feat cube support Array (#1907)

Cargo.lock

@@ -534,6 +534,7 @@ dependencies = [
  "derive-new",
  "half",
  "log",
+ "num-traits",
  "serde",
 ]
 
@@ -1983,6 +1984,15 @@ dependencies = [
  "slab",
 ]
 
+[[package]]
+name = "gelu"
+version = "0.14.0"
+dependencies = [
+ "burn-cube",
+ "burn-cuda",
+ "burn-wgpu",
+]
+
 [[package]]
 name = "gemm"
 version = "0.17.1"

crates/burn-cube/Cargo.toml

@@ -29,5 +29,6 @@ half = { workspace = true, features = ["bytemuck"] }
 serde = { workspace = true }
 burn-cube-macros = { path = "../burn-cube-macros", version = "0.14.0" }
 derive-new = { workspace = true }
+num-traits = { workspace = true }
 
 log = { workspace = true }

crates/burn-cube/src/compute/launcher.rs

@@ -1,9 +1,11 @@
 use crate::compute::{CubeCount, KernelTask};
 use crate::ir::{Elem, FloatKind, IntKind};
+use crate::prelude::ArrayHandle;
 use crate::{calculate_num_elems_dyn_rank, frontend::TensorHandle, Kernel, Runtime};
 use burn_compute::client::ComputeClient;
 use burn_compute::server::Binding;
 use bytemuck::NoUninit;
+use num_traits::ToPrimitive;
 
 /// Prepare a kernel for [launch](KernelLauncher::launch).
 pub struct KernelLauncher<R: Runtime> {
@@ -24,6 +26,11 @@ impl<R: Runtime> KernelLauncher<R> {
         self.tensors.push(tensor);
     }
 
+    /// Register an array to be launched.
+    pub fn register_array(&mut self, array: &ArrayHandle<'_, R>) {
+        self.tensors.push(&array.as_tensor());
+    }
+
     /// Register a u32 scalar to be launched.
     pub fn register_u32(&mut self, scalar: u32) {
         self.register_scalar(Elem::UInt);
@@ -165,17 +172,21 @@ impl<R: Runtime> TensorState<R> {
 
         bindings.push(tensor.handle.clone().binding());
 
-        if metadata.is_empty() {
-            metadata.push(tensor.strides.len() as u32);
-        }
+        let old_rank = if metadata.is_empty() {
+            let rank = tensor.strides.len() as u32;
+            metadata.push(rank);
+            None
+        } else if tensor.strides.len() > metadata[0] as usize {
+            let old_rank = metadata[0];
+            let rank = tensor.strides.len() as u32;
+            Self::adjust_rank(metadata, bindings.len(), rank);
+            Some(old_rank)
+        } else {
+            None
+        };
 
-        for s in tensor.strides.iter() {
-            metadata.push(*s as u32);
-        }
-
-        for s in tensor.shape.iter() {
-            metadata.push(*s as u32);
-        }
+        Self::register_strides(tensor.strides, tensor.shape, old_rank, metadata);
+        Self::register_shape(tensor.shape, old_rank, metadata);
 
         if R::require_array_lengths() {
             let len = calculate_num_elems_dyn_rank(tensor.shape);
@@ -183,6 +194,82 @@ impl<R: Runtime> TensorState<R> {
         }
     }
 
