style: apply rustfmt across all crates and demos

Consistent formatting pass: line wrapping, import sorting, trailing
whitespace removal, let-chain indentation, merged derive attributes,
and unsafe block reformatting.

Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>

crates/core/rtx-autograd/src/autodiff/backend.rs

@@ -1362,7 +1362,6 @@ fn create_parents_2<B: Backend, const D: usize>(
 
 /// Convert internal gradient storage to public GradientMap.
 fn convert_gradient_storage<B: Backend>(storage: GradientStorage<B>) -> GradientMap<B> {
-    
     // Note: Full conversion would require extending GradientMap with insert method
     // For now, return empty map - will be properly implemented when needed
     GradientMap::new()

crates/core/rtx-autograd/src/autodiff/ops/reductions.rs

@@ -622,7 +622,6 @@ where
     let scaled_diff = B::mul(abs_diff, scale_tensor);
     let neg_scaled = B::neg(scaled_diff);
 
-
     B::exp(neg_scaled)
 }
 

crates/core/rtx-autograd/src/autodiff/ops/shapes.rs

@@ -171,7 +171,6 @@ where
 {
     // Get the tensor from grad_output (D2 dimensional)
 
-
     match grad {
         GradTensor::D1(g) if D2 == 1 => {
             let reshaped = B::reshape(g.clone(), target_shape);
@@ -197,11 +196,9 @@ where
             let reshaped = B::reshape(g.clone(), target_shape);
             wrap_as_grad_tensor::<B, D1>(reshaped)
         }
-        _ => {
+        _ => Err(AutogradError::DimensionMismatch(
-            Err(AutogradError::DimensionMismatch(
             "reshape backward".to_string(),
-            ))
+        )),
-        }
     }
 }
 

crates/core/rtx-autograd/src/func/functionalize.rs

@@ -366,10 +366,16 @@ where
     let func_outputs = f(&func_inputs)?;
 
     // Collect mutations from all inputs
-    let mutations: Vec<MutationRecord> = func_inputs.iter().flat_map(Functionalized::mutations).collect();
+    let mutations: Vec<MutationRecord> = func_inputs
+        .iter()
+        .flat_map(Functionalized::mutations)
+        .collect();
 
     // Unwrap outputs
-    let outputs: Vec<Variable> = func_outputs.into_iter().map(Functionalized::into_inner).collect();
+    let outputs: Vec<Variable> = func_outputs
+        .into_iter()
+        .map(Functionalized::into_inner)
+        .collect();
 
     Ok((outputs, mutations))
 }

crates/core/rtx-autograd/src/func/vjp.rs

@@ -58,7 +58,8 @@ where
     let vjp_final = compute_gradients_weighted(&f, &inputs_with_grad, &combined_cotangent)?;
 
     // Detach outputs
-    let outputs_detached: Vec<Variable> = outputs.iter().map(super::super::Variable::detach).collect();
+    let outputs_detached: Vec<Variable> =
+        outputs.iter().map(super::super::Variable::detach).collect();
 
     Ok((outputs_detached, vjp_final))
 }

crates/core/rtx-autograd/src/vmap/vmap_impl.rs

@@ -60,7 +60,8 @@ where
         // Determine batch size
         let batch_size = inputs
             .iter()
-            .zip(in_dims.iter()).find_map(|(tensor, dim)| dim.map(|d| tensor.shape().get(d).copied().unwrap_or(1)))
+            .zip(in_dims.iter())
+            .find_map(|(tensor, dim)| dim.map(|d| tensor.shape().get(d).copied().unwrap_or(1)))
             .unwrap_or(1);
 
         if batch_size == 0 {
@@ -174,7 +175,8 @@ where
         // Determine batch size from the first batched input
         let batch_size = inputs
             .iter()
-            .zip(in_dims.iter()).find_map(|(tensor, dim)| dim.map(|d| tensor.shape().get(d).copied().unwrap_or(1)))
+            .zip(in_dims.iter())
+            .find_map(|(tensor, dim)| dim.map(|d| tensor.shape().get(d).copied().unwrap_or(1)))
             .unwrap_or(1);
 
         if batch_size == 0 {

crates/core/rtx-backend-cpu/src/simd/neon.rs

@@ -4,7 +4,9 @@
 
 #![cfg(target_arch = "aarch64")]
 
