Add some preliminary ONNX support (#1260)

* Add the onnx protos.

* Move the reading bits.

* Install protoc on the CI.

* Install protoc on the cuda CI too.

* Use clap for the onnx tool.

* Tweak the CI protoc install.

* Add some simple evalution function.

* Add some binary operator support.

candle-onnx/examples/onnx_basics.rs

@@ -0,0 +1,56 @@
+use anyhow::Result;
+use candle::{Device, Tensor};
+
+use clap::{Parser, Subcommand};
+
+#[derive(Subcommand, Debug, Clone)]
+enum Command {
+    Print {
+        #[arg(long)]
+        file: String,
+    },
+    SimpleEval {
+        #[arg(long)]
+        file: String,
+    },
+}
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+pub struct Args {
+    #[command(subcommand)]
+    command: Command,
+}
+
+pub fn main() -> Result<()> {
+    let args = Args::parse();
+    match args.command {
+        Command::Print { file } => {
+            let model = candle_onnx::read_file(file)?;
+            println!("{model:?}");
+            let graph = model.graph.unwrap();
+            for node in graph.node.iter() {
+                println!("{node:?}");
+            }
+        }
+        Command::SimpleEval { file } => {
+            let model = candle_onnx::read_file(file)?;
+            let inputs = model
+                .graph
+                .as_ref()
+                .unwrap()
+                .input
+                .iter()
+                .map(|name| {
+                    let value = Tensor::new(&[-3.2, 2.7], &Device::Cpu)?;
+                    Ok((name.name.clone(), value))
+                })
+                .collect::<Result<_>>()?;
+            let outputs = candle_onnx::simple_eval(&model, inputs)?;
+            for (name, value) in outputs.iter() {
+                println!("{name}: {value:?}")
+            }
+        }
+    }
+    Ok(())
+}