var("n_predict") { options["n-predict"] = serde_json::from_str(val.as_str()).expect("invalid n-predict value (unsigned integer") } options["ctx-size"] = serde_json::from_str("1024").unwrap(); options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } const JSON_GRAMMAR: &str = r#" root ::= object value ::= object | array | string | number | ("true" | "false" | "null") ws object ::= "{" ws ( string ":" ws value ("," ws string ":" ws value)* )? "}" ws array ::= "[" ws ( value ("," ws value)* )? "]" ws string ::= "\"" ( [^"\\\x7F\x00-\x1F] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) )* "\"" ws number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws ws ::= ([ \t\n] ws)? "#; fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let mut options = get_options_from_env(); // Add grammar for JSON output. // Check [here](https://github.com/ggerganov/llama.cpp/tree/master/grammars) for more details. options["grammar"] = JSON_GRAMMAR.into(); // Make the output more consistent. options["temp"] = json!(0.1); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); std::process::exit(0); } loop { println!("USER:"); let input = read_input(); // Set prompt to the input tensor. set_data_to_context(&mut context, input.as_bytes().to_vec()).expect("Failed to set input"); // Execute the inference. match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); } } ================================================ FILE: wasmedge-ggml/json-schema/Cargo.toml ================================================ [package] name = "wasmedge-ggml-json-schema" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.8.0" ================================================ FILE: wasmedge-ggml/json-schema/README.md ================================================ # JSON Schema Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example of using json schema in ggml. ## Get the Model In this example, we are going to use the [llama-2-7b](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF) model. Please note that we are not using a fine-tuned chat model. ```bash curl -LO https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf ``` ## Parameters > [!NOTE] > Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. In this example, we are going to use the `json-schema` option to constrain the model to generate the JSON output in a specific format. You can check [the documents at llama.cpp](https://github.com/ggerganov/llama.cpp/tree/master/examples/main#grammars--json-schemas) for more details about this. ## Execute ```console $ wasmedge --dir .:. \ --env n_predict=99 \ --nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \ wasmedge-ggml-json-schema.wasm default USER: Give me a JSON array of Apple products. ASSISTANT: [ { "productId": 1, "productName": "iPhone 12 Pro", "price": 799.99 }, { "productId": 2, "productName": "iPad Air", "price": 599.99 }, { "productId": 3, "productName": "MacBook Air", "price": 999.99 }, { "productId": 4, "productName": "Apple Watch Series 7", "price": 399.99 }, { "productId": 5, "productName": "AirPods Pro", "price": 249.99 } ] ``` ================================================ FILE: wasmedge-ggml/json-schema/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } if let Ok(val) = env::var("n_predict") { options["n-predict"] = serde_json::from_str(val.as_str()).expect("invalid n-predict value (unsigned integer") } if let Ok(val) = env::var("json_schema") { options["json-schema"] = serde_json::from_str(val.as_str()).expect("invalid n-predict value (unsigned integer") } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } const JSON_SCHEMA: &str = r#" { "items": { "title": "Product", "description": "A product from the catalog", "type": "object", "properties": { "productId": { "description": "The unique identifier for a product", "type": "integer" }, "productName": { "description": "Name of the product", "type": "string" }, "price": { "description": "The price of the product", "type": "number", "exclusiveMinimum": 0 } }, "required": [ "productId", "productName", "price" ] }, "minItems": 5 } "#; fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let mut options = get_options_from_env(); // Add grammar for JSON output. // Check [here](https://github.com/ggerganov/llama.cpp/tree/master/grammars) for more details. options["json-schema"] = JSON_SCHEMA.into(); // Make the output more consistent. options["temp"] = json!(0.1); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); std::process::exit(0); } loop { println!("USER:"); let input = read_input(); // Set prompt to the input tensor. set_data_to_context(&mut context, input.as_bytes().to_vec()).expect("Failed to set input"); // Execute the inference. match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); } } ================================================ FILE: wasmedge-ggml/llama/Cargo.toml ================================================ [package] name = "wasmedge-ggml-llama" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/llama/README.md ================================================ # `llama` ## Execute - llama 3 ### Model Download Link ```console wget https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct.Q5_K_M.gguf ``` ### Execution Command Please make sure you have the `Meta-Llama-3-8B-Instruct.Q5_K_M.gguf` file in the current directory. Don't forget to set the `llama3` environment variable to `true` to enable the llama3 prompt template. If you want to enable GPU support, please set the `n_gpu_layers` environment variable. You can also change the `ctx_size` to have a larger context window via `--env ctx_size=8192`. The default value is 1024. ```console $ wasmedge --dir .:. \ --env llama3=true \ --env n_gpu_layers=100 \ --nn-preload default:GGML:AUTO:Meta-Llama-3-8B-Instruct.Q5_K_M.gguf \ wasmedge-ggml-llama.wasm default USER: What's WasmEdge? ASSISTANT: WasmEdge is an open-source WebAssembly runtime and compiler that can run WebAssembly code in various environments, including web browsers, mobile devices, and server-side applications. USER: Does it support in Docker? ASSISTANT: Yes, WasmEdge supports running in Docker containers. USER: Does it support in Podman? ASSISTANT: Yes, WasmEdge also supports running in Podman containers. USER: Does it work with crun? ASSISTANT: Yes, WasmEdge supports running in crun containers. ``` ## Execute - llama 2 ```console $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \ wasmedge-ggml-llama.wasm default USER: What's the capital of U.S.? ASSISTANT: The capital of the United States is Washington, D.C. (District of Columbia). USER: How about France? ASSISTANT: The capital of France is Paris. ``` ================================================ FILE: wasmedge-ggml/llama/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("llama3") { options["llama3"] = serde_json::from_str(val.as_str()) .expect("invalid value for llama3 option (true/false)"); } else { options["llama3"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } if let Ok(val) = env::var("n_predict") { options["n-predict"] = serde_json::from_str(val.as_str()).expect("invalid n_predict value (unsigned integer") } options["ctx-size"] = serde_json::from_str("1024").unwrap(); options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // We also support setting the options via input tensor with index 1. // Uncomment the line below to run the example, Check our README for more details. // set_metadata_to_context( // &mut context, // serde_json::to_string(&options) // .expect("Failed to serialize options") // .as_bytes() // .to_vec(), // ) // .expect("Failed to set metadata"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); return; } let mut saved_prompt = String::new(); let system_prompt = String::from("You are a helpful, respectful and honest assistant. Always answer as short as possible, while being safe." ); loop { println!("USER:"); let input = read_input(); if saved_prompt.is_empty() { if options["llama3"].as_bool().unwrap() { saved_prompt = format!( "<|start_header_id|>system<|end_header_id|>\n\n{}<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n", system_prompt, input ); } else { saved_prompt = format!( "[INST] <> {} <> {} [/INST]", system_prompt, input ); } } else { if options["llama3"].as_bool().unwrap() { saved_prompt = format!("{}<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n", saved_prompt, input); } else { saved_prompt = format!("{} [INST] {} [/INST]", saved_prompt, input); } } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let mut output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{} {}", saved_prompt, output); } } } ================================================ FILE: wasmedge-ggml/llama-stream/Cargo.toml ================================================ [package] name = "wasmedge-ggml-llama-stream" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/llama-stream/README.md ================================================ # `llama-stream` ## Execute - llama 3 ### Model Download Link ```console wget https://huggingface.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct.Q5_K_M.gguf ``` ### Execution Command Please make sure you have the `Meta-Llama-3-8B-Instruct.Q5_K_M.gguf` file in the current directory. Don't forget to set the `llama3` environment variable to `true` to enable the llama3 prompt template. If you want to enable GPU support, please set the `n_gpu_layers` environment variable. You can also change the `ctx_size` to have a larger context window via `--env ctx_size=8192`. The default value is 1024. ```console $ wasmedge --dir .:. \ --env llama3=true \ --env n_gpu_layers=100 \ --nn-preload default:GGML:AUTO:Meta-Llama-3-8B-Instruct.Q5_K_M.gguf \ wasmedge-ggml-llama-stream.wasm default USER: What's WasmEdge? ASSISTANT: WasmEdge is an open-source WebAssembly runtime and compiler that can run WebAssembly code in various environments, including web browsers, mobile devices, and server-side applications. USER: Does it support in Docker? ASSISTANT: Yes, WasmEdge supports running in Docker containers. USER: Does it support in Podman? ASSISTANT: Yes, WasmEdge also supports running in Podman containers. USER: Does it work with crun? ASSISTANT: Yes, WasmEdge supports running in crun containers. ``` ## Execute - llama 2 ```console $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \ wasmedge-ggml-llama-stream.wasm default USER: What's the capital of U.S.? ASSISTANT: The capital of the United States is Washington, D.C. (District of Columbia). USER: How about France? ASSISTANT: The capital of France is Paris. ``` ================================================ FILE: wasmedge-ggml/llama-stream/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io::{self, Write}; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("llama3") { options["llama3"] = serde_json::from_str(val.as_str()) .expect("invalid value for llama3 option (true/false)"); } else { options["llama3"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("ctx_size") { options["ctx-size"] = serde_json::from_str(val.as_str()).expect("invalid ctx-size value (unsigned integer") } else { options["ctx-size"] = serde_json::from_str("1024").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize, is_single: bool) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = if is_single { context .get_output_single(index, &mut output_buffer) .expect("Failed to get single output") } else { context .get_output(index, &mut output_buffer) .expect("Failed to get output") }; output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } #[allow(dead_code)] fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0, false) } fn get_single_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0, true) } #[allow(dead_code)] fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1, false)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // We also support setting the options via input tensor with index 1. // Uncomment the line below to run the example, Check our README for more details. // set_metadata_to_context( // &mut context, // serde_json::to_string(&options) // .expect("Failed to serialize options") // .as_bytes() // .to_vec(), // ) // .expect("Failed to set metadata"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. let mut output = String::new(); loop { match context.compute_single() { Ok(_) => (), Err(Error::BackendError(BackendError::EndOfSequence)) => { break; } Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); break; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); break; } Err(err) => { println!("\n[ERROR] {}", err); std::process::exit(1); } } // Retrieve the single output token and print it. let token = get_single_output_from_context(&context); print!("{}", token); io::stdout().flush().unwrap(); output += &token; } println!(); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); std::process::exit(0); } let mut saved_prompt = String::new(); let system_prompt = String::from("You are a helpful, respectful and honest assistant. Always answer as short as possible, while being safe." ); loop { println!("USER:"); let input = read_input(); if saved_prompt.is_empty() { if options["llama3"].as_bool().unwrap() { saved_prompt = format!( "<|start_header_id|>system<|end_header_id|>\n\n{}<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n", system_prompt, input ); } else { saved_prompt = format!( "[INST] <> {} <> {} [/INST]", system_prompt, input ); } } else { if options["llama3"].as_bool().unwrap() { saved_prompt = format!("{}<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n", saved_prompt, input); } else { saved_prompt = format!("{} [INST] {} [/INST]", saved_prompt, input); } } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Get the number of input tokens and llama.cpp versions. // let input_metadata = get_metadata_from_context(&context); // println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); // println!( // "[INFO] llama_build_number: {}", // input_metadata["llama_build_number"] // ); // println!