-use std::arch::aarch64::{vld1q_f32, vaddq_f32, vst1q_f32, vmulq_f32, vfmaq_f32, vdupq_n_f32, vaddvq_f32, vmaxq_f32};
+use std::arch::aarch64::{
+    vaddq_f32, vaddvq_f32, vdupq_n_f32, vfmaq_f32, vld1q_f32, vmaxq_f32, vmulq_f32, vst1q_f32,
+};
 
 /// NEON vector width in f32 elements
 pub const VECTOR_WIDTH: usize = 4;
@@ -13,7 +15,8 @@ pub const VECTOR_WIDTH: usize = 4;
 ///
 /// Uses `vaddq_f32` for 4-wide vector addition.
 #[target_feature(enable = "neon")]
-pub unsafe fn add_neon(a: &[f32], b: &[f32], c: &mut [f32]) { unsafe {
+pub unsafe fn add_neon(a: &[f32], b: &[f32], c: &mut [f32]) {
+    unsafe {
         let len = a.len();
         let chunks = len / VECTOR_WIDTH;
         let remainder = len % VECTOR_WIDTH;
@@ -32,7 +35,8 @@ pub unsafe fn add_neon(a: &[f32], b: &[f32], c: &mut [f32]) { unsafe {
         for i in 0..remainder {
             c[tail_start + i] = a[tail_start + i] + b[tail_start + i];
         }
-}}
+    }
+}
 
 /// Safe wrapper for NEON add
 pub fn add(a: &[f32], b: &[f32], c: &mut [f32]) {
@@ -43,7 +47,8 @@ pub fn add(a: &[f32], b: &[f32], c: &mut [f32]) {
 ///
 /// Uses `vmulq_f32` for 4-wide vector multiplication.
 #[target_feature(enable = "neon")]
-pub unsafe fn mul_neon(a: &[f32], b: &[f32], c: &mut [f32]) { unsafe {
+pub unsafe fn mul_neon(a: &[f32], b: &[f32], c: &mut [f32]) {
+    unsafe {
         let len = a.len();
         let chunks = len / VECTOR_WIDTH;
         let remainder = len % VECTOR_WIDTH;
@@ -60,7 +65,8 @@ pub unsafe fn mul_neon(a: &[f32], b: &[f32], c: &mut [f32]) { unsafe {
         for i in 0..remainder {
             c[tail_start + i] = a[tail_start + i] * b[tail_start + i];
         }
-}}
+    }
+}
 
 /// Safe wrapper for NEON mul
 pub fn mul(a: &[f32], b: &[f32], c: &mut [f32]) {
@@ -71,7 +77,8 @@ pub fn mul(a: &[f32], b: &[f32], c: &mut [f32]) {
 ///
 /// Uses `vfmaq_f32` for fused multiply-add (no intermediate rounding).
 #[target_feature(enable = "neon")]
-pub unsafe fn fma_neon(a: &[f32], b: &[f32], c: &mut [f32]) { unsafe {
+pub unsafe fn fma_neon(a: &[f32], b: &[f32], c: &mut [f32]) {
+    unsafe {
         let len = a.len();
         let chunks = len / VECTOR_WIDTH;
         let remainder = len % VECTOR_WIDTH;
@@ -90,7 +97,8 @@ pub unsafe fn fma_neon(a: &[f32], b: &[f32], c: &mut [f32]) { unsafe {
         for i in 0..remainder {
             c[tail_start + i] = a[tail_start + i].mul_add(b[tail_start + i], c[tail_start + i]);
         }
-}}
+    }
+}
 
