Merge branch 'main' into chore/feature-flags

Cargo.toml

@@ -101,7 +101,7 @@ ratatui = "0.29.0"
 
 # WGPU stuff
 text_placeholder = "0.5.1"
-wgpu = "23.0.0"
+wgpu = "24.0.0"
 
 # Benchmarks and Burnbench
 arboard = "3.4.1"
@@ -153,8 +153,8 @@ ahash = { version = "0.8.11", default-features = false }
 portable-atomic-util = { version = "0.2.4", features = ["alloc"] }
 
 ### For the main burn branch. ###
-cubecl = { git = "https://github.com/tracel-ai/cubecl", default-features = false, rev = "2cc42af02671d90255ab823e29a4a3ad2e564333" }
-cubecl-common = { git = "https://github.com/tracel-ai/cubecl", default-features = false, rev = "2cc42af02671d90255ab823e29a4a3ad2e564333" }
+cubecl = { git = "https://github.com/tracel-ai/cubecl", default-features = false, rev = "2a6dd3e60b686230a8f686aafd246342259f7003" }
+cubecl-common = { git = "https://github.com/tracel-ai/cubecl", default-features = false, rev = "2a6dd3e60b686230a8f686aafd246342259f7003" }
 ### For local development. ###
 # cubecl = { path = "../cubecl/crates/cubecl", default-features = false }
 # cubecl-common = { path = "../cubecl/crates/cubecl-common", default-features = false }

backend-comparison/benches/matmul_fused.rs

@@ -1,5 +1,9 @@
 use backend_comparison::persistence::save;
-use burn::tensor::{activation::relu, backend::Backend, Distribution, Shape, Tensor};
+use burn::tensor::{
+    activation::{gelu, relu},
+    backend::Backend,
+    Distribution, Shape, Tensor,
+};
 use burn_common::benchmark::{run_benchmark, Benchmark};
 use derive_new::new;
 
@@ -14,7 +18,7 @@ impl<B: Backend, const D: usize> Benchmark for MatmulBenchmark<B, D> {
     type Args = (Tensor<B, D>, Tensor<B, D>, Tensor<B, 1>);
 
     fn name(&self) -> String {
-        "matmul_bias_relu".into()
+        "matmul_relu_bias_gelu".into()
     }
 
     fn shapes(&self) -> Vec<Vec<usize>> {
@@ -23,7 +27,7 @@ impl<B: Backend, const D: usize> Benchmark for MatmulBenchmark<B, D> {
 
     fn execute(&self, (lhs, rhs, bias): Self::Args) {
         let bias = bias.unsqueeze();
-        relu(lhs.matmul(rhs) + bias);
+        gelu(relu(lhs.matmul(rhs)) + bias);
     }
 
     fn prepare(&self) -> Self::Args {

burn-book/src/advanced/no-std.md

@@ -68,15 +68,15 @@ We are using ndarray, so we just need to define the NdArray backend as usual
 use burn::{backend::NdArray, tensor::Tensor};
 
 type Backend = NdArray<f32>;
-type BackendDeice = <Backend as burn::tensor::backend::Backend>::Device;
+type BackendDevice = <Backend as burn::tensor::backend::Backend>::Device;
 ```
 
 Then inside the `main` function add 
 ```rs
 use your_model::Model;
 
 // Get a default device for the backend
-let device = BackendDeice::default();
+let device = BackendDevice::default();
 
 // Create a new model and load the state
 let model: Model<Backend> = Model::default();

crates/burn-autodiff/Cargo.toml

@@ -18,7 +18,7 @@ std = []
 async = [] # Require std
 
 [dependencies]
-burn-common = { path = "../burn-common", version = "0.17.0" }
+burn-common = { path = "../burn-common", version = "0.17.0", default-features = false }
 burn-tensor = { path = "../burn-tensor", version = "0.17.0", default-features = false }
 burn-tensor-testgen = { path = "../burn-tensor-testgen", version = "0.17.0", optional = true }
 

crates/burn-core/src/lib.rs

@@ -1,6 +1,7 @@
 #![cfg_attr(not(feature = "std"), no_std)]
 #![warn(missing_docs)]
 #![cfg_attr(docsrs, feature(doc_auto_cfg))]
+#![recursion_limit = "135"]
 
 //! The core crate of Burn.
 

crates/burn-fusion/src/stream/context.rs

@@ -59,6 +59,84 @@ pub(crate) struct OperationConverter {
     scalar_u8: Vec<u8>,
 }
 