+    fn adjust_rank(metadata: &mut Vec<u32>, num_registered: usize, rank: u32) {
+        let old_rank = metadata[0] as usize;
+        let rank_diff = rank as usize - old_rank;
+        let mut updated_metadata = Vec::with_capacity(2 * rank_diff * num_registered);
+
+        for pos in 0..num_registered {
+            let stride_index = (pos * old_rank * 2) + 1;
+            let shape_index = stride_index + old_rank;
+
+            let strides_old = &metadata[stride_index..stride_index + old_rank];
+            let shape_old = &metadata[shape_index..shape_index + old_rank];
+
+            Self::register_strides(
+                strides_old,
+                shape_old,
+                Some(old_rank as u32),
+                &mut updated_metadata,
+            );
+            Self::register_shape(shape_old, Some(old_rank as u32), &mut updated_metadata);
+        }
+
+        core::mem::swap(&mut updated_metadata, metadata);
+    }
+
+    fn register_strides<T: ToPrimitive>(
+        strides: &[T],
+        shape: &[T],
+        old_rank: Option<u32>,
+        output: &mut Vec<u32>,
+    ) {
+        let old_rank = if let Some(old_rank) = old_rank {
+            let rank = output[0];
+            let rank_diff = old_rank - rank;
+            let padded_strides = if rank_diff > 0 {
+                shape
+                    .iter()
+                    .take(old_rank as usize)
+                    .map(|a| a.to_u32().unwrap())
+                    .sum::<u32>()
+            } else {
+                0
+            };
+
+            for _ in 0..rank_diff {
+                output.push(padded_strides.to_u32().unwrap());
+            }
+
+            old_rank as usize
+        } else {
+            output[0] as usize // same as current.
+        };
+
+        for stride in strides.iter().take(old_rank) {
+            output.push(stride.to_u32().unwrap());
+        }
+    }
+
+    fn register_shape<T: ToPrimitive>(shape: &[T], old_rank: Option<u32>, output: &mut Vec<u32>) {
+        let old_rank = if let Some(old_rank) = old_rank {
+            let rank = output[0];
+            let rank_diff = rank - old_rank;
+
+            for _ in 0..rank_diff {
+                output.push(1);
+            }
+
+            old_rank as usize
+        } else {
+            output[0] as usize // same as current
+        };
+
+        for elem in shape.iter().take(old_rank) {
+            output.push(elem.to_u32().unwrap());
+        }
+    }
+
     fn register(
         self,
         client: &ComputeClient<R::Server, R::Channel>,
@@ -205,6 +292,7 @@ impl<R: Runtime> TensorState<R> {
         }
     }
 }
+
 impl<T: NoUninit> ScalarState<T> {
     /// Add a new scalar value to the state.
     pub fn push(&mut self, val: T) {

crates/burn-cube/src/frontend/element/array.rs

@@ -1,6 +1,13 @@
 use std::marker::PhantomData;
 
-use crate::frontend::{CubeType, ExpandElement};
+use crate::{
+    compute::{KernelBuilder, KernelLauncher},
+    frontend::{CubeType, ExpandElement},
+    ir::{Item, Vectorization},
+    unexpanded, Runtime,
+};
+
+use super::{ArgSettings, CubeElem, LaunchArg, TensorHandle, UInt};
 
 #[derive(new, Clone, Copy)]
 pub struct Array<E> {
@@ -10,3 +17,52 @@ pub struct Array<E> {
 impl<C: CubeType> CubeType for Array<C> {
     type ExpandType = ExpandElement;
 }
+
+impl<E: CubeType> Array<E> {
+    /// Obtain the array length of input
+    pub fn len(self) -> UInt {
+        unexpanded!()
+    }
+}
+
+impl<C: CubeElem> LaunchArg for Array<C> {
+    type RuntimeArg<'a, R: Runtime> = ArrayHandle<'a, R>;
+
+    fn compile_input(builder: &mut KernelBuilder, vectorization: Vectorization) -> ExpandElement {
+        builder.input_array(Item::vectorized(C::as_elem(), vectorization))
+    }
+
+    fn compile_output(builder: &mut KernelBuilder, vectorization: Vectorization) -> ExpandElement {
+        builder.output_array(Item::vectorized(C::as_elem(), vectorization))
+    }
+}
+
+pub struct ArrayHandle<'a, R: Runtime> {
+    pub handle: &'a burn_compute::server::Handle<R::Server>,
+    pub length: [usize; 1],
+}
+
+impl<'a, R: Runtime> ArgSettings<R> for ArrayHandle<'a, R> {
+    fn register(&self, launcher: &mut KernelLauncher<R>) {
+        launcher.register_array(self)
+    }
+}
+
+impl<'a, R: Runtime> ArrayHandle<'a, R> {
+    pub fn new(handle: &'a burn_compute::server::Handle<R::Server>, length: usize) -> Self {
+        Self {
+            handle,
+            length: [length],
+        }
+    }
+
+    pub fn as_tensor(&self) -> TensorHandle<'_, R> {
+        let shape = &self.length;
+
+        TensorHandle {
+            handle: self.handle,
+            strides: &[1],
+            shape,
+        }
+    }
+}

crates/burn-cube/src/prelude.rs

@@ -9,7 +9,9 @@ pub use crate::ir::{CubeDim, KernelDefinition};
 pub use crate::runtime::Runtime;
 