 /// Safe wrapper for NEON fma
 pub fn fma(a: &[f32], b: &[f32], c: &mut [f32]) {
@@ -101,7 +109,8 @@ pub fn fma(a: &[f32], b: &[f32], c: &mut [f32]) {
 ///
 /// Uses `vaddvq_f32` for horizontal sum.
 #[target_feature(enable = "neon")]
-pub unsafe fn sum_neon(a: &[f32]) -> f32 { unsafe {
+pub unsafe fn sum_neon(a: &[f32]) -> f32 {
+    unsafe {
         let len = a.len();
         let chunks = len / VECTOR_WIDTH;
         let remainder = len % VECTOR_WIDTH;
@@ -124,7 +133,8 @@ pub unsafe fn sum_neon(a: &[f32]) -> f32 { unsafe {
         }
 
         total
-}}
+    }
+}
 
 /// Safe wrapper for NEON sum
 pub fn sum(a: &[f32]) -> f32 {
@@ -135,7 +145,8 @@ pub fn sum(a: &[f32]) -> f32 {
 ///
 /// Uses fused multiply-add for accumulation.
 #[target_feature(enable = "neon")]
-pub unsafe fn dot_neon(a: &[f32], b: &[f32]) -> f32 { unsafe {
+pub unsafe fn dot_neon(a: &[f32], b: &[f32]) -> f32 {
+    unsafe {
         let len = a.len();
         let chunks = len / VECTOR_WIDTH;
         let remainder = len % VECTOR_WIDTH;
@@ -157,7 +168,8 @@ pub unsafe fn dot_neon(a: &[f32], b: &[f32]) -> f32 { unsafe {
         }
 
         total
-}}
+    }
+}
 
 /// Safe wrapper for NEON dot
 pub fn dot(a: &[f32], b: &[f32]) -> f32 {
@@ -166,7 +178,8 @@ pub fn dot(a: &[f32], b: &[f32]) -> f32 {
 
 /// Element-wise maximum
 #[target_feature(enable = "neon")]
-pub unsafe fn max_neon(a: &[f32], b: &[f32], c: &mut [f32]) { unsafe {
+pub unsafe fn max_neon(a: &[f32], b: &[f32], c: &mut [f32]) {
+    unsafe {
         let len = a.len();
         let chunks = len / VECTOR_WIDTH;
         let remainder = len % VECTOR_WIDTH;
@@ -183,7 +196,8 @@ pub unsafe fn max_neon(a: &[f32], b: &[f32], c: &mut [f32]) { unsafe {
         for i in 0..remainder {
             c[tail_start + i] = a[tail_start + i].max(b[tail_start + i]);
         }
-}}
+    }
+}
 
 /// Safe wrapper for NEON max
 pub fn max(a: &[f32], b: &[f32], c: &mut [f32]) {
@@ -209,7 +223,8 @@ pub fn exp(a: &[f32], c: &mut [f32]) {
 
 /// Vector scale: c = a * scalar
 #[target_feature(enable = "neon")]
-pub unsafe fn scale_neon(a: &[f32], scalar: f32, c: &mut [f32]) { unsafe {
+pub unsafe fn scale_neon(a: &[f32], scalar: f32, c: &mut [f32]) {
+    unsafe {
         let len = a.len();
         let chunks = len / VECTOR_WIDTH;
         let remainder = len % VECTOR_WIDTH;
@@ -227,7 +242,8 @@ pub unsafe fn scale_neon(a: &[f32], scalar: f32, c: &mut [f32]) { unsafe {
         for i in 0..remainder {
             c[tail_start + i] = a[tail_start + i] * scalar;
         }
-}}
+    }
+}
 
 /// Safe wrapper for NEON scale
 pub fn scale(a: &[f32], scalar: f32, c: &mut [f32]) {
@@ -251,7 +267,8 @@ pub unsafe fn gemm_neon(
     beta: f32,
     c: &mut [f32],
     ldc: usize,
-) { unsafe {
+) {
+    unsafe {
         // Block sizes for cache efficiency on Apple Silicon
         const MC: usize = 64; // Rows of A per block
         const NC: usize = 256; // Cols of B per block
@@ -327,7 +344,8 @@ pub unsafe fn gemm_neon(
                 }
             }
         }
-}}
+    }
+}
 
