Add float cast op for JIT backend

burn-book/src/advanced/backend-extension/custom-cubecl-kernel.md

@@ -171,8 +171,14 @@ impl<R: JitRuntime, F: FloatElement, I: IntElement> Backend for JitBackend<R, F,
             .empty(shape_out.num_elements() * core::mem::size_of::<F>());
 
         // Create the output tensor primitive.
-        let output =
-            JitTensor::new_contiguous(lhs.client.clone(), lhs.device.clone(), shape_out, buffer);
+        // Create the output tensor primitive.
+        let output = JitTensor::new_contiguous(
+            lhs.client.clone(),
+            lhs.device.clone(),
+            shape_out,
+            buffer,
+            F::dtype(),
+        );
 
         // Declare the wgsl workgroup with the number of cubes in x, y and z.
         let cubes_needed_in_x = f32::ceil(num_rows as f32 / cube_dim.x as f32) as u32;
@@ -186,10 +192,10 @@ impl<R: JitRuntime, F: FloatElement, I: IntElement> Backend for JitBackend<R, F,
             &lhs.client,
             cube_count,
             cube_dim,
-            lhs.as_tensor_arg(1),
-            rhs.as_tensor_arg(1),
-            bias.as_tensor_arg(1),
-            output.as_tensor_arg(1),
+            lhs.as_tensor_arg::<F>(1),
+            rhs.as_tensor_arg::<F>(1),
+            bias.as_tensor_arg::<F>(1),
+            output.as_tensor_arg::<F>(1),
         );
 
         // Return the output tensor.
@@ -251,12 +257,12 @@ impl<B: Backend, C: CheckpointStrategy> Backend for Autodiff<B, C> {
 
                 // Set our state.
                 let (lhs_state, rhs_state, output, shape_bias) = ops.state;
-                let lhs = checkpointer.retrieve_node_output(lhs_state);
-                let rhs = checkpointer.retrieve_node_output(rhs_state);
+                let lhs: FloatTensor<B> = checkpointer.retrieve_node_output(lhs_state);
+                let rhs: FloatTensor<B> = checkpointer.retrieve_node_output(rhs_state);
 
                 // Fetch shapes of our tensor to support broadcasting.
-                let shape_lhs = B::float_shape(&lhs);
-                let shape_rhs = B::float_shape(&rhs);
+                let shape_lhs = lhs.shape();
+                let shape_rhs = rhs.shape();
 
                 // Compute the gradient of the output using the already existing `relu_backward`
                 // function in the basic Burn backend trait.
@@ -314,7 +320,7 @@ impl<B: Backend, C: CheckpointStrategy> Backend for Autodiff<B, C> {
                 // compute bound operation.
                 let lhs_state = prep.checkpoint(&lhs);
                 let rhs_state = prep.checkpoint(&rhs);
-                let bias_shape = B::float_shape(&bias.primitive);
+                let bias_shape = bias.primitive.shape();
 
                 let output = B::fused_matmul_add_relu(
                     lhs.primitive.clone(),

burn-book/src/advanced/backend-extension/custom-wgpu-kernel.md

@@ -239,14 +239,19 @@ impl<F: FloatElement, I: IntElement> Backend for JitBackend<WgpuRuntime, F, I> {
             .empty(shape_out.num_elements() * core::mem::size_of::<F>());
 
         // Create the output tensor primitive.
-        let output =
-            JitTensor::new_contiguous(lhs.client.clone(), lhs.device.clone(), shape_out, buffer);
+        let output = JitTensor::new_contiguous(
+            lhs.client.clone(),
+            lhs.device.clone(),
+            shape_out,
+            buffer,
+            F::dtype(),
+        );
 
         // Create the kernel.
         let kernel = FusedMatmulAddRelu::<F>::new(cube_dim);
 
         // Build info buffer with tensor information needed by the kernel, such as shapes and strides.
-        let info = build_info(&[&lhs, &rhs, &output]);
+        let info = build_info::<_, F>(&[&lhs, &rhs, &output]);
         let info_handle = lhs.client.create(bytemuck::cast_slice(&info));
 
         // Declare the wgsl workgroup with the number of cubes in x, y and z.
@@ -331,12 +336,12 @@ impl<B: Backend, C: CheckpointStrategy> Backend for Autodiff<B, C> {
 
