Gather CPA to CubeCL (#2165)

* working version

* cleanup

* wip

* working version of gather

* testsetsetser

* Revert "testsetsetser"

This reverts commit f37b329697.

* Reapply "testsetsetser"

This reverts commit f8ada0044e.

* Revert "testsetsetser"

This reverts commit f37b329697.

* Revert "working version of gather"

This reverts commit f5047c27c8.

* Revert "wip"

This reverts commit abaaa2dd55.

* Revert "Merge branch 'main' into index-cpa-to-cubecl"

This reverts commit 05bed8ea74, reversing
changes made to 94954fc32c.

* Revert "cleanup"

This reverts commit 94954fc32c.

* Revert "working version"

This reverts commit a06933f029.

* gather test

* fix

* fix clippy

* cleanup

crates/burn-jit/src/kernel/index/gather.rs

@@ -1,128 +1,46 @@
 use crate::{
     element::JitElement, kernel::Kernel, ops::numeric::empty_device, tensor::JitTensor, JitRuntime,
 };
-use cubecl::ir::{
+use cubecl::frontend::{Numeric, Tensor, UInt, ABSOLUTE_POS};
-    Elem, IndexOffsetGlobalWithLayout, IntKind, Item, KernelDefinition, Scope, Variable, Visibility,
+use cubecl::linalg::tensor::index_offset_with_layout;
-};
+use cubecl::CubeDim;
-use cubecl::{
+use cubecl::{calculate_cube_count_elemwise, prelude::*};
-    cpa, CubeCountSettings, Execution, InputInfo, KernelExpansion, KernelIntegrator,
-    KernelSettings, OutputInfo,
-};
-use std::marker::PhantomData;
 
-#[derive(new)]
+#[cube(launch_unchecked)]
-struct GatherEagerKernel<R: JitRuntime, E: JitElement> {
+fn gather_kernel<T: Numeric, I: Numeric>(
-    dim: usize,
+    input: &Tensor<T>,
-    _runtime: PhantomData<R>,
+    indices: &Tensor<I>,
-    _elem: PhantomData<E>,
+    output: &mut Tensor<T>,
+    dim: &UInt,
+) {
+    let index = indices[ABSOLUTE_POS];
+
+    let stride = input.stride(*dim);
+    let mut offset = UInt::cast_from(index);
+    offset *= stride;
+
+    if *dim > 0 {
+        let offset_before = index_offset_with_layout(
+            input,
+            output,
+            ABSOLUTE_POS,
+            UInt::new(0),
+            *dim,
+            Comptime::new(false),
+        );
+        offset += offset_before;
     }
 