( // "[INFO] Number of input tokens: {}", // input_metadata["input_tokens"] // ); // Execute the inference (streaming mode). let mut output = String::new(); let mut reset_prompt = false; println!("ASSISTANT:"); loop { match context.compute_single() { Ok(_) => (), Err(Error::BackendError(BackendError::EndOfSequence)) => { break; } Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; break; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; break; } Err(err) => { println!("\n[ERROR] {}", err); break; } } // Retrieve the single output token and print it. let token = get_single_output_from_context(&context); print!("{}", token); io::stdout().flush().unwrap(); output += &token; } println!(); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{} {}", saved_prompt, output); } // Retrieve the output metadata. // let metadata = get_metadata_from_context(&context); // println!( // "[INFO] Number of input tokens: {}", // metadata["input_tokens"] // ); // println!( // "[INFO] Number of output tokens: {}", // metadata["output_tokens"] // ); // Delete the context in compute_single mode. context.fini_single().unwrap(); } } ================================================ FILE: wasmedge-ggml/llava/Cargo.toml ================================================ [package] name = "wasmedge-ggml-llava" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/llava/README.md ================================================ # Llava Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example of the Llava model. ## Get Llava Model In this example, we are going to use the pre-converted [llava-v1.5-7b](https://huggingface.co/mys/ggml_llava-v1.5-7b) model. Download the llava v1.5 model: ```bash curl -LO https://huggingface.co/mys/ggml_llava-v1.5-7b/resolve/main/ggml-model-q5_k.gguf curl -LO https://huggingface.co/mys/ggml_llava-v1.5-7b/resolve/main/mmproj-model-f16.gguf ``` or use the llava v1.6 model: ```bash curl -LO https://huggingface.co/cmp-nct/llava-1.6-gguf/resolve/main/vicuna-7b-q5_k.gguf curl -LO https://huggingface.co/cmp-nct/llava-1.6-gguf/resolve/main/mmproj-vicuna7b-f16.gguf ``` ## Prepare the Image Download the image for the Llava model: ```bash curl -LO https://llava-vl.github.io/static/images/monalisa.jpg ``` ## Parameters > [!NOTE] > Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. For GPU offloading, please adjust the `n-gpu-layers` options to the number of layers that you want to offload to the GPU. ```rust options.insert("n-gpu-layers", Value::from(...)); ``` In llava inference, we recommend to use the `ctx-size` at least `2048` when using llava-v1.5 and at least `4096` when using llava-v1.6 for better results. ```rust options.insert("ctx-size", Value::from(4096)); ``` ## Execute (llava-1.5) Execute the WASM with the `wasmedge` using the named model feature to preload a large model: > [!NOTE] > You may see some warnings stating `key clip.vision.* not found in file.` when using llava v1.5 models. These are expected and can be ignored. ```console $ wasmedge --dir .:. \ --env mmproj=mmproj-model-f16.gguf \ --env image=monalisa.jpg \ --nn-preload default:GGML:AUTO:ggml-model-q5_k.gguf \ wasmedge-ggml-llava.wasm default USER: what is in this picture? ASSISTANT: The image features a painting or portrait of a woman with long hair, likely inspired by the famous Mona Lisa artwork. She appears to be smiling and is surrounded by an ocean view, giving the impression of being on a boat. The scene is painted in black and white, creating a classic and timeless look. Additionally, there are two birds present within this painting, one on the left side towards the top and another slightly higher up in the center. USER: Do you know who drew this painting? ASSISTANT: As a visual AI, I don't have knowledge about the artist who created this painting. However, it is inspired by the famous Mona Lisa artwork, which was painted by Leonardo da Vinci. ``` ## Execute (llava-1.6) Execute the WASM with the `wasmedge` using the named model feature to preload a large model: > [!NOTE] > For the llava-1.6 model, we will need to set the context size to at least 4096 to work. ```console $ wasmedge --dir .:. \ --env mmproj=mmproj-vicuna7b-f16.gguf \ --env image=monalisa.jpg \ --env ctx_size=4096 \ --nn-preload default:GGML:AUTO:vicuna-7b-q5_k.gguf \ wasmedge-ggml-llava.wasm default USER: what is in this picture? ASSISTANT: The image you've provided appears to be a portrait of the famous artwork known as the Mona Lisa, one of Leonardo da Vinci's most recognizable paintings. It features a woman with a subtly enigmatic smile and her gaze directed slightly off-center towards the viewer. The background is filled with elements of Renaissance landscape painting, including rolling hills, trees, waterways, and a distant city skyline, creating a serene yet intriguing atmosphere that complements the subject's ethereal presence. USER: Do you know who drew this painting? ASSISTANT: Yes, the painting was created by Leonardo da Vinci. It is one of his most celebrated works and is known for its enigmatic expression and the artist's attention to detail in both the subject's features and the landscape elements surrounding her. ``` ================================================ FILE: wasmedge-ggml/llava/src/main.rs ================================================ use serde_json::Value; use std::collections::HashMap; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> HashMap<&'static str, Value> { let mut options = HashMap::new(); // Required parameters for llava if let Ok(val) = env::var("mmproj") { options.insert("mmproj", Value::from(val.as_str())); } else { eprintln!("Failed to get mmproj model."); std::process::exit(1); } if let Ok(val) = env::var("image") { options.insert("image", Value::from(val.as_str())); } else { eprintln!("Failed to get the target image."); std::process::exit(1); } // Optional parameters if let Ok(val) = env::var("enable_log") { options.insert("enable-log", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("enable-log", Value::from(false)); } if let Ok(val) = env::var("ctx_size") { options.insert("ctx-size", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("ctx-size", Value::from(2048)); } if let Ok(val) = env::var("n_gpu_layers") { options.insert("n-gpu-layers", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("n-gpu-layers", Value::from(0)); } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); String::from_utf8_lossy(&output_buffer[..output_size]).to_string() } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // You could also set the options manually like this: // options.insert("enable-log", Value::from(false)); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); std::process::exit(0); } let mut saved_prompt = String::new(); let system_prompt = String::from("You are a helpful, respectful and honest assistant. Always answer as short as possible, while being safe." ); let image_placeholder = ""; loop { println!("USER:"); let input = read_input(); // llava chat format is "\nUSER:\n\nASSISTANT:" if saved_prompt.is_empty() { saved_prompt = format!( "{}\nUSER:{}\n{}\nASSISTANT:", system_prompt, image_placeholder, input ); } else { saved_prompt = format!("{}\nUSER: {}\nASSISTANT:", saved_prompt, input); } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let mut output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{} {}", saved_prompt, output); } } } ================================================ FILE: wasmedge-ggml/llava-base64-stream/Cargo.toml ================================================ [package] name = "wasmedge-ggml-llava-base64-stream" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/llava-base64-stream/README.md ================================================ # Llava Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example of the Llava model. > Refer to the [wasmedge-ggml/llava/README.md](../llava/README.md) for downloading Llava models and execution commands. This example is to demonstrate the usage of the Llava model inference with inline base64 encoded image. Here we hardcode the base64 encoded image in the source code. ## Execute Execute the WASM with the `wasmedge` using the named model feature to preload a large model: > [!NOTE] > You may see some warnings stating `key clip.vision.* not found in file.` when using llava v1.5 models. These are expected and can be ignored. ```console $ wasmedge --dir .:. \ --env mmproj=mmproj-model-f16.gguf \ --nn-preload default:GGML:AUTO:ggml-model-q5_k.gguf \ wasmedge-ggml-llava-base64-stream.wasm default USER: what is in this picture? ASSISTANT: The image showcases a bowl filled with an assortment of fresh berries, including several strawberries and blueberries. A person is standing close to the bowl, holding it in their hand or about to grab some fruit from it. The colorful fruit arrangement adds vibrancy to the scene. USER: please tell me a kind of fruit that is not in the picture ASSISTANT: There are no bananas in the picture. ``` ================================================ FILE: wasmedge-ggml/llava-base64-stream/src/main.rs ================================================ use serde_json::Value; use std::collections::HashMap; use std::env; use std::io::{self, Write}; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> HashMap<&'static str, Value> { let mut options = HashMap::new(); // Required parameters for llava if let Ok(val) = env::var("mmproj") { options.insert("mmproj", Value::from(val.as_str())); } else { eprintln!("Failed to get mmproj model."); std::process::exit(1); } // Optional parameters if let Ok(val) = env::var("enable_log") { options.insert("enable-log", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("enable-log", Value::from(false)); } if let Ok(val) = env::var("ctx_size") { options.insert("ctx-size", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("ctx-size", Value::from(2048)); } if let Ok(val) = env::var("n_gpu_layers") { options.insert("n-gpu-layers", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("n-gpu-layers", Value::from(0)); } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize, is_single: bool) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = if is_single { context .get_output_single(index, &mut output_buffer) .expect("Failed to get single output") } else { context .get_output(index, &mut output_buffer) .expect("Failed to get output") }; output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0, false) } fn get_single_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0, true) } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); std::process::exit(0); } let mut saved_prompt = String::new(); let system_prompt = String::from("You are a helpful, respectful and honest assistant. Always answer as short as possible, while being safe." ); // This image is converted from https://upload.wikimedia.org/wikipedia/commons/2/2f/Blackberryraspberrystrawberry.jpg let image_base64_bytes = 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"; let image_tag = format!( "", image_base64_bytes ); loop { println!("USER:"); let input = read_input(); // llava chat format is "\nUSER:\n\nASSISTANT:" if saved_prompt.is_empty() { saved_prompt = format!( "{}\nUSER:{}\n{}\nASSISTANT:", system_prompt, image_tag, input ); } else { saved_prompt = format!("{}\nUSER: {}\nASSISTANT:", saved_prompt, input); } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut output = String::new(); let mut reset_prompt = false; println!("ASSISTANT:"); loop { match context.compute_single() { Ok(_) => (), Err(Error::BackendError(BackendError::EndOfSequence)) => { break; } Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; break; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; break; } Err(err) => { println!("\n[ERROR] {}", err); break; } } // Retrieve the single output token and print it. let token = get_single_output_from_context(&context); print!("{}", token); io::stdout().flush().unwrap(); output += &token; } println!(); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{} {}", saved_prompt, output); } // Delete the context in compute_single mode. context.fini_single().unwrap(); } } ================================================ FILE: wasmedge-ggml/multimodel/Cargo.toml ================================================ [package] name = "wasmedge-ggml-multimodel" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/multimodel/README.md ================================================ # Multiple Models Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example for the chaining the results between multiple models. In this example, we will try asking the `Llava` model a question with a image, and then pass the answer to the `Llama2` model for further response. This example will demonstrate how to use WasmEdge WASI-NN plugin to link two or more models together. ## Get the Model This example uses the `Llama2` model and `Llava` model. You can download the models from the following links: ```bash curl -LO https://huggingface.co/second-state/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf curl -LO https://huggingface.co/cmp-nct/llava-1.6-gguf/resolve/main/vicuna-7b-q5_k.gguf curl -LO https://huggingface.co/cmp-nct/llava-1.6-gguf/resolve/main/mmproj-vicuna7b-f16.gguf ``` ## Parameters > [!NOTE] > Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. Download the image for the Llava model: ```bash curl -LO https://llava-vl.github.io/static/images/monalisa.jpg ``` ## Execute Execute the WASM with the `wasmedge` using the named model feature to preload the two large models: ```console $ wasmedge --dir .:. \ --env image=monalisa.jpg \ --env mmproj=mmproj-vicuna7b-f16.gguf \ --nn-preload llama2:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \ --nn-preload llava:GGML:AUTO:vicuna-7b-q5_k.gguf \ wasmedge-ggml-multimodel.wasm USER: describe this picture please ASSISTANT (llava): The image you've provided appears to be a painting of the Mona Lisa, one of Leonardo da Vinci's most famous works. It is a portrait of a woman with a serene and enigmatic expression, looking directly at the viewer. Her hair is styled in an updo, and she wears a dark dress that drapes elegantly around her shoulders. The background features a landscape with rolling hills and a river, which adds depth to the composition. The painting is renowned for its subtle changes in expression and the enigmatic smile on the subject's face, which has intrigued viewers for centuries. ASSISTANT (llama2): The image provided is a painting of the Mona Lisa, one of Leonardo da Vinci's most famous works, depicting a woman with a serene and enigmatic expression, styled in an updo with a dark dress draped elegantly around her shoulders, set against a landscape background with rolling hills and a river. ``` ================================================ FILE: wasmedge-ggml/multimodel/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); // Required parameters for llava if let Ok(val) = env::var("mmproj") { options["mmproj"] = Value::from(val.as_str()); } else { eprintln!