[Example] Update pytorch example

.github/workflows/pytorch.yml

@@ -13,12 +13,12 @@ on:
     branches: [ '*' ]
     paths:
       - ".github/workflows/pytorch.yml"
-      - "pytorch-mobilenet-image/**"
+      - "pytorch-resnet18-image/**"
   pull_request:
     branches: [ '*' ]
     paths:
       - ".github/workflows/pytorch.yml"
-      - "pytorch-mobilenet-image/**"
+      - "pytorch-resnet18-image/**"
 
 jobs:
   build:
@@ -41,25 +41,27 @@ jobs:
 
     - name: Install WasmEdge + WASI-NN + PyTorch
       run: |
-        VERSION=0.13.4
+        VERSION=0.14.1
         curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | sudo 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-${PYTORCH_VERSION}%2Bcpu.zip
+        export PYTORCH_VERSION="2.4.1"
+        # For AOTI example, use the libtorch with cxx11 abi.
+        export PYTORCH_ABI="libtorch"
+        curl -s -L -O --remote-name-all https://download.pytorch.org/libtorch/cpu/${PYTORCH_LINK}-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
+        cd pytorch-resnet18-image/rust
         cargo build --target wasm32-wasi --release
         cd ..
-        wasmedge compile rust/target/wasm32-wasi/release/wasmedge-wasinn-example-mobilenet-image.wasm wasmedge-wasinn-example-mobilenet-image-aot.wasm
-        wasmedge compile rust/target/wasm32-wasi/release/wasmedge-wasinn-example-mobilenet-image-named-model.wasm wasmedge-wasinn-example-mobilenet-image-named-model-aot.wasm
+        wasmedge compile rust/target/wasm32-wasi/release/wasmedge-wasinn-example-resnet18-image.wasm wasmedge-wasinn-example-resnet18-image-aot.wasm
+        wasmedge compile rust/target/wasm32-wasi/release/wasmedge-wasinn-example-resnet18-image-named-model.wasm wasmedge-wasinn-example-resnet18-image-named-model-aot.wasm
         echo "Run without named model"
-        wasmedge --dir .:. wasmedge-wasinn-example-mobilenet-image-aot.wasm mobilenet.pt input.jpg
+        wasmedge --dir .:. wasmedge-wasinn-example-resnet18-image-aot.wasm resnet18.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
+        wasmedge --dir .:. --nn-preload demo:PyTorch:CPU:resnet18.pt wasmedge-wasinn-example-resnet18-image-named-model-aot.wasm demo input.jpg
+        echo "Run with AOTI"
+        wasmedge --dir .:. --nn-preload demo:PyTorchAOTI:CPU:$(pwd)/resnet18_pt2.so wasmedge-wasinn-example-resnet18-image-named-model.wasm demo input.jpg