 /// Safe wrapper for NEON gemm
 pub fn gemm(

crates/core/rtx-backend-sycl/src/ops/attention.rs

@@ -55,7 +55,9 @@ pub fn flash_attention(
     let q_data = query.to_host()?;
     let k_data = key.to_host()?;
     let v_data = value.to_host()?;
-    let mask_data = mask.map(super::super::tensor::SyclTensorPrimitive::to_host).transpose()?;
+    let mask_data = mask
+        .map(super::super::tensor::SyclTensorPrimitive::to_host)
+        .transpose()?;
 
     let mut output = vec![0.0f32; batch * heads * q_seq * head_dim];
 

crates/core/rtx-backend-sycl/src/ops/normalization.rs

@@ -16,7 +16,9 @@ pub fn layer_norm<const D: usize>(
     let shape = tensor.shape();
     let data = tensor.to_host()?;
     let weight_data = weight.to_host()?;
-    let bias_data = bias.map(super::super::tensor::SyclTensorPrimitive::to_host).transpose()?;
+    let bias_data = bias
+        .map(super::super::tensor::SyclTensorPrimitive::to_host)
+        .transpose()?;
 
     let normalized_shape = shape[D - 1];
     if weight_data.len() != normalized_shape {

crates/core/rtx-cubecl/src/backend.rs

@@ -141,7 +141,8 @@ impl Backend for CubeclBackend {
 
         // Try to allocate on GPU, fall back to CPU
         if let Ok(client) = client::get_or_create(device)
-            && let Ok(handle) = client.create_f32(&data) {
+            && let Ok(handle) = client.create_f32(&data)
+        {
             return CubeclTensorPrimitive::from_handle(shape, handle, device.clone());
         }
 
@@ -155,7 +156,8 @@ impl Backend for CubeclBackend {
         let data = vec![1.0f32; numel];
 
         if let Ok(client) = client::get_or_create(device)
-            && let Ok(handle) = client.create_f32(&data) {
+            && let Ok(handle) = client.create_f32(&data)
+        {
             return CubeclTensorPrimitive::from_handle(shape, handle, device.clone());
         }
 
@@ -172,7 +174,8 @@ impl Backend for CubeclBackend {
         let data = vec![fill_value; numel];
 
         if let Ok(client) = client::get_or_create(device)
-            && let Ok(handle) = client.create_f32(&data) {
+            && let Ok(handle) = client.create_f32(&data)
+        {
             return CubeclTensorPrimitive::from_handle(shape, handle, device.clone());
         }
 
@@ -192,7 +195,8 @@ impl Backend for CubeclBackend {
             .collect();
 
         if let Ok(client) = client::get_or_create(device)
-            && let Ok(handle) = client.create_f32(&data) {
+            && let Ok(handle) = client.create_f32(&data)
+        {
             return CubeclTensorPrimitive::from_handle(shape, handle, device.clone());
         }
 
@@ -217,7 +221,8 @@ impl Backend for CubeclBackend {
             .collect();
 
         if let Ok(client) = client::get_or_create(device)
-            && let Ok(handle) = client.create_f32(&data) {
+            && let Ok(handle) = client.create_f32(&data)
+        {
             return CubeclTensorPrimitive::from_handle(shape, handle, device.clone());
         }
 
@@ -231,7 +236,8 @@ impl Backend for CubeclBackend {
         device: &Self::Device,
     ) -> Self::TensorPrimitive<D> {
         if let Ok(client) = client::get_or_create(device)
-            && let Ok(handle) = client.create_f32(data) {
+            && let Ok(handle) = client.create_f32(data)
+        {
             return CubeclTensorPrimitive::from_handle(shape, handle, device.clone());
         }
 
