Try out juice crate; ignore __pycache__

.gitignore

@@ -1,5 +1,7 @@
 # Python virtual environment:
 .venv/
+# Python cache:
+__pycache__/
 # IDE project settings:
 .vscode/
 .idea/

py/feed_forward_tf.py

@@ -11,6 +11,8 @@ from tensorflow.python.ops.gen_math_ops import mat_mul, sigmoid
 from tensorflow.python.ops.gen_nn_ops import bias_add
 from tensorflow.python.ops.resource_variable_ops import ResourceVariable
 from keras.engine.keras_tensor import KerasTensor
+from keras.api._v2.keras.callbacks import Callback
+from keras.api._v2.keras.constraints import Constraint
 from keras.api._v2.keras.layers import Dense, Input
 from keras.api._v2.keras.models import Model
 
@@ -41,7 +43,16 @@ def simple_layer_test() -> None:
     b_data = np.array([-1.0, 2.0], dtype=F32)
     w_init = partial(init_params, w_data.T)
     b_init = partial(init_params, b_data.T)
-    layer = Dense(units=2, activation='sigmoid', kernel_initializer=w_init, bias_initializer=b_init)(inputs)
+
+    class Const(Constraint):
+        def __init__(self, zero_mask: np.ndarray) -> None:
+            self.mask = zero_mask
+
+        def __call__(self, weights: ResourceVariable) -> ResourceVariable:
+            weights.assign(weights - self.mask * weights)
+            return weights
+
+    layer = Dense(units=2, activation='sigmoid', kernel_initializer=w_init, bias_initializer=b_init, kernel_constraint=Const(w_data.T == 0))(inputs)
     assert isinstance(layer, KerasTensor)
     model = Model(inputs=inputs, outputs=layer)
     w_tensor = model.trainable_variables[0]
@@ -50,13 +61,21 @@ def simple_layer_test() -> None:
     assert isinstance(b_tensor, ResourceVariable)
     assert np.equal(w_tensor.numpy().T, w_data).all()
     assert np.equal(b_tensor.numpy().T, b_data).all()
-    model.compile()
+    model.compile(optimizer='adam', loss='categorical_crossentropy')
     x = np.array([[1.0, -2.0, 0.2]], dtype=F32)
     print("input", x[0])
     y = model(x)
     assert isinstance(y, tf.Tensor)
     print("output", np.array(y)[0])
     assert y[0][1] == 0.5
+    samples = np.array([[1., 1., 1.], [2., 2., 2.], [3., 3., 3.]], dtype=F32)
+    labels = np.array([[0., 1.], [0., 2.], [3., 0.]], dtype=F32)
+
+    class CB(Callback):
+        def on_train_batch_begin(self, batch, logs=None):
+            print(f"...start of batch {batch}; model weights:")
+            print(self.model.trainable_variables[0].numpy())
+    model.fit(samples, labels, batch_size=1, callbacks=[CB()], verbose=0)
 
 
 def build_model(input_shape: Sequence[int], *layers: tuple[np.ndarray, np.ndarray]) -> Model:
@@ -127,5 +146,5 @@ def main(n: int) -> None:
 
 
 if __name__ == '__main__':
-    # simple_layer_test()
-    main(int(sys.argv[1]))
+    simple_layer_test()
+    # main(int(sys.argv[1]))

rs/Cargo.toml

@@ -1,5 +1,5 @@
 [package]
-name = "feed_forward"
+name = "feed_forward_ndarray"
 version = "0.1.0"
 edition = "2021"
 

rs_juice/Cargo.toml

@@ -0,0 +1,16 @@
+[package]
+name = "feed_forward_juice"
+version = "0.1.0"
+edition = "2021"
+
+[[bin]]
+name = "feed_forward"
+path = "src/feed_forward.rs"
+
+[dependencies]
+juice = { version = "0.3", features = ["cuda"] }
+coaster = { version = "0.2.0" }
+coaster-blas = { version = "0.4.0" }
+coaster-nn = { version = "0.5.0" }
+csv = "*"
+glob = "*"

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>());
+}

rs_juice/src/feed_forward.rs

@@ -0,0 +1,39 @@
+#![allow(dead_code)]
+
+// use std::rc::Rc;
+// use std::sync::{Arc, RwLock};
+//
+// use coaster::backend::Backend;
+// use coaster::IFramework;
+// use coaster::frameworks::cuda::{Cuda, get_cuda_backend};
+// use coaster::frameworks::native::{Cpu, Native};
+// use coaster::tensor::SharedTensor;
+// use juice::layer::{LayerConfig, Layer, ComputeOutput, ILayer};
+// use juice::layers::{LinearConfig, SequentialConfig, Sequential};
+// use juice::util::ArcLock;
+
+// mod drafts;
+// use drafts::write_to_memory;
+mod simple;
+
+
+fn main() {
+    simple::test_simple();
+    // let backend: Rc<Backend<Cuda>> = Rc::new(get_cuda_backend());
+    // let native: Native = Native::new();
+    // let cpu: Cpu = native.new_device(native.hardwares()).unwrap();
+    //
+    // let mut net_cfg: SequentialConfig = SequentialConfig::default();
+    // net_cfg.add_input("data", &vec![1, 3, 1]);
+    // net_cfg.add_layer(
+    //     LayerConfig::new("linear1", LinearConfig { output_size: 2 })
+    // );
+    // let mut net = Sequential::from_config(backend.clone(), &net_cfg);
+    //
+    // let mut x: SharedTensor<f32> = SharedTensor::<f32>::new(&(3, 1));
+    // let x_values: &Vec<f32> = &vec![1f32, -2.0,  0.2];
+    // write_to_memory(x.write_only(&cpu).unwrap(), x_values);
+    // let x_lock: ArcLock<SharedTensor<f32>> = Arc::new(RwLock::new(x));
+    //
+    // net.forward(&backend, &[x_lock],)
+}

rs_juice/src/simple.rs

@@ -0,0 +1,43 @@
+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_blas::plugin::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_simple() {
+    let backend: Backend<Cuda> = get_cuda_backend();
+    let native: Native = Native::new();
+    let cpu: Cpu = native.new_device(native.hardwares()).unwrap();
+
+    let mut x: SharedTensor<f32> = SharedTensor::<f32>::new(&(2, 1));
+    let mut w: SharedTensor<f32> = SharedTensor::<f32>::new(&(2, 2));
+    let mut wx: SharedTensor<f32> = SharedTensor::<f32>::new(&(2, 1));
+
+    let x_values: &Vec<f32> = &vec![1f32, 2.0];
+    let w_values: &Vec<f32> = &vec![1f32,  1.0,
+                                     3.0, -1.0];
+
+    println!("Data:");
+    println!("x = {:?}", x_values);
+    println!("w = [{:?},\n     {:?}]", &w_values[..2], &w_values[2..]);
+    println!("Expected result:");
+    println!("w*x = [3.0, 1.0]");
+
+    write_to_memory(x.write_only(&cpu).unwrap(), x_values);
+    write_to_memory(w.write_only(&cpu).unwrap(), w_values);
+
+    backend.dot(&w, &x, &mut wx).unwrap();
+    println!("Actual result:");
+    println!("w*x = {:?}", wx.read(&cpu).unwrap().as_slice::<f32>());
+}