 /// Elements
-pub use crate::frontend::{Float, LaunchArg, Tensor, TensorHandle, UInt, F16, F32, F64, I32, I64};
+pub use crate::frontend::{
+    Array, ArrayHandle, Float, LaunchArg, Tensor, TensorHandle, UInt, F16, F32, F64, I32, I64,
+};
 pub use crate::pod::CubeElement;
 
 /// Topology

crates/burn-cuda/src/lib.rs

@@ -10,7 +10,7 @@ pub mod compiler;
 pub use device::*;
 
 use burn_jit::JitBackend;
-use runtime::CudaRuntime;
+pub use runtime::CudaRuntime;
 
 #[cfg(not(feature = "fusion"))]
 pub type Cuda<F = f32, I = i32> = JitBackend<CudaRuntime, F, I>;

examples/custom-csv-dataset/Cargo.toml

@@ -18,4 +18,4 @@ reqwest = {workspace = true, features = ["blocking"]}
 
 # CSV parsing
 csv = {workspace = true}
-serde = {workspace = true, features = ["std", "derive"]}
+serde = {workspace = true, features = ["std", "derive"]}

examples/gelu/Cargo.toml

@@ -0,0 +1,17 @@
+[package]
+authors = []
+name = "gelu"
+publish = false
+edition.workspace = true
+license.workspace = true
+version.workspace = true
+
+[features]
+default = ["wgpu"]
+cuda = ["burn-cuda"]
+wgpu = ["burn-wgpu"]
+
+[dependencies]
+burn-cube = { path = "../../crates/burn-cube", version = "0.14.0" }
+burn-cuda = { path = "../../crates/burn-cuda", version = "0.14.0", optional = true }
+burn-wgpu = { path = "../../crates/burn-wgpu", version = "0.14.0", optional = true }

examples/gelu/examples/gelu.rs

@@ -0,0 +1,6 @@
+fn main() {
+    #[cfg(feature = "cuda")]
+    gelu::launch::<burn_cuda::CudaRuntime>(&Default::default());
+    #[cfg(feature = "wgpu")]
+    gelu::launch::<burn_wgpu::WgpuRuntime>(&Default::default());
+}

examples/gelu/src/lib.rs

@@ -0,0 +1,36 @@
+use burn_cube::prelude::*;
+
+#[cube(launch)]
+fn gelu<F: Float>(input: Array<F>, mut output: Array<F>) {
+    if ABSOLUTE_POS < input.len() {
+        output[ABSOLUTE_POS] = gelu_scalar::<F>(input[ABSOLUTE_POS]);
+    }
+}
+
+#[cube]
+fn gelu_scalar<F: Float>(x: F) -> F {
+    x * (F::new(1.0) + F::erf(x / F::sqrt(F::new(2.0)))) / F::new(2.0)
+}
+
+pub fn launch<R: Runtime>(device: &R::Device) {
+    let client = R::client(device);
+    println!("Executing gelu with runtime {:?}", R::name());
+
+    let input = &[-1., 0., 1., 5.];
+    let input_handle = client.create(f32::as_bytes(input));
+    let output_handle = client.empty(input.len() * core::mem::size_of::<f32>());
+
+    gelu_launch::<F32, R>(
+        client.clone(),
+        CubeCount::new(1, 1, 1),
+        KernelSettings::default(),
+        ArrayHandle::new(&input_handle, input.len()),
+        ArrayHandle::new(&output_handle, input.len()),
+    );
+
+    let output = client.read(output_handle.binding()).read_sync().unwrap();
+    let output = f32::from_bytes(&output);
+
+    // Should be [-0.1587,  0.0000,  0.8413,  5.0000]
+    println!("{output:?}");
+}