("Failed to get mmproj model."); std::process::exit(1); } if let Ok(val) = env::var("image") { options["image"] = Value::from(val.as_str()); } else { eprintln!("Failed to get the target image."); std::process::exit(1); } // Optional parameters if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } #[allow(dead_code)] fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let mut options = get_options_from_env(); // We set the temperature to 0.1 for more consistent results. options["temp"] = Value::from(0.1); // Set the context size to 4096 tokens for the llava 1.6 model. options["ctx-size"] = Value::from(4096); // Create the llava model. let mut graphs = Vec::new(); graphs.push( GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache("llava") .expect("Failed to build graph"), ); // Remove unnecessary options for the llama2 model. options .as_object_mut() .expect("Failed to get jsons object") .remove("mmproj"); options .as_object_mut() .expect("Failed to get json object") .remove("image"); // Create the llama2 model. graphs.push( GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache("llama2") .expect("Failed to build graph"), ); // Initilize the execution contexts. let mut contexts = Vec::new(); contexts.push( graphs[0] .init_execution_context() .expect("Failed to init context"), ); contexts.push( graphs[1] .init_execution_context() .expect("Failed to init context"), ); let system_prompt = String::from("You are a helpful, respectful and honest assistant."); let mut input = String::from(""); // If the user provides a prompt, use it. println!("USER:"); if args.len() >= 2 { input += &args[1]; println!("{}", input); } else { input = read_input(); } // Llava inference. let image_placeholder = ""; let mut saved_prompt = format!( "{}\nUSER:{}\n{}\nASSISTANT:", system_prompt, image_placeholder, input ); set_data_to_context(&mut contexts[0], saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); match contexts[0].compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the llava output. let mut output = get_output_from_context(&contexts[0]); println!("ASSISTANT (llava):\n{}", output.trim()); // Llama2 inference. let llama2_prompt = "Summarize the following text in 1 sentence:"; saved_prompt = format!( "[INST] <> {} <> {} {} [/INST]", system_prompt, llama2_prompt, output.trim() ); set_data_to_context(&mut contexts[1], saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); match contexts[1].compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the llama2 output. output = get_output_from_context(&contexts[1]); println!("ASSISTANT (llama2):\n{}", output.trim()); } ================================================ FILE: wasmedge-ggml/nnrpc/Cargo.toml ================================================ [package] name = "wasmedge-ggml-nnrpc" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/nnrpc/README.md ================================================ # RPC Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example of the WASI-NN RPC usage. ## Parameters > [!NOTE] > Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. For GPU offloading, please adjust the `n-gpu-layers` options to the number of layers that you want to offload to the GPU. ```rust options.insert("n-gpu-layers", Value::from(...)); ``` In llava inference, we recommend to use the `ctx-size` at least `2048` when using llava-v1.5 and at least `4096` when using llava-v1.6 for better results. ```rust options.insert("ctx-size", Value::from(4096)); ``` ## Execute ```console # Run the RPC server. $ wasi_nn_rpcserver --nn-rpc-uri unix://$PWD/nn_server.sock \ --nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf # Run the wasmedge and inference though the RPC server. $ wasmedge \ --nn-rpc-uri unix://$PWD/nn_server.sock \ wasmedge-ggml-nnrpc.wasm default USER: What's the capital of the United States? ASSISTANT: The capital of the United States is Washington, D.C. (District of Columbia). USER: How about France? ASSISTANT: The capital of France is Paris. ``` ================================================ FILE: wasmedge-ggml/nnrpc/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } options["ctx-size"] = serde_json::from_str("1024").unwrap(); options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } #[allow(dead_code)] fn set_metadata_to_context( context: &mut GraphExecutionContext, data: Vec, ) -> Result<(), Error> { context.set_input(1, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } #[allow(dead_code)] fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // We also support setting the options via input tensor with index 1. // Uncomment the line below to run the example, Check our README for more details. set_metadata_to_context( &mut context, serde_json::to_string(&options) .expect("Failed to serialize options") .as_bytes() .to_vec(), ) .expect("Failed to set metadata"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { // Set the prompt. let prompt = &args[2]; println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the response. println!("Response:"); context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); std::process::exit(0); } let mut saved_prompt = String::new(); let system_prompt = String::from("You are a helpful, respectful and honest assistant. Always answer as short as possible, while being safe." ); loop { println!("USER:"); let input = read_input(); if saved_prompt.is_empty() { saved_prompt = format!( "[INST] <> {} <> {} [/INST]", system_prompt, input ); } else { saved_prompt = format!("{} [INST] {} [/INST]", saved_prompt, input); } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let mut output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{} {}", saved_prompt, output); } } } ================================================ FILE: wasmedge-ggml/qwen/Cargo.toml ================================================ [package] name = "wasmedge-ggml-qwen" version = "0.1.0" edition = "2021" # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/qwen/README.md ================================================ # `通义千问` ## Execute - Tong Yi Qwen ### Model Download Link ```console wget https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat-GGUF/resolve/main/qwen1_5-0_5b-chat-q2_k.gguf ``` ### Execution Command Please make sure you have the `qwen1_5-0_5b-chat-q2_k.gguf` file in the current directory. If you want to enable GPU support, please set the `n_gpu_layers` environment variable. You can also change the `ctx_size` to have a larger context window via `--env ctx_size=8192`. The default value is 1024. ```console $ wasmedge --dir .:. \ --env n_gpu_layers=10 \ --nn-preload default:GGML:AUTO:qwen1_5-0_5b-chat-q2_k.gguf \ wasmedge-ggml-qwen.wasm default USER: 你好 ASSISTANT: 你好！有什么我能帮你的吗？ USER: 你是谁 ASSISTANT: 我是一个人工智能助手，我叫通义千问。有什么我可以帮助你的吗？ USER: 能帮助我写Rust代码吗？ ASSISTANT: 当然可以！我可以帮助你使用Rust语言编写代码，帮助你理解和编写出高质量的代码。我可以帮你编写函数、函数和类，提供使用Python解释器编译和运行代码的建议，提供使用Node.js、Python或Java的代码示例，以及更多关于如何使用Python、Java或C++的代码示例。 ``` ================================================ FILE: wasmedge-ggml/qwen/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } options["ctx-size"] = serde_json::from_str("1024").unwrap(); options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); String::from_utf8((output_buffer[..output_size]).to_vec()) .unwrap() .to_string() } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); let mut saved_prompt = String::from("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"); loop { println!("USER:"); let input = read_input(); saved_prompt = format!("{}\n<|im_start|>user\n{}<|im_end|>\n", saved_prompt, input); let contex_prompt = saved_prompt.clone() + "<|im_start|>assistant\n"; // Set prompt to the input tensor. set_data_to_context(&mut context, contex_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); } } // Retrieve the output. let mut output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{} {}", saved_prompt, output); } } } ================================================ FILE: wasmedge-ggml/qwen2vl/Cargo.toml ================================================ [package] name = "wasmedge-ggml-qwen2vl" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/qwen2vl/README.md ================================================ # Qwen-2VL Example For WASI-NN with GGML Backend > [!NOTE] > Please refer to the [wasmedge-ggml/README.md](../README.md) for the general introduction and the setup of the WASI-NN plugin with GGML backend. This document will focus on the specific example of the Qwen2-VL model. ## Get Qwen2-VL Model In this example, we are going to use the pre-converted [Qwen2-VL-2B](https://huggingface.co/second-state/Qwen2-VL-2B-Instruct-GGUF/tree/main) model. Download the model: ```bash curl -LO https://huggingface.co/second-state/Qwen2-VL-2B-Instruct-GGUF/resolve/main/Qwen2-VL-2B-Instruct-vision-encoder.gguf curl -LO https://huggingface.co/second-state/Qwen2-VL-2B-Instruct-GGUF/resolve/main/Qwen2-VL-2B-Instruct-Q5_K_M.gguf ``` ## Prepare the Image Download the image you want to perform inference on: ```bash curl -LO https://llava-vl.github.io/static/images/monalisa.jpg ``` ## Parameters > [!NOTE] > Please check the parameters section of [wasmedge-ggml/README.md](https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters) first. In Qwen2-VL inference, we recommend to use the `ctx-size` at least `4096` for better results. ```rust options.insert("ctx-size", Value::from(4096)); ``` ## Execute Execute the WASM with the `wasmedge` using the named model feature to preload a large model: ```bash wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:Qwen2-VL-2B-Instruct-Q5_K_M.gguf \ --env mmproj=Qwen2-VL-2B-Instruct-vision-encoder.gguf \ --env image=monalisa.jpg \ --env ctx_size=4096 \ wasmedge-ggml-qwen2vl.wasm default ``` ================================================ FILE: wasmedge-ggml/qwen2vl/src/main.rs ================================================ use serde_json::Value; use std::collections::HashMap; use std::env; use std::io; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn read_input() -> String { loop { let mut answer = String::new(); io::stdin() .read_line(&mut answer) .expect("Failed to read line"); if !answer.is_empty() && answer != "\n" && answer != "\r\n" { return answer.trim().to_string(); } } } fn get_options_from_env() -> HashMap<&'static str, Value> { let mut options = HashMap::new(); // Required parameters for llava if let Ok(val) = env::var("mmproj") { options.insert("mmproj", Value::from(val.as_str())); } else { eprintln!("Failed to get mmproj model."); std::process::exit(1); } if let Ok(val) = env::var("image") { options.insert("image", Value::from(val.as_str())); } else { eprintln!("Failed to get the target image."); std::process::exit(1); } // Optional parameters if let Ok(val) = env::var("enable_log") { options.insert("enable-log", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("enable-log", Value::from(false)); } if let Ok(val) = env::var("ctx_size") { options.insert("ctx-size", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("ctx-size", Value::from(4096)); } if let Ok(val) = env::var("n_gpu_layers") { options.insert("n-gpu-layers", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("n-gpu-layers", Value::from(0)); } options } fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); String::from_utf8_lossy(&output_buffer[..output_size]).to_string() } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // You could also set the options manually like this: // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() >= 3 { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Get the output. context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); // Retrieve the output metadata. let metadata = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", metadata["input_tokens"] ); println!( "[INFO] Number of output tokens: {}", metadata["output_tokens"] ); std::process::exit(0); } let mut saved_prompt = String::new(); let system_prompt = String::from("You are a helpful assistant."); let image_placeholder = ""; loop { println!("USER:"); let input = read_input(); // Qwen2VL prompt format: <|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n<|vision_start|>{image_placeholder}<|vision_end|>{user_prompt}<|im_end|>\n<|im_start|>assistant\n"; if saved_prompt.is_empty() { saved_prompt = format!( "<|im_start|>system\n{}<|im_end|>\n<|im_start|>user\n<|vision_start|>{}<|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n", system_prompt, image_placeholder, input ); } else { saved_prompt = format!( "{}<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n", saved_prompt, input ); } // Set prompt to the input tensor. set_data_to_context(&mut context, saved_prompt.as_bytes().to_vec()) .expect("Failed to set input"); // Execute the inference. let mut reset_prompt = false; match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); reset_prompt = true; } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); reset_prompt = true; } Err(err) => { println!