Add pytorch backend, tests, examples

crates/test-programs/src/bin/nn_wit_image_classification_pytorch.rs

@@ -0,0 +1,37 @@
+use anyhow::{Context, Result};
+use std::fs;
+use test_programs::nn::{sort_results, wit};
+
+pub fn main() -> Result<()> {
+    let model = fs::read("fixture/model.pt")
+        .context("the model file to be mapped to the fixture directory")?;
+    let graph = wit::load(
+        &[model],
+        wit::GraphEncoding::Pytorch,
+        wit::ExecutionTarget::Cpu,
+    )?;
+    let tensor = fs::read("fixture/kitten.tensor")
+        .context("the tensor file to be mapped to the fixture directory")?;
+    let output_buffer = wit::classify(graph, ("input", tensor), "output")?;
+    let result = softmax(output_buffer);
+    let top_five = &sort_results(&result)[..5];
+    assert_eq!(top_five[0].class_id(), 281);
+    println!("found results, sorted top 5: {top_five:?}");
+    Ok(())
+}
+
+fn softmax(output_tensor: Vec<f32>) -> Vec<f32> {
+    let max_val = output_tensor
+        .iter()
+        .cloned()
+        .fold(f32::NEG_INFINITY, f32::max);
+
+    // Compute the exponential of each element subtracted by max_val for numerical stability.
+    let exps: Vec<f32> = output_tensor.iter().map(|&x| (x - max_val).exp()).collect();
+
+    // Compute the sum of the exponentials.
+    let sum_exps: f32 = exps.iter().sum();
+
+    // Normalize each element to get the probabilities.
+    exps.iter().map(|&exp| exp / sum_exps).collect()
+}

crates/test-programs/src/bin/nn_witx_image_classification_pytorch.rs

@@ -0,0 +1,37 @@
+use anyhow::{Context, Result};
+use std::fs;
+use test_programs::nn::{sort_results, witx};
+
+pub fn main() -> Result<()> {
+    let model = fs::read("fixture/model.pt")
+        .context("the model file to be mapped to the fixture directory")?;
+    let graph = witx::load(
+        &[&model],
+        witx::GraphEncoding::Pytorch,
+        witx::ExecutionTarget::CPU,
+    )?;
+    let tensor = fs::read("fixture/kitten.tensor")
+        .context("the tensor file to be mapped to the fixture directory")?;
+    let output_buffer = witx::classify(graph, tensor)?;
+    let result = softmax(output_buffer);
+    let top_five = &sort_results(&result)[..5];
+    assert_eq!(top_five[0].class_id(), 281);
+    println!("found results, sorted top 5: {top_five:?}");
+    Ok(())
+}
+
+fn softmax(output_tensor: Vec<f32>) -> Vec<f32> {
+    let max_val = output_tensor
+        .iter()
+        .cloned()
+        .fold(f32::NEG_INFINITY, f32::max);
+
+    // Compute the exponential of each element subtracted by max_val for numerical stability.
+    let exps: Vec<f32> = output_tensor.iter().map(|&x| (x - max_val).exp()).collect();
+
+    // Compute the sum of the exponentials.
+    let sum_exps: f32 = exps.iter().sum();
+
+    // Normalize each element to get the probabilities.
+    exps.iter().map(|&exp| exp / sum_exps).collect()
+}

crates/wasi-nn/Cargo.toml

@@ -38,6 +38,7 @@ ort = { version = "2.0.0-rc.2", default-features = false, features = [
     "copy-dylibs",
     "download-binaries",
 ], optional = true }
+tch = { version = "0.17.0", default-features = false, optional = true}
 
 [target.'cfg(windows)'.dependencies.windows]
 version = "0.52"
@@ -69,7 +70,9 @@ openvino = ["dep:openvino"]
 onnx = ["dep:ort"]
 # WinML is only available on Windows 10 1809 and later.
 winml = ["dep:windows"]
+# PyTorch is available on all platforms; requires Libtorch to be installed
+pytorch = ["dep:tch"]
 
 [[test]]
 name = "test-programs"
-harness = false
+harness = false

crates/wasi-nn/examples/classification-example-pytorch/Cargo.toml

@@ -0,0 +1,17 @@
+[package]
+name = "wasi-nn-example-pytorch"
+version = "0.0.0"
+edition = "2021"
+publish = false
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+wasi-nn = "0.6.0"
+anyhow = "1.0.86"
+image = { version = "0.25.2", default-features = false, features = ["png"] }
+
+# This crate is built with the wasm32-wasip1 target, so it's separate
+# from the main Wasmtime build, so use this directive to exclude it
+# from the parent directory's workspace.
+[workspace]

crates/wasi-nn/examples/classification-example-pytorch/README.md

@@ -0,0 +1,15 @@
+This example project demonstrates using the `wasi-nn` API to perform PyTorch based inference. It consists of Rust code that is built using the `wasm32-wasip1` target.
+
+To run this example: 
+1. Ensure you set appropriate Libtorch enviornment variables according to [tch-rs instructions]( https://github.com/LaurentMazare/tch-rs?tab=readme-ov-file#libtorch-manual-install). 
+    - Requires the C++ PyTorch library (libtorch) in version *v2.4.0* to be available on
+your system. 
+    - `export LIBTORCH=/path/to/libtorch`
+2. Build Wasmtime  with `wasmtime-wasi-nn/pytorch` feature.
+3. Navigate to this example directory `crates/wasi-nn/examples/classification-example-pytorch`.
+4. Build this example `cargo build --target=wasm32-wasip1`.
+5. Run the generated wasm file with wasmtime after mapping the directory containing Resnet18 `model.pt` and sample image `kitten.png`
+    ```
+    ${Wasmtime_root_dir}/target/debug/wasmtime -S nn --dir ${Wasmtime_root_dir}/crates/wasi-nn/examples/classification-example-pytorch::. ${Wasmtime_root_dir}/crates/wasi-nn/examples/classification-example-pytorch/target/wasm32-wasip1/debug/wasi-nn-example-pytorch.wasm
+    ```
+6. Check that result `281` has highest probability, which corresponds to `tabby cat`.