-struct GatherComputeShader {
+    let offset_after = index_offset_with_layout(
-    tensor: Variable,
+        input,
-    indices: Variable,
+        output,
-    out: Variable,
+        ABSOLUTE_POS,
-    dim: usize,
+        *dim + 1,
-}
+        input.rank(),
-
+        Comptime::new(false),
-impl GatherComputeShader {
+    );
-    pub fn expand(self, scope: &mut Scope) {
+    offset += offset_after;
-        match self.tensor {
+    output[ABSOLUTE_POS] = input[offset];
-            Variable::GlobalInputArray { .. } => (),
-            Variable::GlobalOutputArray { .. } => (),
-            _ => panic!("Tensor variable must be an global array."),
-        };
-
-        let tensor = self.tensor;
-        let output = self.out;
-
-        let stride = scope.create_local(Elem::UInt);
-        let offset = scope.create_local(Elem::UInt);
-
-        // The offset of the `dim` dimension is obtained by the indices tensor.
-        cpa!(scope, offset = cast(self.indices));
-        cpa!(scope, stride = stride(tensor, self.dim));
-        cpa!(scope, offset = offset * stride);
-
-        // We fetch the offset before the `dim` dimension.
-        if self.dim > 0 {
-            let offset_before = scope.create_local(Elem::UInt);
-            scope.index_offset_with_output_layout(IndexOffsetGlobalWithLayout {
-                tensors: vec![tensor],
-                indexes: vec![offset_before],
-                layout: Variable::AbsolutePos, // Will be updated.
-                position: Variable::AbsolutePos,
-                dim_start: 0u32.into(),
-                dim_end: self.dim.into(),
-            });
-            cpa!(scope, offset += offset_before);
-        }
-
-        let offset_after = scope.create_local(Elem::UInt);
-        scope.index_offset_with_output_layout(IndexOffsetGlobalWithLayout {
-            tensors: vec![tensor],
-            indexes: vec![offset_after],
-            layout: Variable::AbsolutePos, // Will be updated.
-            position: Variable::AbsolutePos,
-            dim_start: (self.dim + 1).into(),
-            dim_end: Variable::Rank,
-        });
-        cpa!(scope, offset += offset_after);
-
-        cpa!(scope, output = tensor[offset]);
-    }
-}
-
-impl<R: JitRuntime, E: JitElement> Kernel for GatherEagerKernel<R, E> {
-    fn define(&self) -> KernelDefinition {
-        let mut scope = Scope::root();
-        let item_tensor = E::cube_elem().into();
-        let item_indices: Item = Elem::Int(IntKind::I32).into();
-
-        let tensor = Variable::GlobalInputArray {
-            id: 0,
-            item: item_tensor,
-        };
-        let indices = scope.read_array(1, item_indices, Variable::AbsolutePos);
-
-        let output_array = Variable::GlobalOutputArray {
-            id: 0,
-            item: item_tensor,
-        };
-        let output_local = scope.create_local(item_tensor);
-
-        GatherComputeShader {
-            tensor,
-            indices,
-            out: output_local,
-            dim: self.dim,
-        }
-        .expand(&mut scope);
-
-        scope.write_global(output_local, output_array, Variable::AbsolutePos);
-
-        let tensor = InputInfo::Array {
-            item: item_tensor,
-            visibility: Visibility::Read,
-        };
-        let indices = InputInfo::Array {
-            item: Elem::Int(IntKind::I32).into(),
-            visibility: Visibility::Read,
-        };
-        let out = OutputInfo::Array { item: item_tensor };
-
-        let info = KernelExpansion {
-            inputs: vec![tensor, indices],
-            outputs: vec![out],
-            scope,
-        };
-
-        let settings = KernelSettings::default();
-        KernelIntegrator::new(info).integrate(settings)
-    }
-
-    fn id(&self) -> cubecl::KernelId {
-        cubecl::KernelId::new::<Self>().info(self.dim)
-    }
 }
 
 pub(crate) fn gather<R: JitRuntime, E: JitElement, I: JitElement, const D: usize>(
@@ -131,13 +49,21 @@ pub(crate) fn gather<R: JitRuntime, E: JitElement, I: JitElement, const D: usize
     indices: JitTensor<R, I, D>,
 ) -> JitTensor<R, E, D> {
     let shape_output = indices.shape.clone();
+    let total_elem = shape_output.num_elements();
     let output = empty_device(tensor.client.clone(), tensor.device.clone(), shape_output);
-    let kernel = GatherEagerKernel::<R, E>::new(dim);
-
-    Execution::start(kernel, tensor.client.clone())
-        .inputs(&[tensor.as_handle_ref(), indices.as_handle_ref()])
-        .outputs(&[output.as_handle_ref()])
-        .execute(CubeCountSettings::Output { pos: 0 });
 
+    let cube_dim = CubeDim::default();
+    let cube_count = calculate_cube_count_elemwise(total_elem, cube_dim);
+    unsafe {
+        gather_kernel::launch_unchecked::<E::Primitive, I::Primitive, R>(
+            &tensor.client,
+            cube_count,
+            cube_dim,
+            tensor.as_tensor_arg(1),
+            indices.as_tensor_arg(1),
+            output.as_tensor_arg(1),
+            ScalarArg::new(dim as u32),
+        )
+    }
     output
 }

examples/text-classification/Cargo.toml

@@ -20,7 +20,15 @@ cuda-jit = ["burn/cuda-jit"]
 
 [dependencies]
 # Burn
-burn = {path = "../../crates/burn", features=["train", "ndarray", "std", "metrics", "autotune", "fusion", "default"], default-features = false}
+burn = { path = "../../crates/burn", features = [
+    "train",
+    "ndarray",
+    "std",
+    "metrics",
+    "autotune",
+    "fusion",
+    "default",
+], default-features = false }
 
 # Tokenizer
 tokenizers = { version = "0.20.0", default-features = false, features = [