("\n[ERROR] {}", err); std::process::exit(1); } } // Retrieve the output. let mut output = get_output_from_context(&context); println!("ASSISTANT:\n{}", output.trim()); // Update the saved prompt. if reset_prompt { saved_prompt.clear(); } else { output = output.trim().to_string(); saved_prompt = format!("{}{}<|im_end|>\n", saved_prompt, output); } } } ================================================ FILE: wasmedge-ggml/test/model-not-found/Cargo.toml ================================================ [package] name = "wasmedge-ggml-model-not-found" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/test/model-not-found/README.md ================================================ # `model-not-found` Ensure that we get the `ModelNotFound` error when the model does not exist. ## Execute ```console $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:model-not-found.gguf \ wasmedge-ggml-model-not-found.wasm default ``` ================================================ FILE: wasmedge-ggml/test/model-not-found/src/main.rs ================================================ use std::env; use wasmedge_wasi_nn::{self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding}; fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO).build_from_cache(model_name); // Check graph match graph { Err(Error::BackendError(BackendError::ModelNotFound)) => { println!("Model not found"); } Err(_) => { panic!("Should be model not found"); } Ok(_) => { panic!("Should be model not found"); } } } ================================================ FILE: wasmedge-ggml/test/phi-3/Cargo.toml ================================================ [package] name = "wasmedge-ggml-phi-3" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/test/phi-3/README.md ================================================ # `phi-3-mini` Ensure that we can use the `phi-3-mini` model. ## Execute ```console $ curl -LO https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/resolve/main/Phi-3-mini-4k-instruct-q4.gguf $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:Phi-3-mini-4k-instruct-q4.gguf \ wasmedge-ggml-phi-3-mini.wasm default \ ``` ================================================ FILE: wasmedge-ggml/test/phi-3/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } options } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // This is mainly for the CI workflow. Only support the prompt from the command line. if args.len() < 3 { println!("Usage: {} ", args[0]); std::process::exit(1); } let prompt = &args[2]; // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); println!("Graph {} loaded.", graph); let mut context = graph .init_execution_context() .expect("Failed to init context"); // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Execute the inference. match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); } Err(err) => { println!("\n[ERROR] {}", err); std::process::exit(1); } } // Retrieve the output. let output = get_output_from_context(&context); println!("{}", output.trim()); // Unload. graph.unload().expect("Failed to unload graph"); } ================================================ FILE: wasmedge-ggml/test/set-input-twice/Cargo.toml ================================================ [package] name = "wasmedge-ggml-set-input-twice" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/test/set-input-twice/README.md ================================================ # `set-input-twice` Ensure that we get the same result from executing `set_input` twice. ## Execute ```console $ curl -LO https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \ wasmedge-ggml-set-input-twice.wasm default '' ``` ================================================ FILE: wasmedge-ggml/test/set-input-twice/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use wasmedge_wasi_nn::{ self, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } options["ctx-size"] = serde_json::from_str("1024").unwrap(); options } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&get_data_from_context(context, 1)).expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() < 3 { println!("Usage: {} ", args[0]); } else { let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); // Set the prompt, twice. context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); // Get the number of input tokens and llama.cpp versions. let input_metadata_after = get_metadata_from_context(&context); println!( "[INFO] Number of input tokens: {}", input_metadata_after["input_tokens"] ); // Check it the numbers of input_tokens are the same if input_metadata["input_tokens"] != input_metadata_after["input_tokens"] { panic!("The number of input tokens is different after setting the input twice."); } } } ================================================ FILE: wasmedge-ggml/test/unload/Cargo.toml ================================================ [package] name = "wasmedge-ggml-unload" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/test/unload/README.md ================================================ # `unload` Ensure that we can unload and reload the graph multiple times without errors. ## Execute ```console $ curl -LO https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf $ wasmedge --dir .:. \ --nn-preload default:GGML:AUTO:llama-2-7b-chat.Q5_K_M.gguf \ wasmedge-ggml-unload.wasm default \ ``` ================================================ FILE: wasmedge-ggml/test/unload/src/main.rs ================================================ use serde_json::json; use serde_json::Value; use std::env; use wasmedge_wasi_nn::{ self, BackendError, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn get_options_from_env() -> Value { let mut options = json!({}); if let Ok(val) = env::var("enable_log") { options["enable-log"] = serde_json::from_str(val.as_str()) .expect("invalid value for enable-log option (true/false)") } else { options["enable-log"] = serde_json::from_str("false").unwrap() } if let Ok(val) = env::var("n_gpu_layers") { options["n-gpu-layers"] = serde_json::from_str(val.as_str()).expect("invalid ngl value (unsigned integer") } else { options["n-gpu-layers"] = serde_json::from_str("0").unwrap() } options } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. if args.len() < 3 { println!("Usage: {} ", args[0]); std::process::exit(1); } let prompt = &args[2]; // Create and inference 5 times to make sure unload function works. for i in 0..5 { println!("----- Test {} -----", i); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); println!("Graph {} loaded.", graph); let mut context = graph .init_execution_context() .expect("Failed to init context"); // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); println!("Response:"); // Execute the inference. match context.compute() { Ok(_) => (), Err(Error::BackendError(BackendError::ContextFull)) => { println!("\n[INFO] Context full, we'll reset the context and continue."); } Err(Error::BackendError(BackendError::PromptTooLong)) => { println!("\n[INFO] Prompt too long, we'll reset the context and continue."); } Err(err) => { println!("\n[ERROR] {}", err); std::process::exit(1); } } // Retrieve the output. let output = get_output_from_context(&context); println!("{}", output.trim()); // Unload. graph.unload().expect("Failed to unload graph"); } } ================================================ FILE: wasmedge-ggml/tts/Cargo.toml ================================================ [package] name = "wasmedge-ggml-tts" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.7.1" ================================================ FILE: wasmedge-ggml/tts/README.md ================================================ # `tts` - Text-to-Speech Example ## Model Download ```console wget https://huggingface.co/second-state/OuteTTS-0.2-500M-GGUF/resolve/main/OuteTTS-0.2-500M-Q5_K_M.gguf wget https://huggingface.co/second-state/OuteTTS-0.2-500M-GGUF/resolve/main/wavtokenizer-large-75-ggml-f16.gguf ``` ## Speaker Profile Download ```console wget https://raw.githubusercontent.com/edwko/OuteTTS/refs/heads/main/outetts/version/v1/default_speakers/en_male_1.json ``` > [!NOTE] > The default speaker profile of the plugin is `en_female_1.json`. ### Execution ```console $ wasmedge --dir .:. \ --env tts=true \ --env tts_output_file=output.wav \ --env tts_speaker_file=en_male_1.json \ --env model_vocoder=wavtokenizer-large-75-ggml-f16.gguf \ --nn-preload default:GGML:AUTO:OuteTTS-0.2-500M-Q5_K_M.gguf \ wasmedge-ggml-tts.wasm default 'Hello, world.' Prompt: Hello, world! [INFO] llama_commit: "955a6c2d" [INFO] llama_build_number: 4534 [INFO] Number of input tokens: 891 [INFO] Write output file to "output.wav" ``` ================================================ FILE: wasmedge-ggml/tts/src/main.rs ================================================ use serde_json::Value; use std::collections::HashMap; use std::env; use std::fs::File; use std::io::Write; use wasmedge_wasi_nn::{ self, Error, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn get_options_from_env() -> HashMap<&'static str, Value> { let mut options = HashMap::new(); // Required parameters for tts options.insert("tts", Value::from(true)); if let Ok(val) = env::var("tts_output_file") { options.insert("tts-output-file", Value::from(val.as_str())); } else { eprintln!("Failed to get output file name."); std::process::exit(1); } if let Ok(val) = env::var("model_vocoder") { options.insert("model-vocoder", Value::from(val.as_str())); } else { eprintln!("Failed to get vocoder model."); std::process::exit(1); } // Speaker profile is optional. if let Ok(val) = env::var("tts_speaker_file") { options.insert("tts-speaker-file", Value::from(val.as_str())); } // Optional parameters if let Ok(val) = env::var("enable_log") { options.insert("enable-log", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("enable-log", Value::from(false)); } if let Ok(val) = env::var("enable_debug_log") { options.insert( "enable-debug-log", serde_json::from_str(val.as_str()).unwrap(), ); } else { options.insert("enable-debug-log", Value::from(false)); } if let Ok(val) = env::var("n_predict") { options.insert("n-predict", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("n-predict", Value::from(4096)); } if let Ok(val) = env::var("ctx_size") { options.insert("ctx-size", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("ctx-size", Value::from(8192)); } if let Ok(val) = env::var("batch_size") { options.insert("batch-size", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("batch-size", Value::from(8192)); } if let Ok(val) = env::var("ubatch_size") { options.insert("ubatch-size", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("ubatch-size", Value::from(8192)); } if let Ok(val) = env::var("n_gpu_layers") { options.insert("n-gpu-layers", serde_json::from_str(val.as_str()).unwrap()); } else { options.insert("n-gpu-layers", Value::from(100)); } if let Ok(val) = env::var("seed") { options.insert("seed", serde_json::from_str(val.as_str()).unwrap()); } if let Ok(val) = env::var("temp") { options.insert("temp", serde_json::from_str(val.as_str()).unwrap()); } options } #[allow(dead_code)] fn set_data_to_context(context: &mut GraphExecutionContext, data: Vec) -> Result<(), Error> { context.set_input(0, TensorType::U8, &[data.len()], &data) } fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> Vec { // Use 1MB as the maximum output buffer size for audio output. const MAX_OUTPUT_BUFFER_SIZE: usize = 1024 * 1024; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); output_buffer[..output_size].to_vec() } fn get_metadata_from_context(context: &GraphExecutionContext) -> Value { serde_json::from_str(&String::from_utf8_lossy(&get_data_from_context(context, 1)).to_string()) .expect("Failed to get metadata") } fn main() { let args: Vec = env::args().collect(); if args.len() < 3 { println!("Usage: {} ", args[0]); return; } let model_name: &str = &args[1]; // Set options for the graph. Check our README for more details: // https://github.com/second-state/WasmEdge-WASINN-examples/tree/master/wasmedge-ggml#parameters let options = get_options_from_env(); // Create graph and initialize context. let graph = GraphBuilder::new(GraphEncoding::Ggml, ExecutionTarget::AUTO) .config(serde_json::to_string(&options).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); // If there is a third argument, use it as the prompt and enter non-interactive mode. // This is mainly for the CI workflow. let prompt = &args[2]; // Set the prompt. println!("Prompt:\n{}", prompt); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); // Get the number of input tokens and llama.cpp versions. let input_metadata = get_metadata_from_context(&context); println!("[INFO] llama_commit: {}", input_metadata["llama_commit"]); println!( "[INFO] llama_build_number: {}", input_metadata["llama_build_number"] ); println!( "[INFO] Number of input tokens: {}", input_metadata["input_tokens"] ); context.compute().expect("Failed to compute"); println!( "[INFO] Plugin writes output to file {}", options["tts-output-file"] ); // Write output buffer to file, should be the same as the output file in the options. let output_filename = "output-buffer.wav"; let output_bytes = get_data_from_context(&context, 0); let mut output_file = File::create(output_filename).expect("Failed to create output file"); output_file .write_all(&output_bytes) .expect("Failed to write output file"); println!