@@ -606,7 +612,8 @@ impl Backend for CubeclBackend {
         if let Some(buffer) = tensor.buffer() {
             if let Some(gpu_handle) = buffer.as_gpu()
                 && let Ok(client) = client::get_or_create(&tensor.device)
-                    && let Ok(data) = client.read_f32(gpu_handle) {
+                && let Ok(data) = client.read_f32(gpu_handle)
+            {
                 return data;
             }
             // Read from CPU buffer

crates/core/rtx-cubecl/src/client.rs

@@ -239,7 +239,7 @@ impl std::fmt::Debug for CubeclClient {
 ///
 /// This allows reusing clients across tensor operations.
 mod registry {
-    use super::{CubeclDevice, CubeclClient, Result, CubeclError};
+    use super::{CubeclClient, CubeclDevice, CubeclError, Result};
     use std::collections::HashMap;
     use std::sync::RwLock;
 

crates/core/rtx-cubecl/src/ops.rs

@@ -55,7 +55,8 @@ pub fn read_tensor_data<const D: usize>(tensor: &CubeclTensorPrimitive<D>) -> Ve
         // Try to read from GPU
         if let Some(gpu_handle) = buffer.as_gpu()
             && let Ok(client) = client::get_or_create(&tensor.device)
-                && let Ok(data) = client.read_f32(gpu_handle) {
+            && let Ok(data) = client.read_f32(gpu_handle)
+        {
             return data;
         }
         // Read from CPU buffer
@@ -78,7 +79,8 @@ pub fn write_tensor_data<const D: usize>(
 ) -> CubeclTensorPrimitive<D> {
     // Try to write to GPU
     if let Ok(client) = client::get_or_create(device)
-        && let Ok(handle) = client.create_f32(&data) {
+        && let Ok(handle) = client.create_f32(&data)
+    {
         return CubeclTensorPrimitive::from_handle(shape, handle, device.clone());
     }
 

crates/core/rtx-graph/src/optimizer/mod.rs

@@ -461,8 +461,10 @@ impl SelfOptimizer {
             0.02 // Significant
         };
 
-        let effect_size =
+        let effect_size = f64::midpoint(
-            f64::midpoint(execution_time_improvement.abs(), throughput_improvement.abs()) / 100.0;
+            execution_time_improvement.abs(),
+            throughput_improvement.abs(),
+        ) / 100.0;
         let confidence_interval = (effect_size - 0.1, effect_size + 0.1);
 
         Ok(AbTestResults {

crates/core/rtx-graph/src/optimizer/tests.rs

@@ -153,9 +153,7 @@ fn test_cost_benefit_analysis() {
     // Should create a valid optimization decision
     assert!(decision.confidence >= 0.0 && decision.confidence <= 1.0);
     assert!(decision.expected_benefit.execution_time_reduction >= 0.0);
-    assert!(
+    assert!(decision.cost_estimate.risk_score >= 0.0 && decision.cost_estimate.risk_score <= 1.0);
-        decision.cost_estimate.risk_score >= 0.0 && decision.cost_estimate.risk_score <= 1.0
-    );
     assert_eq!(decision.status, DecisionStatus::Pending);
     assert_eq!(decision.target_pattern, WorkloadPatternType::ComputeBound);
 }

crates/core/rtx-graph/src/zerocopy/tests.rs

@@ -9,10 +9,7 @@ async fn create_test_manager() -> ZeroCopyManager {
 }
 
 /// Helper to create test GPU memory region
-async fn create_test_gpu_region(
+async fn create_test_gpu_region(manager: &ZeroCopyManager, size: usize) -> Arc<GpuMemoryRegion> {
-    manager: &ZeroCopyManager,
-    size: usize,
-) -> Arc<GpuMemoryRegion> {
     manager
         .allocate_gpu_memory(0, size, 4096)
         .expect("Failed to allocate test GPU memory")

crates/core/rtx-interpret/src/neuron/tcav.rs

@@ -332,9 +332,8 @@ impl TCAV {
             gradient_data.push(grad_i);
         }
 
