Try out juice crate; ignore __pycache__

rs_juice/src/drafts.rs

@@ -0,0 +1,69 @@
+// use std::iter::repeat;
+
+use coaster::backend::Backend;
+use coaster::IFramework;
+use coaster::frameworks::cuda::{Cuda, get_cuda_backend};
+use coaster::frameworks::native::{Cpu, Native};
+use coaster::frameworks::native::flatbox::FlatBox;
+use coaster::tensor::SharedTensor;
+use coaster_nn::Sigmoid;
+use coaster_blas::plugin::{Asum, Dot};
+
+
+pub fn write_to_memory<T: Copy>(mem: &mut FlatBox, data: &[T]) {
+    let mem_buffer: &mut[T] = mem.as_mut_slice::<T>();
+    for (index, datum) in data.iter().enumerate() {
+        mem_buffer[index] = *datum;
+    }
+}
+
+
+pub fn test_layer() {
+    let backend: Backend<Cuda> = get_cuda_backend();
+    let mut x: SharedTensor<f32> = SharedTensor::<f32>::new(&(3, 1));
+    let mut w: SharedTensor<f32> = SharedTensor::<f32>::new(&(2, 3));
+    let mut b: SharedTensor<f32> = SharedTensor::<f32>::new(&(2, 1));
+    let mut result1: SharedTensor<f32> = SharedTensor::<f32>::new(&(2, 1));
+    let mut result2: SharedTensor<f32> = SharedTensor::<f32>::new(&(2, 1));
+
+    // let x_values: &Vec<f32> = &repeat(0f32).take(x.capacity()).collect::<Vec<f32>>();
+    let x_values: &Vec<f32> = &vec![1f32, -2.0,  0.2];
+    let w_values: &Vec<f32> = &vec![1f32, -0.5,  0.0,
+                                     3.0,  2.0, -5.0];
+    let b_values: &Vec<f32> = &vec![-1f32,
+                                      2.0];
+    println!("x = {:?}", x_values);
+    println!("w = [{:?},\n     {:?}]", &w_values[..3], &w_values[3..]);
+    println!("b = {:?}", b_values);
+    println!("w*x+b = [1.0, 0.0]");
+
+    let native: Native = Native::new();
+    let cpu: Cpu = native.new_device(native.hardwares()).unwrap();
+    write_to_memory(x.write_only(&cpu).unwrap(), x_values);
+    write_to_memory(w.write_only(&cpu).unwrap(), w_values);
+    write_to_memory(b.write_only(&cpu).unwrap(), b_values);
+
+    println!("Computing layer...");
+    backend.dot(&w, &x, &mut result1).unwrap();
+    println!("x = {:?}", x.read(&cpu).unwrap().as_slice::<f32>());
+    println!("w = {:?}", w.read(&cpu).unwrap().as_slice::<f32>());
+    println!("w*x = {:?}", result1.read(&cpu).unwrap().as_slice::<f32>());
+    // backend.sigmoid(&result1, &mut result2).unwrap();
+    // println!("y = {:?}", result2.read(&cpu).unwrap().as_slice::<f32>());
+}
+
+pub fn test_example() {
+    let backend: Backend<Cuda> = get_cuda_backend();
+    // Initialize two SharedTensors.
+    let mut x = SharedTensor::<f32>::new(&(1, 1, 3));
+    let mut result = SharedTensor::<f32>::new(&(1, 1, 3));
+    // Fill `x` with some data.
+    let payload: &[f32] = &::std::iter::repeat(1f32).take(x.capacity()).collect::<Vec<f32>>();
+    let native = Native::new();
+    let cpu = native.new_device(native.hardwares()).unwrap();
+    write_to_memory(x.write_only(&cpu).unwrap(), payload); // Write to native host memory.
+    // Run the sigmoid operation, provided by the NN Plugin, on your CUDA enabled GPU.
+    backend.sigmoid(&mut x, &mut result).unwrap();
+    // See the result.
+    println!("{:?}", result.read(&cpu).unwrap().as_slice::<f32>());
+}