("[INFO] Write output buffer to file {}", output_filename); return; } ================================================ FILE: wasmedge-ggml/whisper/Cargo.toml ================================================ [package] name = "whisper-basic" version = "0.1.0" edition = "2021" [dependencies] wasmedge-wasi-nn = "0.8.0" ================================================ FILE: wasmedge-ggml/whisper/README.md ================================================ # Basic Example For WASI-NN with Whisper Backend This example is for a basic audio recognition with WASI-NN whisper backend in WasmEdge. In current status, WasmEdge implement the Whisper backend of WASI-NN in only English. We'll extend more options in the future. ## Dependencies This crate depends on the `wasmedge-wasi-nn` in the `Cargo.toml`: ```toml [dependencies] wasmedge-wasi-nn = "0.8.0" ``` ## Build Compile the application to WebAssembly: ```bash cargo build --target=wasm32-wasip1 --release ``` The output WASM file will be at [`target/wasm32-wasip1/release/whisper-basic.wasm`](whisper-basic.wasm). To speed up the processing, we can enable the AOT mode in WasmEdge with: ```bash wasmedge compile target/wasm32-wasip1/release/whisper-basic.wasm whisper-basic_aot.wasm ``` ## Run ### Test data The testing audio is located at `./test.wav`. Users should get the model by the guide from [whisper.cpp repository](https://github.com/ggerganov/whisper.cpp/tree/master/models): ```bash curl -sSf https://raw.githubusercontent.com/ggerganov/whisper.cpp/master/models/download-ggml-model.sh | bash -s -- base.en ``` The model will be stored at `./ggml-base.en.bin`. ### Input Audio The WASI-NN whisper backend for WasmEdge currently supported 16kHz, 1 channel, and `pcm_s16le` format. Users can convert their input audio as following `ffmpeg` command: ```bash ffmpeg -i test.m4a -acodec pcm_s16le -ac 1 -ar 16000 test.wav ``` ### Execute > Note: This is prepared for `0.14.2` or later release in the future. Please build from source now. Users should [install the WasmEdge with WASI-NN plug-in in Whisper backend](https://wasmedge.org/docs/start/install/#wasi-nn-plug-ins). ```bash curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- --plugins wasi_nn-whisper ``` Execute the WASM with the `wasmedge` with WASI-NN plug-in: ```bash wasmedge --dir .:. whisper-basic_aot.wasm ggml-base.en.bin test.wav ``` You will get recognized string from the audio file in the output: ```bash Read model, size in bytes: 147964211 Loaded graph into wasi-nn with ID: Graph#0 Read input tensor, size in bytes: 141408 Recognized from audio: [00:00:00.000 --> 00:00:04.300] This is a test record for whisper.cpp ``` ================================================ FILE: wasmedge-ggml/whisper/src/main.rs ================================================ use std::env; use std::error::Error; use std::fs; use wasmedge_wasi_nn::{ExecutionTarget, GraphBuilder, GraphEncoding, TensorType}; pub fn main() -> Result<(), Box> { let args: Vec = env::args().collect(); let model_bin_name: &str = &args[1]; let wav_name: &str = &args[2]; let model_bin = fs::read(model_bin_name)?; println!("Read model, size in bytes: {}", model_bin.len()); let graph = GraphBuilder::new(GraphEncoding::Whisper, ExecutionTarget::CPU) .build_from_bytes(&[&model_bin])?; let mut ctx = graph.init_execution_context()?; println!("Loaded graph into wasi-nn with ID: {}", graph); // Load the raw pcm tensor. let wav_buf = fs::read(wav_name)?; println!("Read input tensor, size in bytes: {}", wav_buf.len()); // Set input. ctx.set_input(0, TensorType::F32, &[1, wav_buf.len()], &wav_buf)?; // Execute the inference. ctx.compute()?; // Retrieve the output. let mut output_buffer = vec![0u8; 2048]; _ = ctx.get_output(0, &mut output_buffer)?; println!( "Recognized from audio: \n{}", String::from_utf8(output_buffer).unwrap() ); Ok(()) } ================================================ FILE: wasmedge-mlx/llama/Cargo.toml ================================================ [package] name = "wasmedge-mlx" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.8.0" ================================================ FILE: wasmedge-mlx/llama/README.md ================================================ # MLX example with WasmEdge WASI-NN MLX plugin This example demonstrates using WasmEdge WASI-NN MLX plugin to perform an inference task with LLM model. ## Supported Models | Family | Models | |--------|--------| | LLaMA 2 | llama_2_7b_chat_hf | | LLaMA 3 | llama_3_8b | | TinyLLaMA | tiny_llama_1.1B_chat_v1.0 | ## Install WasmEdge with WASI-NN MLX plugin The MLX backend relies on [MLX](https://github.com/ml-explore/mlx), but we will auto-download MLX when you build WasmEdge. You do not need to install it yourself. If you want to custom MLX, install it yourself or set the `CMAKE_PREFIX_PATH` variable when configuring cmake. Build and install WasmEdge from source: ``` bash cd cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_PLUGIN_WASI_NN_BACKEND="mlx" cmake --build build # For the WASI-NN plugin, you should install this project. cmake --install build ``` Then you will have an executable `wasmedge` runtime under `/usr/local/bin` and the WASI-NN with MLX backend plug-in under `/usr/local/lib/wasmedge/libwasmedgePluginWasiNN.so` after installation. ## Download the model and tokenizer In this example, we will use `tiny_llama_1.1B_chat_v1.0`, which you can change to `llama_2_7b_chat_hf` or `llama_3_8b`. ``` bash # Download model weight wget https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/resolve/main/model.safetensors # Download tokenizer wget https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/resolve/main/tokenizer.json ``` ## Build wasm Run the following command to build wasm, the output WASM file will be at `target/wasm32-wasip1/release/` ```bash cargo build --target wasm32-wasip1 --release ``` ## Execute Execute the WASM with the `wasmedge` using nn-preload to load model. ``` bash wasmedge --dir .:. \ --nn-preload default:mlx:AUTO:model.safetensors \ ./target/wasm32-wasip1/release/wasmedge-mlx.wasm default ``` If your model has multiple weight files, you need to provide all in the nn-preload. For example: ``` bash wasmedge --dir .:. \ --nn-preload default:mlx:AUTO:llama2-7b/model-00001-of-00002.safetensors:llama2-7b/model-00002-of-00002.safetensors \ ./target/wasm32-wasip1/release/wasmedge-mlx.wasm default ``` ## Other There are some metadata for MLX plugin you can set. ### Basic setting - model_type (required): LLM model type. - tokenizer (required): tokenizer.json path - max_token (option): maximum generate token number, default is 1024. - enable_debug_log (option): if print debug log, default is false. ### Quantization The following three parameters need to be set together. - is_quantized (option): If the weight is quantized. If is_quantized is false, then MLX backend will quantize the weight. - group_size (option): The group size to use for quantization. - q_bits (option): The number of bits to quantize to. ``` rust let graph = GraphBuilder::new(GraphEncoding::Mlx, ExecutionTarget::AUTO) .config(serde_json::to_string(&json!({"model_type": "tiny_llama_1.1B_chat_v1.0", "tokenizer":tokenizer_path, "max_token":100})) ``` ================================================ FILE: wasmedge-mlx/llama/src/main.rs ================================================ use serde_json::json; use std::env; use wasmedge_wasi_nn::{ self, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn main() { let tokenizer_path = "./tokenizer.json"; let prompt = "Once upon a time, there existed a little girl,"; let args: Vec = env::args().collect(); let model_name: &str = &args[1]; let graph = GraphBuilder::new(GraphEncoding::Mlx, ExecutionTarget::AUTO) .config(serde_json::to_string(&json!({"is_quantized":false, "group_size": 64, "q_bits": 4,"model_type": "tiny_llama_1.1B_chat_v1.0", "tokenizer":tokenizer_path, "max_token":100})).expect("Failed to serialize options")) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); let tensor_data = prompt.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); } ================================================ FILE: wasmedge-mlx/vlm/Cargo.toml ================================================ [package] name = "wasmedge-vlm" version = "0.1.0" edition = "2021" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.8.0" rust_processor = { git = "https://github.com/second-state/wasi_processor", subdirectory = "processor", branch = "main" } ================================================ FILE: wasmedge-mlx/vlm/README.md ================================================ # VLM example with WasmEdge WASI-NN MLX plugin This example demonstrates using WasmEdge WASI-NN MLX plugin to perform an inference task with VLM model. ## Supported Models | Family | Models | |--------|--------| | Gemma 3 | gemma-3-4b-pt-bf16 | ## Install WasmEdge with WASI-NN MLX plugin The MLX backend relies on [MLX](https://github.com/ml-explore/mlx), but we will auto-download MLX when you build WasmEdge. You do not need to install it yourself. If you want to custom MLX, install it yourself or set the `CMAKE_PREFIX_PATH` variable when configuring cmake. Build and install WasmEdge from source: ``` bash cd cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_PLUGIN_WASI_NN_BACKEND="mlx" cmake --build build # For the WASI-NN plugin, you should install this project. cmake --install build ``` Then you will have an executable `wasmedge` runtime under `/usr/local/bin` and the WASI-NN with MLX backend plug-in under `/usr/local/lib/wasmedge/libwasmedgePluginWasiNN.so` after installation. ## Download the model and tokenizer In this example, we will use `gemma-3-4b-it-4bit`. ``` bash git clone https://huggingface.co/mlx-community/gemma-3-4b-it-4bit ``` ## Build wasm Run the following command to build wasm, the output WASM file will be at `target/wasm32-wasip1/release/`. Then we use AOT-compiled WASM to improve the performance. ```bash cargo build --target wasm32-wasip1 --release wasmedge compiler ./target/wasm32-wasip1/release/wasmedge-vlm.wasm wasmedge-vlm_aot.wasm ``` ## Execute Execute the WASM with the `wasmedge` using nn-preload to load model. ``` bash # Download sample image wget https://github.com/WasmEdge/WasmEdge/raw/master/docs/wasmedge-runtime-logo.png wasmedge --dir .:. \ --nn-preload default:mlx:AUTO:gemma-3-4b-it-4bit/model.safetensors \ ./wasmedge-vlm_aot.wasm default gemma-3-4b-it-4bit ``` If your model has multiple weight files, you need to provide all in the nn-preload. For example: ``` bash wasmedge --dir .:. \ --nn-preload default:mlx:AUTO:gemma-3-4b-it-4bit/model-00001-of-00002.safetensors:gemma-3-4b-it-4bit/model-00002-of-00002.safetensors \ ./target/wasm32-wasip1/release/wasmedge-vlm.wasm default ``` ## Other There are some metadata for MLX plugin you can set. ### Basic setting - model_type (required): model type. - max_token (option): maximum generate token number, default is 1024. - enable_debug_log (option): if print debug log, default is false. ================================================ FILE: wasmedge-mlx/vlm/decode.py ================================================ from transformers import AutoProcessor import mlx.core as mx import sys def _remove_space(x): if x and x[0] == " ": return x[1:] return x class Detokenizer(): def __init__(self, tokenizer, trim_space=True): self.trim_space = trim_space self.tokenmap = [None] * len(tokenizer.vocab) for value, tokenid in tokenizer.vocab.items(): self.tokenmap[tokenid] = value for i in range(len(self.tokenmap)): if self.tokenmap[i].startswith("<0x"): self.tokenmap[i] = chr(int(self.tokenmap[i][3:5], 16)) self.offset = 0 self._unflushed = "" self.text = "" self.tokens = [] def add_token(self, token): v = self.tokenmap[token] if v[0] == "\u2581": if self.text or not self.trim_space: self.text += self._unflushed.replace("\u2581", " ") else: self.text = _remove_space( self._unflushed.replace("\u2581", " ")) self._unflushed = v else: self._unflushed += v def decode(token: list, model_path: str, **kwargs): processor = AutoProcessor.from_pretrained(model_path, **kwargs) detokenizer = Detokenizer(processor.tokenizer) for (i, token) in enumerate(token): detokenizer.add_token(token) return detokenizer.text if __name__ == "__main__": model_path, output = sys.argv[1:] tokenList = mx.load(output) print(decode(tokenList.tolist(), model_path)) ================================================ FILE: wasmedge-mlx/vlm/encode.py ================================================ from transformers import AutoProcessor import mlx.core as mx from PIL import Image, ImageOps import sys def encode(processor, image, prompts): model_inputs = {} processor.tokenizer.pad_token = processor.tokenizer.eos_token image = Image.open(image) image = ImageOps.exif_transpose(image) image = image.convert("RGB") images = [image] inputs = processor( text=prompts, images=images, padding=True, return_tensors="mlx" ) if "images" in inputs: inputs["pixel_values"] = inputs["images"] inputs.pop("images") if isinstance(inputs["pixel_values"], list): pixel_values = inputs["pixel_values"] else: pixel_values = mx.array(inputs["pixel_values"]) model_inputs["pixel_values"] = pixel_values model_inputs["attention_mask"] = ( mx.array(inputs["attention_mask"] ) if "attention_mask" in inputs else None ) # Convert inputs to model_inputs with mx.array if present for key, value in inputs.items(): if key not in model_inputs and not isinstance(value, (str, list)): model_inputs[key] = mx.array(value) mx.save("input_ids.npy", model_inputs["input_ids"]) mx.save("pixel_values.npy", model_inputs["pixel_values"]) mx.save("mask.npy", model_inputs["attention_mask"]) if __name__ == "__main__": model_path, image, prompts = sys.argv[1:] processor = AutoProcessor.from_pretrained(model_path) formatted_prompt = f"user\n\ {prompts}\n\ model" encode(processor, image, formatted_prompt) ================================================ FILE: wasmedge-mlx/vlm/src/main.rs ================================================ use rust_processor::auto::processing_auto::AutoProcessor; use rust_processor::gemma3::detokenizer::decode; use rust_processor::processor_utils::prepare_inputs; use rust_processor::NDTensorI32; use std::env; use wasmedge_wasi_nn::{ self, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; use serde_json::Value; use std::fs::File; use std::io::{self, BufReader}; fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> NDTensorI32 { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return NDTensorI32::from_bytes(&output_buffer[..output_size]).unwrap(); } fn get_output_from_context(context: &GraphExecutionContext) -> NDTensorI32 { get_data_from_context(context, 0) } fn read_json(path: &str) -> io::Result { let file = File::open(path)?; let reader = BufReader::new(file); let v = serde_json::from_reader(reader) .map_err(|e| io::Error::new(io::ErrorKind::InvalidData, e))?; Ok(v) } fn main() { // prompt: "What is this icon?"; // image: "wasmedge-runtime-logo.png"; let args: Vec = env::args().collect(); let model_name: &str = &args[1]; let model_dir = &args[2]; let config = read_json(&format!