-        Tensor::from_slice(&gradient_data, input.shape().dims(), device).map_err(|e| {
+        Tensor::from_slice(&gradient_data, input.shape().dims(), device)
-            InterpretError::tensor(format!("Failed to create gradient tensor: {}", e))
+            .map_err(|e| InterpretError::tensor(format!("Failed to create gradient tensor: {}", e)))
-        })
     }
 
     /// Extract class output from model output tensor

crates/core/rtx-interpret/src/sae/features.rs

@@ -452,9 +452,7 @@ impl FeatureAnalyzer {
 
     /// Get total samples analyzed
     pub fn total_samples(&self) -> usize {
-        self.feature_stats
+        self.feature_stats.first().map_or(0, |s| s.samples_seen)
-            .first()
-            .map_or(0, |s| s.samples_seen)
     }
 }
 

crates/core/rtx-losses/src/combined_loss.rs

@@ -323,7 +323,10 @@ impl CombinedLoss {
 
     /// Get total effective weight (sum of enabled component weights)
     pub fn total_effective_weight(&self) -> f32 {
-        self.components.iter().map(LossComponent::effective_weight).sum()
+        self.components
+            .iter()
+            .map(LossComponent::effective_weight)
+            .sum()
     }
 
     /// Validate configuration

crates/core/rtx-losses/src/hinge_loss.rs

@@ -156,7 +156,10 @@ impl HingeLoss {
     /// # Panics
     /// Panics if smoothing is not in [0, 1)
     pub fn with_label_smoothing(mut self, smoothing: f32) -> Self {
-        assert!((0.0..1.0).contains(&smoothing), "Label smoothing must be in [0, 1), got {smoothing}");
+        assert!(
+            (0.0..1.0).contains(&smoothing),
+            "Label smoothing must be in [0, 1), got {smoothing}"
+        );
         self.label_smoothing = smoothing;
         self
     }

crates/core/rtx-losses/src/quantile_loss.rs

@@ -93,7 +93,10 @@ impl QuantileLoss {
     /// - tau = 0.5: 50th percentile / median (equivalent to MAE)
     /// - tau = 0.9: 90th percentile (penalizes under-prediction more)
     pub fn with_tau(mut self, tau: f32) -> Self {
-        assert!(!(tau <= 0.0 || tau >= 1.0), "Tau must be between 0 and 1 (exclusive)");
+        assert!(
+            !(tau <= 0.0 || tau >= 1.0),
+            "Tau must be between 0 and 1 (exclusive)"
+        );
         self.tau = tau;
         self
     }

crates/core/rtx-losses/src/swav_minimal.rs

@@ -62,7 +62,8 @@ impl SwAVMinimal {
 
     /// Set custom prototypes
     pub fn with_prototypes(mut self, prototypes: MinimalTensor) -> Self {
-        assert!(prototypes.shape()[0] == self.num_prototypes, 
+        assert!(
+            prototypes.shape()[0] == self.num_prototypes,
             "Prototype tensor must have {} prototypes",
             self.num_prototypes
         );

crates/core/rtx-losses/src/wasserstein_loss.rs

@@ -133,7 +133,10 @@ impl WassersteinLoss {
 
     /// Set number of random projections for sliced Wasserstein
     pub fn with_num_projections(mut self, num_projections: usize) -> Self {
-        assert!(num_projections != 0, "Number of projections must be positive");
+        assert!(
+            num_projections != 0,
+            "Number of projections must be positive"
+        );
         self.num_projections = num_projections;
         self
     }

crates/core/rtx-macros/src/config/codegen.rs

@@ -50,14 +50,14 @@ impl ConfigCodegen {
                     return Err(syn::Error::new_spanned(
                         ast,
                         "Config derive only supports named fields",
-                    ))
+                    ));
                 }
             },
             _ => {
                 return Err(syn::Error::new_spanned(
                     ast,
                     "Config derive only supports structs",
-                ))
+                ));
             }
         };
 
