Fix conflicting feature flags w/ default-features = false

burn-tensor
- Move rayon to default flags (but not enabled by std for now)

burn-jit
- Does not require burn-tensor default features
- Enable burn-tensor/std w/ std flag

burn-import
- Split burn-tensor (only std flag) w/ and burn-ndarray
  - Use of burn-tensor w/ std and ndarray would enable rayon (for build dependency)

burn-wgpu
- Does not require burn-tensor default features

crates/burn-import/Cargo.toml

@@ -20,7 +20,8 @@ onnx = []
 pytorch = ["burn/record-item-custom-serde", "thiserror", "zip"]
 
 [dependencies]
-burn = { path = "../burn", version = "0.16.0", features = ["ndarray"] }
+burn = { path = "../burn", version = "0.16.0", default-features = false, features = ["std"]}
+burn-ndarray = { path = "../burn-ndarray", version = "0.16.0", default-features = false }
 onnx-ir = { path = "../onnx-ir", version = "0.16.0" }
 candle-core = { workspace = true }
 derive-new = { workspace = true }

crates/burn-import/src/burn/graph.rs

@@ -50,7 +50,7 @@ pub struct BurnGraph<PS: PrecisionSettings> {
 }
 
 // The backend used for recording.
-type Backend = burn::backend::ndarray::NdArray;
+type Backend = burn_ndarray::NdArray;
 
 impl<PS: PrecisionSettings> BurnGraph<PS> {
     /// Register a new operation node into the graph.

crates/burn-import/src/burn/node/base.rs

@@ -17,13 +17,12 @@ use super::{
     unsqueeze::UnsqueezeNode,
 };
 use crate::burn::{BurnImports, Scope, Type};
-use burn::backend::NdArray;
 use burn::record::PrecisionSettings;
 use proc_macro2::TokenStream;
 use serde::Serialize;
 
 /// Backend used for serialization.
-pub type SerializationBackend = NdArray<f32>;
+pub type SerializationBackend = burn_ndarray::NdArray<f32>;
 
 /// Codegen trait that should be implemented by all [node](Node) entries.
 pub trait NodeCodegen<PS: PrecisionSettings>: std::fmt::Debug {

crates/burn-jit/Cargo.toml

@@ -25,13 +25,13 @@ export_tests = [
     "paste",
 ]
 fusion = ["burn-fusion"]
-std = ["cubecl/std"]
+std = ["cubecl/std", "burn-tensor/std"]
 template = []
 
 [dependencies]
 burn-common = { path = "../burn-common", version = "0.16.0" }
 burn-fusion = { path = "../burn-fusion", version = "0.16.0", optional = true }
-burn-tensor = { path = "../burn-tensor", version = "0.16.0", features = [
+burn-tensor = { path = "../burn-tensor", version = "0.16.0", default-features = false, features = [
     "cubecl",
     "repr",
 ] }

crates/burn-tensor/Cargo.toml

@@ -16,7 +16,7 @@ cubecl = ["dep:cubecl"]
 cubecl-cuda = ["cubecl", "cubecl/cuda"]
 cubecl-hip = ["cubecl", "cubecl/hip"]
 cubecl-wgpu = ["cubecl", "cubecl/wgpu"]
-default = ["std", "repr"]
+default = ["std", "repr", "burn-common/rayon"]
 doc = ["default"]
 experimental-named-tensor = []
 export_tests = ["burn-tensor-testgen", "cubecl"]
@@ -26,7 +26,6 @@ std = [
     "half/std",
     "num-traits/std",
     "burn-common/std",
-    "burn-common/rayon",
     "colored",
 ]
 

crates/burn-wgpu/Cargo.toml

@@ -26,7 +26,7 @@ cubecl = { workspace = true, features = ["wgpu"] }
 
 burn-fusion = { path = "../burn-fusion", version = "0.16.0", optional = true }
 burn-jit = { path = "../burn-jit", version = "0.16.0", default-features = false }
-burn-tensor = { path = "../burn-tensor", version = "0.16.0", features = [
+burn-tensor = { path = "../burn-tensor", version = "0.16.0", default-features = false, features = [
     "cubecl-wgpu",
 ] }
 

examples/image-classification-web/Cargo.toml

@@ -34,4 +34,4 @@ js-sys = "0.3"
 
 [build-dependencies]
 # Used to generate code from ONNX model
-burn-import = { path = "../../crates/burn-import" }
+burn-import = { path = "../../crates/burn-import", default-features = false, features = ["onnx"]}