("{}/config.json", model_dir)).unwrap(); let prompt = "user\ What is this icon?\ model"; let image_path = "wasmedge-runtime-logo.png"; println!("create processor: {}", model_dir); let mut processor = match AutoProcessor::from_pretrained(model_dir) { Ok(processor) => match processor { rust_processor::auto::processing_auto::AutoProcessorType::Gemma3(processor) => { processor } _ => { eprintln!("Error loading processor: not a Gemma3Processor"); return; } }, Err(e) => { eprintln!("Error loading processor: {}", e); return; } }; println!("processor created"); let image_token_index = config["image_token_index"].as_u64().unwrap_or(262144) as u32; let model_inputs = prepare_inputs( &mut processor, &[image_path], // Use single image array prompt, image_token_index, Some((896, 896)), // Use 896x896 as image size ); let graph = GraphBuilder::new(GraphEncoding::Mlx, ExecutionTarget::AUTO) .config(config.to_string()) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); let tensor_data = model_inputs["input_ids"].to_bytes(); context .set_input(0, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); let tensor_data = model_inputs["pixel_values"].to_bytes(); context .set_input(1, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); let tensor_data = model_inputs["mask"].to_bytes(); context .set_input(2, TensorType::U8, &[tensor_data.len()], &tensor_data) .expect("Failed to set input"); context.compute().expect("Failed to compute"); let tokens = get_output_from_context(&context); let output = decode( &tokens .data .into_iter() .map(|x| x as usize) .collect::>(), &processor, true, ); println!("{}", output.trim()); } ================================================ FILE: wasmedge-mlx/whisper/Cargo.toml ================================================ [package] name = "wasmedge-whisper" version = "0.1.0" edition = "2024" [dependencies] serde_json = "1.0" wasmedge-wasi-nn = "0.8.0" ================================================ FILE: wasmedge-mlx/whisper/README.md ================================================ # Whisper example with WasmEdge WASI-NN MLX plugin This example demonstrates using WasmEdge WASI-NN MLX plugin to perform an inference task with whisper model. ## Install WasmEdge with WASI-NN MLX plugin The MLX backend relies on [MLX](https://github.com/ml-explore/mlx), but we will auto-download MLX when you build WasmEdge. You do not need to install it yourself. If you want to custom MLX, install it yourself or set the `CMAKE_PREFIX_PATH` variable when configuring cmake. Build and install WasmEdge from source: ``` bash cd cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_PLUGIN_WASI_NN_BACKEND="mlx" cmake --build build # For the WASI-NN plugin, you should install this project. cmake --install build ``` Then you will have an executable `wasmedge` runtime under `/usr/local/bin` and the WASI-NN with MLX backend plug-in under `/usr/local/lib/wasmedge/libwasmedgePluginWasiNN.so` after installation. ## Download the model and tokenizer In this example, we will use `whisper-tiny`. ``` bash git clone https://huggingface.co/grorge123/whisper-tiny cp -r whisper-tiny/assets . wget https://raw.githubusercontent.com/ml-explore/mlx-examples/refs/heads/main/whisper/mlx_whisper/assets/multilingual.tiktoken -P assets ``` ## Build wasm Run the following command to build wasm, the output WASM file will be at `target/wasm32-wasip1/release/`. Then we use AOT-compiled WASM to improve the performance. ```bash cargo build --target wasm32-wasip1 --release ``` ## Execute Download the audio and save as `audio.mp3` Execute the WASM with the `wasmedge` using nn-preload to load model. ``` bash wasmedge --dir .:. \ --nn-preload default:mlx:AUTO:whisper-tiny/weights.safetensors \ target/wasm32-wasip1/release/wasmedge-whisper.wasm default whisper-tiny audio.mp3 ``` ================================================ FILE: wasmedge-mlx/whisper/src/main.rs ================================================ use std::env; use wasmedge_wasi_nn::{ self, ExecutionTarget, GraphBuilder, GraphEncoding, GraphExecutionContext, TensorType, }; use serde_json::Value; use std::fs::File; use std::io::{self, BufReader}; fn get_data_from_context(context: &GraphExecutionContext, index: usize) -> String { // Preserve for 4096 tokens with average token length 6 const MAX_OUTPUT_BUFFER_SIZE: usize = 4096 * 6; let mut output_buffer = vec![0u8; MAX_OUTPUT_BUFFER_SIZE]; let mut output_size = context .get_output(index, &mut output_buffer) .expect("Failed to get output"); output_size = std::cmp::min(MAX_OUTPUT_BUFFER_SIZE, output_size); return String::from_utf8_lossy(&output_buffer[..output_size]).to_string(); } fn get_output_from_context(context: &GraphExecutionContext) -> String { get_data_from_context(context, 0) } fn read_json(path: &str) -> io::Result { let file = File::open(path)?; let reader = BufReader::new(file); let v = serde_json::from_reader(reader) .map_err(|e| io::Error::new(io::ErrorKind::InvalidData, e))?; Ok(v) } fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; let model_dir = &args[2]; let audio = &args[3]; let config = read_json(&format!("{}/config.json", model_dir)).unwrap(); let graph = GraphBuilder::new(GraphEncoding::Mlx, ExecutionTarget::AUTO) .config(config.to_string()) .build_from_cache(model_name) .expect("Failed to build graph"); let mut context = graph .init_execution_context() .expect("Failed to init context"); let tensor_data = audio.as_bytes().to_vec(); context .set_input(0, TensorType::U8, &[1], &tensor_data) .expect("Failed to set input"); context.compute().expect("Failed to compute"); let output = get_output_from_context(&context); println!("{}", output.trim()); } ================================================ FILE: wasmedge-piper/Cargo.toml ================================================ [package] name = "wasmedge-piper" version = "0.1.0" edition = "2021" # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html [dependencies] serde_json = "1.0.120" wasmedge-wasi-nn = "0.8.0" ================================================ FILE: wasmedge-piper/README.md ================================================ # Text to speech example with WasmEdge WASI-NN Piper plugin This example demonstrates how to use WasmEdge WASI-NN Piper plugin to perform TTS. ## Build WasmEdge with WASI-NN Piper plugin Overview of WASI-NN Piper plugin dependencies: ![d2 --layout elk dependencies.d2 dependencies.svg](dependencies.svg) - [piper](https://github.com/rhasspy/piper): A fast, local neural text to speech system. - [piper-phonemize](https://github.com/rhasspy/piper-phonemize): C++ library for converting text to phonemes for Piper. - [espeak-ng](https://github.com/rhasspy/espeak-ng): An open source speech synthesizer that supports more than hundred languages and accents. Piper uses it for text to phoneme translation. - [onnxruntime](https://github.com/microsoft/onnxruntime): A cross-platform inference and training machine-learning accelerator. [ONNX](https://onnx.ai/) is an open format built to represent machine learning models. Piper uses ONNX Runtime as an inference backend for its ONNX models to convert phoneme ids to WAV audio. The WasmEdge WASI-NN Piper plugin relies on the ONNX Runtime C++ API. For installation instructions, please refer to the installation table on the [official website](https://onnxruntime.ai/getting-started). Example of installing ONNX Runtime 1.14.1 on Ubuntu: ```bash curl -LO https://github.com/microsoft/onnxruntime/releases/download/v1.14.1/onnxruntime-linux-x64-1.14.1.tgz tar zxf onnxruntime-linux-x64-1.14.1.tgz mv onnxruntime-linux-x64-1.14.1/include/* /usr/local/include/ mv onnxruntime-linux-x64-1.14.1/lib/* /usr/local/lib/ rm -rf onnxruntime-linux-x64-1.14.1.tgz onnxruntime-linux-x64-1.14.1 ldconfig ``` For other dependencies, WasmEdge will download and build them automatically. Build WasmEdge from source: ```bash cd /path/to/wasmedge/source/folder cmake -GNinja -Bbuild -DCMAKE_BUILD_TYPE=Release -DWASMEDGE_USE_LLVM=OFF -DWASMEDGE_PLUGIN_WASI_NN_BACKEND=Piper cmake --build build ``` Then you will have an executable `wasmedge` runtime at `build/tools/wasmedge/wasmedge` and the WASI-NN with Piper backend plug-in at `build/plugins/wasi_nn/libwasmedgePluginWasiNN.so`. ## Model Download Link In this example, we will use the [en_US-lessac-medium](https://huggingface.co/rhasspy/piper-voices/tree/main/en/en_US/lessac/medium) model. [MODEL CARD](https://huggingface.co/rhasspy/piper-voices/blob/main/en/en_US/lessac/medium/MODEL_CARD): ``` # Model card for lessac (medium) * Language: en_US (English, United States) * Speakers: 1 * Quality: medium * Samplerate: 22,050Hz ## Dataset * URL: https://www.cstr.ed.ac.uk/projects/blizzard/2013/lessac_blizzard2013/ * License: https://www.cstr.ed.ac.uk/projects/blizzard/2013/lessac_blizzard2013/license.html ## Training Trained from scratch. ``` It has a model file [en_US-lessac-medium.onnx](https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx) and a config file [en_US-lessac-medium.onnx.json](https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx.json). ```bash # Download model curl -LO https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx # Download config curl -LO https://huggingface.co/rhasspy/piper-voices/resolve/main/en/en_US/lessac/medium/en_US-lessac-medium.onnx.json ``` This model uses [eSpeak NG](https://github.com/rhasspy/espeak-ng) to convert text to phonemes, so we also need to download the required espeak-ng-data. This will download and extract the espeak-ng-data directory to the current working directory: ```bash curl -LO https://github.com/rhasspy/piper/releases/download/2023.11.14-2/piper_linux_x86_64.tar.gz tar -xzf piper_linux_x86_64.tar.gz piper/espeak-ng-data --strip-components=1 ``` ## Build wasm Run the following command to build wasm, the output WASM file will be at `target/wasm32-wasip1/release/` ```bash cargo build --target wasm32-wasip1 --release ``` ## Execute Execute the WASM with the `wasmedge`. ```bash WASMEDGE_PLUGIN_PATH=/path/to/parent/directory/of/libwasmedgePluginWasiNN.so /path/to/wasmedge --dir .:. /path/to/wasm ``` Example layout: ``` . ├── en_US-lessac-medium.onnx ├── en_US-lessac-medium.onnx.json ├── espeak-ng-data/ ├── WasmEdge/build/ │ ├── plugins/wasi_nn/libwasmedgePluginWasiNN.so │ └── tools/wasmedge/wasmedge └── WasmEdge-WASINN-examples/wasmedge-piper/target/wasm32-wasip1/release/wasmedge-piper.wasm ``` Then the command will be: ```bash WASMEDGE_PLUGIN_PATH=WasmEdge/build/plugins/wasi_nn WasmEdge/build/tools/wasmedge/wasmedge --dir .:. WasmEdge-WASINN-examples/wasmedge-piper/target/wasm32-wasip1/release/wasmedge-piper.wasm ``` The output `welcome.wav` is the synthesized audio. ## Config options The JSON config options passed to WasmEdge WASI-NN Piper plugin via `bytes_array` in `wasmedge_wasi_nn::GraphBuilder::build_from_bytes` is similar to the Piper command-line program options. See [config.schema.json](config.schema.json) for available options and [json_input.schema.json](json_input.schema.json) for JSON input. ================================================ FILE: wasmedge-piper/config.schema.json ================================================ { "$schema": "http://json-schema.org/draft-07/schema#", "properties": { "model": { "description": "Path to .onnx voice file", "type": "string" }, "config": { "description": "Path to JSON voice config file, default is model path + .json", "type": "string" }, "output_type": { "default": "wav", "description": "Type of output to produce", "enum": [ "raw", "wav" ] }, "speaker": { "default": 0, "description": "Numerical id of the default speaker (multi-speaker voices)", "type": "number" }, "noise_scale": { "default": 0.667, "description": "Amount of noise to add during audio generation, default value can be overridden by the value in voice model config", "type": "number" }, "length_scale": { "default": 1.0, "description": "Speed of speaking (1 = normal, < 1 is faster, > 1 is slower), default value can be overridden by the value in voice model config", "type": "number" }, "noise_w": { "default": 0.8, "description": "Variation in phoneme lengths, default value can be overridden by the value in voice model config", "type": "number" }, "sentence_silence": { "default": 0.2, "description": "Seconds of silence to add after each sentence", "type": "number" }, "espeak_data": { "description": "Path to espeak-ng data directory, required for espeak phonemes", "type": "string" }, "tashkeel_model": { "description": "Path to libtashkeel ort model (https://github.com/mush42/libtashkeel), required for Arabic", "type": "string" }, "json_input": { "default": false, "description": "input is JSON instead of text", "type": "boolean" }, "phoneme_silence": { "additionalProperties": { "type": "number" }, "description": "Seconds of extra silence to insert after a single phoneme, this is a mapping from single codepoints to seconds" } }, "required": [ "model" ] } ================================================ FILE: wasmedge-piper/dependencies.d2 ================================================ direction: right WasmEdge WASI-NN Piper plugin -> piper piper -> piper-phonemize piper -> espeak-ng piper -> onnxruntime piper-phonemize -> espeak-ng piper-phonemize -> onnxruntime ================================================ FILE: wasmedge-piper/json_input.schema.json ================================================ { "$schema": "http://json-schema.org/draft-07/schema#", "properties": { "text": { "description": "Text input for speech synthesis", "type": "string" }, "speaker_id": { "description": "Override the default speaker id, takes precedence over speaker", "type": "number" }, "speaker": { "description": "Override the default speaker by name", "type": "string" } }, "required": [ "text" ] } ================================================ FILE: wasmedge-piper/src/main.rs ================================================ fn main() { // create graph with the config let config = serde_json::json!