                 // Set our state.
                 let (lhs_state, rhs_state, output, shape_bias) = ops.state;
-                let lhs = checkpointer.retrieve_node_output(lhs_state);
-                let rhs = checkpointer.retrieve_node_output(rhs_state);
+                let lhs: FloatTensor<B> = checkpointer.retrieve_node_output(lhs_state);
+                let rhs: FloatTensor<B> = checkpointer.retrieve_node_output(rhs_state);
 
                 // Fetch shapes of our tensor to support broadcasting.
-                let shape_lhs = B::float_shape(&lhs);
-                let shape_rhs = B::float_shape(&rhs);
+                let shape_lhs = lhs.shape();
+                let shape_rhs = rhs.shape();
 
                 // Compute the gradient of the output using the already existing `relu_backward`
                 // function in the basic Burn backend trait.
@@ -392,7 +397,7 @@ impl<B: Backend, C: CheckpointStrategy> Backend for Autodiff<B, C> {
                 // during the backward pass. Here we choose to save it in the state because it's a compute bound operation.
                 let lhs_state = prep.checkpoint(&lhs);
                 let rhs_state = prep.checkpoint(&rhs);
-                let bias_shape = B::float_shape(&bias.primitive);
+                let bias_shape = bias.primitive.shape();
 
                 let output = B::fused_matmul_add_relu(
                     lhs.primitive.clone(),

crates/burn-autodiff/src/grads.rs

@@ -1,4 +1,4 @@
-use burn_tensor::{backend::Backend, container::TensorContainer, ops::FloatTensor};
+use burn_tensor::{backend::Backend, container::TensorContainer, ops::FloatTensor, TensorMetadata};
 
 use crate::{
     graph::{NodeRef, Requirement},
@@ -22,7 +22,7 @@ impl Gradients {
         };
         gradients.register::<B>(
             root_node.id,
-            B::float_ones(B::float_shape(&root_tensor), &B::float_device(&root_tensor)),
+            B::float_ones(root_tensor.shape(), &B::float_device(&root_tensor)),
         );
         gradients
     }

crates/burn-autodiff/src/ops/base.rs

@@ -10,7 +10,7 @@ use crate::{
     graph::{ComputingProperty, NodeID, NodeRef, Requirement, Step},
     tensor::AutodiffTensor,
 };
-use burn_tensor::{backend::Backend, ops::FloatTensor, Shape};
+use burn_tensor::{backend::Backend, ops::FloatTensor, Shape, TensorMetadata};
 use std::marker::PhantomData;
 
 /// Operation in preparation.
@@ -292,7 +292,7 @@ impl<const N: usize> Step for UntrackedOpsStep<N> {
 /// If broadcasting happened during the forward pass, the gradients will be sum along the
 /// broadcasted dimension.
 pub fn broadcast_shape<B: Backend>(mut grad: FloatTensor<B>, shape: &Shape) -> FloatTensor<B> {
-    let shape_grad = B::float_shape(&grad);
+    let shape_grad = grad.shape();
     let ndims = shape_grad.num_dims();
 
     for i in 0..ndims {

crates/burn-autodiff/src/ops/bool_tensor.rs

@@ -11,10 +11,6 @@ impl<B: Backend, C: CheckpointStrategy> BoolTensorOps<Self> for Autodiff<B, C> {
         B::bool_from_data(data, device)
     }
 
-    fn bool_shape(tensor: &BoolTensor<B>) -> Shape {
-        B::bool_shape(tensor)
-    }
-
     async fn bool_into_data(tensor: BoolTensor<B>) -> TensorData {
         B::bool_into_data(tensor).await
     }

crates/burn-autodiff/src/ops/int_tensor.rs

@@ -11,10 +11,6 @@ impl<B: Backend, C: CheckpointStrategy> IntTensorOps<Self> for Autodiff<B, C> {
         B::int_from_data(data, device)
     }
 
-    fn int_shape(tensor: &IntTensor<B>) -> Shape {
-        B::int_shape(tensor)
-    }
-
     async fn int_into_data(tensor: IntTensor<B>) -> TensorData {
         B::int_into_data(tensor).await
     }