+/// Fork of a [context](Context) which owns its data.
+pub struct ContextOwned<H> {
+    tensors: HashMap<TensorId, TensorDescription>,
+    handles: HandleContainer<H>,
+    scalar_f32: Vec<f32>,
+    scalar_f16: Vec<f16>,
+    scalar_bf16: Vec<bf16>,
+    scalar_i64: Vec<i64>,
+    scalar_i32: Vec<i32>,
+    scalar_i16: Vec<i16>,
+    scalar_i8: Vec<i8>,
+    scalar_u64: Vec<u64>,
+    scalar_u32: Vec<u32>,
+    scalar_u16: Vec<u16>,
+    scalar_u8: Vec<u8>,
+}
+
+impl<H: Clone> ContextOwned<H> {
+    /// Convert into [context](Context).
+    pub fn as_context(&mut self) -> Context<'_, H> {
+        Context {
+            tensors: &mut self.tensors,
+            handles: &mut self.handles,
+            scalar_f32: &self.scalar_f32,
+            scalar_f16: &self.scalar_f16,
+            scalar_bf16: &self.scalar_bf16,
+            scalar_i64: &self.scalar_i64,
+            scalar_i32: &self.scalar_i32,
+            scalar_i16: &self.scalar_i16,
+            scalar_i8: &self.scalar_i8,
+            scalar_u64: &self.scalar_u64,
+            scalar_u32: &self.scalar_u32,
+            scalar_u16: &self.scalar_u16,
+            scalar_u8: &self.scalar_u8,
+        }
+    }
+
+    /// Fork the context again.
+    pub fn fork(&self) -> ContextOwned<H> {
+        ContextOwned {
+            tensors: self.tensors.clone(),
+            handles: self.handles.fork(),
+            scalar_f32: self.scalar_f32.clone(),
+            scalar_f16: self.scalar_f16.clone(),
+            scalar_bf16: self.scalar_bf16.clone(),
+            scalar_i64: self.scalar_i64.clone(),
+            scalar_i32: self.scalar_i32.clone(),
+            scalar_i16: self.scalar_i16.clone(),
+            scalar_i8: self.scalar_i8.clone(),
+            scalar_u64: self.scalar_u64.clone(),
+            scalar_u32: self.scalar_u32.clone(),
+            scalar_u16: self.scalar_u16.clone(),
+            scalar_u8: self.scalar_u8.clone(),
+        }
+    }
+}
+
+impl<H: Clone> Context<'_, H> {
+    /// Fork the context into an [owned context](ContextOwned).
+    pub fn fork(&self) -> ContextOwned<H> {
+        ContextOwned {
+            tensors: self.tensors.clone(),
+            handles: self.handles.fork(),
+            scalar_f32: self.scalar_f32.clone(),
+            scalar_f16: self.scalar_f16.clone(),
+            scalar_bf16: self.scalar_bf16.clone(),
+            scalar_i64: self.scalar_i64.clone(),
+            scalar_i32: self.scalar_i32.clone(),
+            scalar_i16: self.scalar_i16.clone(),
+            scalar_i8: self.scalar_i8.clone(),
+            scalar_u64: self.scalar_u64.clone(),
+            scalar_u32: self.scalar_u32.clone(),
+            scalar_u16: self.scalar_u16.clone(),
+            scalar_u8: self.scalar_u8.clone(),
+        }
+    }
+}
+
 pub(crate) trait RelativeOps {
     /// Convert (usually an [`OperationDescription`]) to a relative form.
     ///

crates/burn-jit/src/fusion/base.rs

@@ -125,20 +125,16 @@ impl<R: JitRuntime, BT: BoolElement> FusionRuntime for FusionJitRuntime<R, BT> {
     fn optimizations(
         device: R::Device,
     ) -> Vec<Box<dyn burn_fusion::OptimizationBuilder<Self::Optimization>>> {
-        let mut optimizations: Vec<Box<dyn burn_fusion::OptimizationBuilder<Self::Optimization>>> =
-            vec![Box::new(ElementWiseBuilder::<R>::new(
+        vec![
+            Box::new(ElementWiseBuilder::<R>::new(
                 device.clone(),
                 BT::as_elem_native_unchecked().into(),
-            ))];
-
-        if cfg!(feature = "fusion-experimental") {
-            optimizations.push(Box::new(MatmulBuilder::<R>::new(
+            )),
+            Box::new(MatmulBuilder::<R>::new(
                 device.clone(),
                 BT::as_elem_native_unchecked().into(),
-            )));
-        }
-
-        optimizations
+            )),
+        ]
     }
 }
 

crates/burn-jit/src/fusion/matmul/builder.rs

@@ -47,7 +47,13 @@ impl<R: JitRuntime> OptimizationBuilder<JitOptimization<R>> for MatmulBuilder<R>
                 let rhs = self.builder.input_unhandled(&op.rhs);
                 let out = self.builder.output_unhandled(&op.out);
 
-                self.matmul = Some(FusedMatmul::new(lhs, rhs, out, op.clone()));
+                self.matmul = Some(FusedMatmul::new(
+                    lhs,
+                    rhs,
+                    out,
+                    op.clone(),
+                    Default::default(),
+                ));
             } else {
                 self.builder.close();
             }

crates/burn-jit/src/fusion/matmul/mod.rs

@@ -2,3 +2,4 @@ pub(crate) mod args;
 pub(crate) mod builder;
 pub(crate) mod optimization;
 pub(crate) mod spec;
+pub(crate) mod tune;