({ "model": "en_US-lessac-medium.onnx", // path to .onnx voice file, required "config": "en_US-lessac-medium.onnx.json", // path to JSON voice config file, optional, default is model path + .json "espeak_data": "espeak-ng-data", // path to espeak-ng data directory, required for espeak phonemes }); let graph = wasmedge_wasi_nn::GraphBuilder::new( wasmedge_wasi_nn::GraphEncoding::Piper, wasmedge_wasi_nn::ExecutionTarget::CPU, ) .build_from_bytes([config.to_string()]) .unwrap(); let mut context = graph.init_execution_context().unwrap(); // set the input text let text = "Welcome to the world of speech synthesis!"; context .set_input( 0, wasmedge_wasi_nn::TensorType::U8, &[text.len()], text.as_bytes(), ) .unwrap(); // synthesize the audio context.compute().unwrap(); // retrieve the output, output is wav by default let mut out_buffer = vec![0u8; 1 << 20]; let size = context.get_output(0, &mut out_buffer).unwrap(); std::fs::write("welcome.wav", &out_buffer[..size]).unwrap(); } ================================================ FILE: wasmedge-tf-llama/README.md ================================================ # Llama Example For WasmEdge-Tensorflow plug-in This package is a high-level Rust bindings for [WasmEdge-TensorFlow plug-in](https://wasmedge.org/docs/develop/rust/tensorflow) example of Mobilenet. ## Note To build this example, you will need the nightly version of Rust. ## Dependencies This crate depends on the `wasmedge_tensorflow_interface` in the `Cargo.toml`: ```toml [dependencies] wasmedge_tensorflow_interface = "0.3.0" wasi-nn = "0.1.0" # Ensure you use the latest version thiserror = "1.0" bytemuck = "1.13.1" log = "0.4.19" env_logger = "0.10.0" anyhow = "1.0.79" ``` ## Build Compile the application to WebAssembly: ```bash cd rust && cargo build --target=wasm32-wasip1 --release ``` The output WASM file will be at [`rust/target/wasm32-wasip1/release/wasmedge-tf-example-llama.wasm`](wasmedge-tf-example-llama.wasm). To speed up the image processing, we can enable the AOT mode in WasmEdge with: ```bash wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-tf-example-llama.wasm wasmedge-tf-example-llama_aot.wasm ``` ## Run The frozen `tflite` model should be translated through `ai_edge_torch` and HuggingFace. ### Execute Users should [install the WasmEdge with WasmEdge-TensorFlow and WasmEdge-Image plug-ins](https://wasmedge.org/docs/start/install#wasmedge-tensorflow-plug-in). ```bash curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- --plugins wasmedge_tensorflow wasmedge_image ``` Execute the WASM with the `wasmedge` with Tensorflow Lite supporting: ```bash wasmedge --dir .:. wasmedge-tf-example-llama.wasm ./llama_1b_q8_ekv1280.tflite ``` You will get the output: ```console Input the Chatbot: Hello world Hello world! ``` ================================================ FILE: wasmedge-tf-llama/rust/Cargo.toml ================================================ [package] name = "wasmedge-tf-example-llama" version = "0.1.0" authors = ["victoryang00"] readme = "README.md" edition = "2021" publish = false [dependencies] wasmedge_tensorflow_interface = "0.3.0" wasi-nn = "0.1.0" # Ensure you use the latest version thiserror = "1.0" bytemuck = "1.13.1" log = "0.4.19" env_logger = "0.10.0" anyhow = "1.0.79" [workspace] ================================================ FILE: wasmedge-tf-llama/rust/src/main.rs ================================================ #![feature(str_as_str)] use bytemuck::{cast_slice, cast_slice_mut}; use std::collections::HashMap; use std::env; use std::fs::File; use std::io::Read; use std::io::{self, BufRead, Write}; use std::process; use thiserror::Error; use wasi_nn; // Define a custom error type to handle multiple error sources #[derive(Error, Debug)] pub enum ChatbotError { #[error("IO Error: {0}")] Io(#[from] std::io::Error), #[error("WASI NN Error: {0}")] WasiNn(String), #[error("Other Error: {0}")] Other(String), } impl From for ChatbotError { fn from(error: wasi_nn::Error) -> Self { ChatbotError::WasiNn(error.to_string()) } } type Result = std::result::Result; // Tokenizer Struct struct Tokenizer { vocab: HashMap, vocab_reverse: HashMap, next_id: i32, } impl Tokenizer { fn new(initial_vocab: Vec<(&str, i32)>) -> Self { let mut vocab = HashMap::new(); let mut vocab_reverse = HashMap::new(); let mut next_id = 1; for (word, id) in initial_vocab { vocab.insert(word.to_string(), id); vocab_reverse.insert(id, word.to_string()); if id >= next_id { next_id = id + 1; } } // Add special tokens vocab.insert("".to_string(), 0); vocab_reverse.insert(0, "".to_string()); vocab.insert("".to_string(), -1); vocab_reverse.insert(-1, "".to_string()); Tokenizer { vocab, vocab_reverse, next_id, } } fn tokenize(&mut self, input: &str) -> Vec { input .split_whitespace() .map(|word| { self.vocab.get(word).cloned().unwrap_or_else(|| { let id = self.next_id; self.vocab.insert(word.to_string(), id); self.vocab_reverse.insert(id, word.to_string()); self.next_id += 1; id }) }) .collect() } fn tokenize_with_fixed_length(&mut self, input: &str, max_length: usize) -> Vec { let mut tokens = self.tokenize(input); if tokens.len() > max_length { tokens.truncate(max_length); } else if tokens.len() < max_length { tokens.extend(vec![-1; max_length - tokens.len()]); // Assuming -1 is the token } tokens } fn detokenize(&self, tokens: &[i32]) -> String { tokens .iter() .map(|&token| { self.vocab_reverse .get(&token) .map_or("", |v| v) .as_str() }) .collect::>() .join(" ") } } // Function to load the TFLite model fn load_model(model_path: &str) -> Result { let mut file = File::open(model_path)?; let mut buffer = Vec::new(); file.read_to_end(&mut buffer)?; let model_segments = &[&buffer[..]]; Ok(unsafe { wasi_nn::load(model_segments, 4, wasi_nn::EXECUTION_TARGET_CPU)? }) } // Function to initialize the execution context fn init_context(graph: wasi_nn::Graph) -> Result { Ok(unsafe { wasi_nn::init_execution_context(graph)? }) } fn main() -> Result<()> { // Parse command-line arguments let args: Vec = env::args().collect(); if args.len() < 2 { eprintln!("Usage: {} ", args[0]); process::exit(1); } let model_file = &args[1]; // Load the model let graph = load_model(model_file)?; // Initialize execution context let ctx = init_context(graph)?; let mut stdout = io::stdout(); let stdin = io::stdin(); // Initialize KV cache data println!("Chatbot is ready! Type your messages below:"); for line in stdin.lock().lines() { let user_input = line?; if user_input.trim().is_empty() { continue; } if user_input.to_lowercase() == "exit" { break; } // Initialize tokenizer let initial_vocab = vec![ ("hello", 1), ("world", 2), ("this", 3), ("is", 4), ("a", 5), ("test", 6), ("", -1), ]; let mut tokenizer = Tokenizer::new(initial_vocab); // let user_input = "hello world this is a test with more words"; // Tokenize with fixed length let max_length = 655360; let tokens = tokenizer.tokenize_with_fixed_length(&user_input, max_length); let tokens_dims = &[1u32, max_length as u32]; let tokens_tensor = wasi_nn::Tensor { dimensions: tokens_dims, r#type: wasi_nn::TENSOR_TYPE_I32, data: cast_slice(&tokens), }; // Create input_pos tensor let input_pos: Vec = (0..max_length as i32).collect(); let input_pos_dims = &[1u32, max_length as u32]; let input_pos_tensor = wasi_nn::Tensor { dimensions: input_pos_dims, r#type: wasi_nn::TENSOR_TYPE_I32, data: cast_slice(&input_pos), }; // Create kv tensor (ensure kv_data has the correct size) let kv_data = vec![0.0_f32; max_length]; // Example initialization let kv_dims = &[32u32, 2u32, 1u32, 16u32, 10u32, 64u32]; let kv_tensor = wasi_nn::Tensor { dimensions: kv_dims, r#type: wasi_nn::TENSOR_TYPE_F32, data: cast_slice(&kv_data), }; // Set inputs unsafe { wasi_nn::set_input(ctx, 0, tokens_tensor)?; wasi_nn::set_input(ctx, 1, input_pos_tensor)?; wasi_nn::set_input(ctx, 2, kv_tensor)?; } // Run inference run_inference(&ctx)?; // Get output let output = get_model_output(&ctx, 0, 655360)?; // Detokenize output let response = tokenizer.detokenize(&output.as_slice()); // Display response writeln!(stdout, "Bot: {}", response)?; } println!("Chatbot session ended."); Ok(()) } // Function to run inference fn run_inference(ctx: &wasi_nn::GraphExecutionContext) -> Result<()> { unsafe { Ok(wasi_nn::compute(*ctx)?) } } // Function to get model output fn get_model_output( ctx: &wasi_nn::GraphExecutionContext, index: u32, size: usize, ) -> Result> { let mut buffer = vec![0i32; size]; let buffer_ptr = cast_slice_mut(&mut buffer).as_mut_ptr(); let byte_len = (size * std::mem::size_of::()) as u32; unsafe { wasi_nn::get_output(*ctx, index, buffer_ptr, byte_len)? }; Ok(buffer) } ================================================ FILE: wasmedge-tf-mobilenet_v2/README.md ================================================ # Mobilenet Example For WasmEdge-Tensorflow plug-in This package is a high-level Rust bindings for [WasmEdge-TensorFlow plug-in](https://wasmedge.org/docs/develop/rust/tensorflow) example of Mobilenet. ## Dependencies This crate depends on the `wasmedge_tensorflow_interface` in the `Cargo.toml`: ```toml [dependencies] wasmedge_tensorflow_interface = "0.3.0" ``` ## Build Compile the application to WebAssembly: ```bash cd rust && cargo build --target=wasm32-wasip1 --release ``` The output WASM file will be at [`rust/target/wasm32-wasip1/release/wasmedge-tf-example-mobilenet.wasm`](wasmedge-tf-example-mobilenet.wasm). To speed up the image processing, we can enable the AOT mode in WasmEdge with: ```bash wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-tf-example-mobilenet.wasm wasmedge-tf-example-mobilenet_aot.wasm ``` ## Run ### Test data The testing image is located at `./grace_hopper.jpg`: ![grace_hopper](grace_hopper.jpg) The frozen `pb` model is located at `./mobilenet_v2_1.4_224_frozen.pb`. ### Execute Users should [install the WasmEdge with WasmEdge-TensorFlow and WasmEdge-Image plug-ins](https://wasmedge.org/docs/start/install#wasmedge-tensorflow-plug-in). ```bash curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- --plugins wasmedge_tensorflow wasmedge_image ``` Execute the WASM with the `wasmedge` with Tensorflow Lite supporting: ```bash wasmedge --dir .:. wasmedge-tf-example-mobilenet.wasm mobilenet_v2_1.4_224_frozen.pb grace_hopper.jpg ``` You will get the output: ```console 653 : 0.3230184316635132 ``` Which is index 653 (0-based index) with rate `0.33690106868743896`. The index 653 of label table (which is line 654 in `imagenet_slim_labels.txt`) is `military uniform`. ================================================ FILE: wasmedge-tf-mobilenet_v2/imagenet_slim_labels.txt ================================================ dummy tench goldfish great white shark tiger shark hammerhead electric ray stingray cock hen ostrich brambling goldfinch house finch junco indigo bunting robin bulbul jay magpie chickadee water ouzel kite bald eagle vulture great grey owl European fire salamander common newt eft spotted salamander axolotl bullfrog tree frog tailed frog loggerhead leatherback turtle mud turtle terrapin box turtle banded gecko common iguana American chameleon whiptail agama frilled lizard alligator lizard Gila monster green lizard African chameleon Komodo dragon African crocodile American alligator triceratops thunder snake ringneck snake hognose snake green snake king snake garter snake water snake vine snake night snake boa constrictor rock python Indian cobra green mamba sea snake horned viper diamondback sidewinder trilobite harvestman scorpion black and gold garden spider barn spider garden spider black widow tarantula wolf spider tick centipede black grouse ptarmigan ruffed grouse prairie chicken peacock quail partridge African grey macaw sulphur-crested cockatoo lorikeet coucal bee eater hornbill hummingbird jacamar toucan drake red-breasted merganser goose black swan tusker echidna platypus wallaby koala wombat jellyfish sea anemone brain coral flatworm nematode conch snail slug sea slug chiton chambered nautilus Dungeness crab rock crab fiddler crab king crab American lobster spiny lobster crayfish hermit crab isopod white stork black stork spoonbill flamingo little blue heron American egret bittern crane limpkin European gallinule American coot bustard ruddy turnstone red-backed sandpiper redshank dowitcher oystercatcher pelican king penguin albatross grey whale killer whale dugong sea lion Chihuahua Japanese spaniel Maltese dog Pekinese Shih-Tzu Blenheim spaniel papillon toy terrier Rhodesian ridgeback Afghan hound basset beagle bloodhound bluetick black-and-tan coonhound Walker hound English foxhound redbone borzoi Irish wolfhound Italian greyhound whippet Ibizan hound Norwegian elkhound otterhound Saluki Scottish deerhound Weimaraner Staffordshire bullterrier American Staffordshire terrier Bedlington terrier Border terrier Kerry blue terrier Irish terrier Norfolk terrier Norwich terrier Yorkshire terrier wire-haired fox terrier Lakeland terrier Sealyham terrier Airedale cairn Australian terrier Dandie Dinmont Boston bull miniature schnauzer giant schnauzer standard schnauzer Scotch terrier Tibetan terrier silky terrier soft-coated wheaten terrier West Highland white terrier Lhasa flat-coated retriever curly-coated retriever golden retriever Labrador retriever Chesapeake Bay retriever German short-haired pointer vizsla English setter Irish setter Gordon setter Brittany spaniel clumber English springer Welsh springer spaniel cocker spaniel Sussex spaniel Irish water spaniel kuvasz schipperke groenendael malinois briard kelpie komondor Old English