pytorch-resnet18-image/README.md

@@ -0,0 +1,144 @@
+# Resnet18 Example For WASI-NN with PyTorch Backend
+
+This package is a high-level Rust bindings for [wasi-nn] example of Resnet18 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-wasi --release
+```
+
+Because here, we will demonstrate two ways of using wasi-nn. So the output WASM files will be at [`target/wasm32-wasi/release/wasmedge-wasinn-example-resnet18-image.wasm`](wasmedge-wasinn-example-resnet18-image.wasm) and [`target/wasm32-wasi/release/wasmedge-wasinn-example-resnet18-image-named-model.wasm`](wasmedge-wasinn-example-resnet18-image-named-model.wasm).
+To speed up the image processing, we can enable the AOT mode in WasmEdge with:
+
+```bash
+wasmedgec rust/target/wasm32-wasi/release/wasmedge-wasinn-example-resnet18-image.wasm wasmedge-wasinn-example-resnet18-image-aot.wasm
+
+wasmedgec rust/target/wasm32-wasi/release/wasmedge-wasinn-example-resnet18-image-named-model.wasm wasmedge-wasinn-example-resnet18-image-named-model-aot.wasm
+```
+
+## Run
+
+### Generate Model
+
+First generate the fixture of the pre-trained mobilenet with the script:
+
+```bash
+pip3 install torch==2.4.1 numpy pillow --extra-index-url https://download.pytorch.org/whl/lts/1.8/cpu
+# generate the model fixture
+python3 gen_resnet18_model.py
+```
+
+(Or you can use the pre-generated one at [`resnet18.pt`](resnet18.pt))
+
+### Test Image
+
+The testing image `input.jpg` is downloaded from <https://github.com/bytecodealliance/wasi-nn/raw/main/rust/examples/images/1.jpg> 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-resnet18-image.wasm resnet18.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](18.0458)banana
+   2.) [940](15.6954)spaghetti squash
+   3.) [951](14.1337)lemon
+   4.) [942](13.2925)butternut squash
+   5.) [941](10.6792)acorn squash
+```
+
+- 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 <name>:<encoding>:<target>:<model_path>
+
+```bash
+wasmedge --dir .:. --nn-preload demo:PyTorch:CPU:resnet18.pt wasmedge-wasinn-example-resnet18-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](18.0458)banana
+   2.) [940](15.6954)spaghetti squash
+   3.) [951](14.1337)lemon
+   4.) [942](13.2925)butternut squash
+   5.) [941](10.6792)acorn squash
+```
+
+## Run from AOTInductor
+
+### Generate Model
+PyTorch backend also support load from the AOTInductor (Shared Library). To compile the pytorch model, please follow the Pytorch official tutorial.
+
+* https://pytorch.org/tutorials/recipes/torch_export_aoti_python.html
+
+
+Or you can use the pre-generated one at [`resnet18_pt2.so`](resnet18_pt2.so). However it may not suitable for your machine. it is suggested to use [`gen_resnet18_aoti`](gen_resnet18_aoti) recompile the model.
+
+> Notice: The AOTInductor from pip will use old c++ abi interface, it is maybe incompatible with wasmedge release, you may need to install the libtorch **without c++11 abi** and rebuild the wasmedge with `-DWASMEDGE_USE_CXX11_ABI=OFF`.
+
+
+```bash
+## Build Wasmedge with cmake example
+cmake -Bbuild -GNinja -DWASMEDGE_USE_CXX11_ABI=OFF -DWASMEDGE_PLUGIN_WASI_NN_BACKEND=PyTorch .
+```
+
+### Execute
+
+To run the AOT Inductor, you need use `--nn-preload` with `PyTorchAOTI` interface and specify absolute path to load the shared library.
+
+```bash
+export LD_LIBRARY_PATH=/path_to_libtorch/lib
+./wasmedge --dir .:. --nn-preload demo:PyTorchAOTI:CPU:/absolute_path_model/resnet18_pt2.so wasmedge-wasinn-example-resnet18-image-named-model.wasm demo input.jpg
+```
+
+```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](18.0458)banana
+   2.) [940](15.6954)spaghetti squash
+   3.) [951](14.1337)lemon
+   4.) [942](13.2925)butternut squash
+   5.) [941](10.6792)acorn squash
+```

pytorch-resnet18-image/gen_resnet18_aoti.py

@@ -0,0 +1,39 @@
+# More detail please follow the link below
+# https://pytorch.org/tutorials/recipes/torch_export_aoti_python.html
+
+import os
+import torch
+from torchvision.models import ResNet18_Weights, resnet18
+
+model = resnet18(weights=ResNet18_Weights.DEFAULT)
+model.eval()
+
+with torch.inference_mode():
+
+    # Specify the generated shared library path
+    aot_compile_options = {
+            "aot_inductor.output_path": os.path.join(os.getcwd(), "resnet18_pt2.so"),
+    }
+    if torch.cuda.is_available():
+        device = "cuda"
+        aot_compile_options.update({"max_autotune": True})
+    else:
+        device = "cpu"
+
+    model = model.to(device=device)
+    example_inputs = (torch.randn(2, 3, 224, 224, device=device),)
+
+    # min=2 is not a bug and is explained in the 0/1 Specialization Problem
+    batch_dim = torch.export.Dim("batch", min=2, max=32)
+    exported_program = torch.export.export(
+        model,
+        example_inputs,
+        # Specify the first dimension of the input x as dynamic
+        dynamic_shapes={"x": {0: batch_dim}},
+    )
+    so_path = torch._inductor.aot_compile(
+        exported_program.module(),
+        example_inputs,
+        # Specify the generated shared library path
+        options=aot_compile_options
+    )

pytorch-mobilenet-image/gen_mobilenet_model.py → pytorch-resnet18-image/gen_resnet18_model.py

@@ -4,15 +4,14 @@
 
 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 = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', 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")
+    if not os.path.exists("resnet18.pt"):
+        sm.save("resnet18.pt")
+    load_sm = jit.load("resnet18.pt")
     out2 = load_sm(fake_input).squeeze()
 
     print(out1[:5], out2[:5])

pytorch-resnet18-image/run.py

@@ -0,0 +1,12 @@
+import os
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_so_path = os.path.join(os.getcwd(), "resnet18_pt2.so")
+
+model = torch._export.aot_load(model_so_path, device)
+example_inputs = (torch.randn(1, 3, 224, 224, device=device),)
+
+with torch.inference_mode():
+    output = model(example_inputs)
+    print(output)