sheepdog Shetland sheepdog collie Border collie Bouvier des Flandres Rottweiler German shepherd Doberman miniature pinscher Greater Swiss Mountain dog Bernese mountain dog Appenzeller EntleBucher boxer bull mastiff Tibetan mastiff French bulldog Great Dane Saint Bernard Eskimo dog malamute Siberian husky dalmatian affenpinscher basenji pug Leonberg Newfoundland Great Pyrenees Samoyed Pomeranian chow keeshond Brabancon griffon Pembroke Cardigan toy poodle miniature poodle standard poodle Mexican hairless timber wolf white wolf red wolf coyote dingo dhole African hunting dog hyena red fox kit fox Arctic fox grey fox tabby tiger cat Persian cat Siamese cat Egyptian cat cougar lynx leopard snow leopard jaguar lion tiger cheetah brown bear American black bear ice bear sloth bear mongoose meerkat tiger beetle ladybug ground beetle long-horned beetle leaf beetle dung beetle rhinoceros beetle weevil fly bee ant grasshopper cricket walking stick cockroach mantis cicada leafhopper lacewing dragonfly damselfly admiral ringlet monarch cabbage butterfly sulphur butterfly lycaenid starfish sea urchin sea cucumber wood rabbit hare Angora hamster porcupine fox squirrel marmot beaver guinea pig sorrel zebra hog wild boar warthog hippopotamus ox water buffalo bison ram bighorn ibex hartebeest impala gazelle Arabian camel llama weasel mink polecat black-footed ferret otter skunk badger armadillo three-toed sloth orangutan gorilla chimpanzee gibbon siamang guenon patas baboon macaque langur colobus proboscis monkey marmoset capuchin howler monkey titi spider monkey squirrel monkey Madagascar cat indri Indian elephant African elephant lesser panda giant panda barracouta eel coho rock beauty anemone fish sturgeon gar lionfish puffer abacus abaya academic gown accordion acoustic guitar aircraft carrier airliner airship altar ambulance amphibian analog clock apiary apron ashcan assault rifle backpack bakery balance beam balloon ballpoint Band Aid banjo bannister barbell barber chair barbershop barn barometer barrel barrow baseball basketball bassinet bassoon bathing cap bath towel bathtub beach wagon beacon beaker bearskin beer bottle beer glass bell cote bib bicycle-built-for-two bikini binder binoculars birdhouse boathouse bobsled bolo tie bonnet bookcase bookshop bottlecap bow bow tie brass brassiere breakwater breastplate broom bucket buckle bulletproof vest bullet train butcher shop cab caldron candle cannon canoe can opener cardigan car mirror carousel carpenter's kit carton car wheel cash machine cassette cassette player castle catamaran CD player cello cellular telephone chain chainlink fence chain mail chain saw chest chiffonier chime china cabinet Christmas stocking church cinema cleaver cliff dwelling cloak clog cocktail shaker coffee mug coffeepot coil combination lock computer keyboard confectionery container ship convertible corkscrew cornet cowboy boot cowboy hat cradle crane crash helmet crate crib Crock Pot croquet ball crutch cuirass dam desk desktop computer dial telephone diaper digital clock digital watch dining table dishrag dishwasher disk brake dock dogsled dome doormat drilling platform drum drumstick dumbbell Dutch oven electric fan electric guitar electric locomotive entertainment center envelope espresso maker face powder feather boa file fireboat fire engine fire screen flagpole flute folding chair football helmet forklift fountain fountain pen four-poster freight car French horn frying pan fur coat garbage truck gasmask gas pump goblet go-kart golf ball golfcart gondola gong gown grand piano greenhouse grille grocery store guillotine hair slide hair spray half track hammer hamper hand blower hand-held computer handkerchief hard disc harmonica harp harvester hatchet holster home theater honeycomb hook hoopskirt horizontal bar horse cart hourglass iPod iron jack-o'-lantern jean jeep jersey jigsaw puzzle jinrikisha joystick kimono knee pad knot lab coat ladle lampshade laptop lawn mower lens cap letter opener library lifeboat lighter limousine liner lipstick Loafer lotion loudspeaker loupe lumbermill magnetic compass mailbag mailbox maillot maillot manhole cover maraca marimba mask matchstick maypole maze measuring cup medicine chest megalith microphone microwave military uniform milk can minibus miniskirt minivan missile mitten mixing bowl mobile home Model T modem monastery monitor moped mortar mortarboard mosque mosquito net motor scooter mountain bike mountain tent mouse mousetrap moving van muzzle nail neck brace necklace nipple notebook obelisk oboe ocarina odometer oil filter organ oscilloscope overskirt oxcart oxygen mask packet paddle paddlewheel padlock paintbrush pajama palace panpipe paper towel parachute parallel bars park bench parking meter passenger car patio pay-phone pedestal pencil box pencil sharpener perfume Petri dish photocopier pick pickelhaube picket fence pickup pier piggy bank pill bottle pillow ping-pong ball pinwheel pirate pitcher plane planetarium plastic bag plate rack plow plunger Polaroid camera pole police van poncho pool table pop bottle pot potter's wheel power drill prayer rug printer prison projectile projector puck punching bag purse quill quilt racer racket radiator radio radio telescope rain barrel recreational vehicle reel reflex camera refrigerator remote control restaurant revolver rifle rocking chair rotisserie rubber eraser rugby ball rule running shoe safe safety pin saltshaker sandal sarong sax scabbard scale school bus schooner scoreboard screen screw screwdriver seat belt sewing machine shield shoe shop shoji shopping basket shopping cart shovel shower cap shower curtain ski ski mask sleeping bag slide rule sliding door slot snorkel snowmobile snowplow soap dispenser soccer ball sock solar dish sombrero soup bowl space bar space heater space shuttle spatula speedboat spider web spindle sports car spotlight stage steam locomotive steel arch bridge steel drum stethoscope stole stone wall stopwatch stove strainer streetcar stretcher studio couch stupa submarine suit sundial sunglass sunglasses sunscreen suspension bridge swab sweatshirt swimming trunks swing switch syringe table lamp tank tape player teapot teddy television tennis ball thatch theater curtain thimble thresher throne tile roof toaster tobacco shop toilet seat torch totem pole tow truck toyshop tractor trailer truck tray trench coat tricycle trimaran tripod triumphal arch trolleybus trombone tub turnstile typewriter keyboard umbrella unicycle upright vacuum vase vault velvet vending machine vestment viaduct violin volleyball waffle iron wall clock wallet wardrobe warplane washbasin washer water bottle water jug water tower whiskey jug whistle wig window screen window shade Windsor tie wine bottle wing wok wooden spoon wool worm fence wreck yawl yurt web site comic book crossword puzzle street sign traffic light book jacket menu plate guacamole consomme hot pot trifle ice cream ice lolly French loaf bagel pretzel cheeseburger hotdog mashed potato head cabbage broccoli cauliflower zucchini spaghetti squash acorn squash butternut squash cucumber artichoke bell pepper cardoon mushroom Granny Smith strawberry orange lemon fig pineapple banana jackfruit custard apple pomegranate hay carbonara chocolate sauce dough meat loaf pizza potpie burrito red wine espresso cup eggnog alp bubble cliff coral reef geyser lakeside promontory sandbar seashore valley volcano ballplayer groom scuba diver rapeseed daisy yellow lady's slipper corn acorn hip buckeye coral fungus agaric gyromitra stinkhorn earthstar hen-of-the-woods bolete ear toilet tissue ================================================ FILE: wasmedge-tf-mobilenet_v2/mobilenet_v2_1.4_224_frozen.pb ================================================ [File too large to display: 23.4 MB] ================================================ FILE: wasmedge-tf-mobilenet_v2/rust/Cargo.toml ================================================ [package] name = "wasmedge-tf-example-mobilenet" version = "0.1.0" authors = ["Second-State"] readme = "README.md" edition = "2021" publish = false [dependencies] wasmedge_tensorflow_interface = "0.3.0" [workspace] ================================================ FILE: wasmedge-tf-mobilenet_v2/rust/src/main.rs ================================================ use std::env; use std::fs::File; use std::io::Read; use wasmedge_tensorflow_interface; fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; let image_name: &str = &args[2]; let mut file_mod = File::open(model_name).unwrap(); let mut mod_buf = Vec::new(); file_mod.read_to_end(&mut mod_buf).unwrap(); let mut file_img = File::open(image_name).unwrap(); let mut img_buf = Vec::new(); file_img.read_to_end(&mut img_buf).unwrap(); let flat_img = wasmedge_tensorflow_interface::load_jpg_image_to_rgb32f(&img_buf, 224, 224); /* The `flat_img` float vector can prepared as following: let img = image::load_from_memory(&img_buf).unwrap().to_rgb8(); let resized = image::imageops::resize(&img, 224, 224, ::image::imageops::FilterType::Triangle); let mut flat_img: Vec = Vec::new(); for rgb in resized.pixels() { flat_img.push(rgb[0] as f32 / 255.); flat_img.push(rgb[1] as f32 / 255.); flat_img.push(rgb[2] as f32 / 255.); } Note that the dependencies are needed in Cargo.toml: image = { version = "0.23.0", default-features = false, features = ["jpeg", "png", "gif"] } imageproc = "0.21.0" */ let mut session = wasmedge_tensorflow_interface::TFSession::new(&mod_buf); session .add_input("input", &flat_img, &[1, 224, 224, 3]) .add_output("MobilenetV2/Predictions/Softmax") .run(); let res_vec: Vec = session.get_output("MobilenetV2/Predictions/Softmax"); let mut i = 0; let mut max_index: i32 = -1; let mut max_value: f32 = -1.0; while i < res_vec.len() { let cur = res_vec[i]; if cur > max_value { max_value = cur; max_index = i as i32; } i += 1; } println!("{} : {}", max_index, max_value as f64); } ================================================ FILE: wasmedge-tf-mtcnn/README.md ================================================ # Mobilenet Example For WasmEdge-Tensorflow plug-in This package is a high-level Rust bindings for [WasmEdge-TensorFlow plug-in](https://wasmedge.org/docs/develop/rust/tensorflow) example of Mobilenet. ## Dependencies This crate depends on the `wasmedge_tensorflow_interface` in the `Cargo.toml`: ```toml [dependencies] wasmedge_tensorflow_interface = "0.3.0" ``` ## Build Compile the application to WebAssembly: ```bash cd rust && cargo build --target=wasm32-wasip1 --release ``` The output WASM file will be at [`rust/target/wasm32-wasip1/release/wasmedge-tf-example-mtcnn.wasm`](wasmedge-tf-example-mtcnn.wasm). To speed up the image processing, we can enable the AOT mode in WasmEdge with: ```bash wasmedge compile rust/target/wasm32-wasip1/release/wasmedge-tf-example-mtcnn.wasm wasmedge-tf-example-mtcnn_aot.wasm ``` ## Run ### Test data The testing image is located at `./solvay.jpg`: ![solvay](solvay.jpg) The frozen `pb` model is located at `./mtcnn.pb`. ### Execute Users should [install the WasmEdge with WasmEdge-TensorFlow](https://wasmedge.org/docs/start/install#wasmedge-tensorflow-plug-in). ```bash curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- --plugins wasmedge_tensorflow ``` Execute the WASM with the `wasmedge` with Tensorflow Lite supporting: ```bash wasmedge --dir .:. wasmedge-tf-example-mtcnn.wasm mtcnn.pb solvay.jpg out.jpg ``` You will get the output: ```console Drawing box: 30 results ... ``` Please check the output `out.jpg` for the result. ================================================ FILE: wasmedge-tf-mtcnn/rust/Cargo.toml ================================================ [package] name = "wasmedge-tf-example-mtcnn" version = "0.1.0" authors = ["Second-State"] readme = "README.md" edition = "2021" publish = false [dependencies] wasmedge_tensorflow_interface = "0.3.0" image = { version = "0.23.0", default-features = false, features = ["jpeg", "png", "gif"] } imageproc = "0.21.0" [workspace] ================================================ FILE: wasmedge-tf-mtcnn/rust/src/main.rs ================================================ use std::env; use std::fs::File; use std::io::Read; use wasmedge_tensorflow_interface::TFSession; use image::{GenericImageView, Pixel}; use imageproc::drawing::draw_hollow_rect_mut; use imageproc::rect::Rect; fn main() { let args: Vec = env::args().collect(); let model_name: &str = &args[1]; let image_name: &str = &args[2]; let img_out_path: &str = &args[3]; let mut file_mod = File::open(model_name).unwrap(); let mut mod_buf = Vec::new(); file_mod.read_to_end(&mut mod_buf).unwrap(); let mut file_img = File::open(image_name).unwrap(); let mut img_buf = Vec::new(); file_img.read_to_end(&mut img_buf).unwrap(); let mut img = image::load_from_memory(&img_buf).unwrap(); let mut flat_img: Vec = Vec::new(); for (_x, _y, rgb) in img.pixels() { flat_img.push(rgb[2] as f32); flat_img.push(rgb[1] as f32); flat_img.push(rgb[0] as f32); } let mut session = TFSession::new(&mod_buf); session .add_input("min_size", &[20.0f32], &[]) .add_input("thresholds", &[0.6f32, 0.7f32, 0.7f32], &[3]) .add_input("factor", &[0.709f32], &[]) .add_input( "input", &flat_img, &[img.height().into(), img.width().into(), 3], ) .add_output("box") .add_output("prob") .run(); let res_vec: Vec = session.get_output("box"); let mut iter = 0; let mut box_vec: Vec<[f32; 4]> = Vec::new(); while (iter * 4) < res_vec.len() { box_vec.push([ res_vec[4 * iter + 1], // x1 res_vec[4 * iter], // y1 res_vec[4 * iter + 3], // x2 res_vec[4 * iter + 2], // y2 ]); iter += 1; } println!("Drawing box: {} results ...", box_vec.len()); let line = Pixel::from_slice(&[0, 255, 0, 0]); for i in 0..box_vec.len() { let xy = box_vec[i]; let x1: i32 = xy[0] as i32; let y1: i32 = xy[1] as i32; let x2: i32 = xy[2] as i32; let y2: i32 = xy[3] as i32; let rect = Rect::at(x1, y1).of_size((x2 - x1) as u32, (y2 - y1) as u32); draw_hollow_rect_mut(&mut img, rect, *line); } img.save(img_out_path).unwrap(); }