@@ -236,7 +236,8 @@ impl ConfigCodegen {
 /// Check if a type is Option<T>.
 fn is_option_type(ty: &Type) -> bool {
     if let Type::Path(type_path) = ty
-        && let Some(segment) = type_path.path.segments.last() {
+        && let Some(segment) = type_path.path.segments.last()
+    {
         return segment.ident == "Option";
     }
     false

crates/core/rtx-macros/src/lib.rs

@@ -92,7 +92,10 @@ mod shared;
 /// including parameter management, device handling, and training mode control.
 ///
 /// See the [crate-level documentation](crate) for detailed usage examples.
-#[proc_macro_derive(Module, attributes(param, module, constant, device_field, training_field))]
+#[proc_macro_derive(
+    Module,
+    attributes(param, module, constant, device_field, training_field)
+)]
 pub fn module_derive(input: TokenStream) -> TokenStream {
     let input = syn::parse_macro_input!(input as syn::DeriveInput);
     module::derive_impl(&input)

crates/core/rtx-macros/src/module/codegen.rs

@@ -1,8 +1,8 @@
 //! Code generation for the Module derive macro.
 
 use crate::shared::field::{
-    find_device_field, find_training_field, get_param_fields, parse_fields, AnalyzedField,
+    AnalyzedField, FieldKind, find_device_field, find_training_field, get_param_fields,
-    FieldKind,
+    parse_fields,
 };
 use proc_macro2::{Ident, TokenStream};
 use quote::quote;
@@ -67,7 +67,8 @@ impl ModuleCodegen {
             };
         }
 
-        let param_code: Vec<TokenStream> = param_fields.iter().map(|f| f.gen_parameters()).collect();
+        let param_code: Vec<TokenStream> =
+            param_fields.iter().map(|f| f.gen_parameters()).collect();
 
         quote! {
             fn parameters(&self) -> Vec<&crate::Tensor> {
@@ -90,8 +91,10 @@ impl ModuleCodegen {
             };
         }
 
-        let param_code: Vec<TokenStream> =
+        let param_code: Vec<TokenStream> = param_fields
-            param_fields.iter().map(|f| f.gen_parameters_mut()).collect();
+            .iter()
+            .map(|f| f.gen_parameters_mut())
+            .collect();
 
         quote! {
             fn parameters_mut(&mut self) -> Vec<&mut crate::Tensor> {
@@ -107,7 +110,12 @@ impl ModuleCodegen {
         let to_device_code: Vec<TokenStream> = self
             .fields
             .iter()
-            .filter(|f| matches!(f.kind, FieldKind::Param | FieldKind::Module | FieldKind::Device))
+            .filter(|f| {
+                matches!(
+                    f.kind,
+                    FieldKind::Param | FieldKind::Module | FieldKind::Device
+                )
+            })
             .map(super::super::shared::field::AnalyzedField::gen_to_device)
             .collect();
 

crates/core/rtx-macros/src/shared/field.rs

@@ -42,7 +42,8 @@ impl AnalyzedField {
     /// Check if this is an Option type.
     pub fn is_option_type(ty: &Type) -> bool {
         if let Type::Path(type_path) = ty
-            && let Some(segment) = type_path.path.segments.last() {
+            && let Some(segment) = type_path.path.segments.last()
+        {
             return segment.ident == "Option";
         }
         false
@@ -55,7 +56,8 @@ impl AnalyzedField {
             && let Some(segment) = type_path.path.segments.last()
             && segment.ident == "Option"
             && let syn::PathArguments::AngleBracketed(args) = &segment.arguments
-                        && let Some(syn::GenericArgument::Type(inner)) = args.args.first() {
+            && let Some(syn::GenericArgument::Type(inner)) = args.args.first()
+        {
             return Some(inner);
         }
         None
@@ -208,14 +210,14 @@ pub fn parse_fields(ast: &syn::DeriveInput) -> syn::Result<Vec<AnalyzedField>> {
                 return Err(syn::Error::new_spanned(
                     ast,
                     "Module derive only supports named fields",
-                ))
+                ));
             }
         },
         _ => {
             return Err(syn::Error::new_spanned(
                 ast,
                 "Module derive only supports structs",
-            ))
+            ));
         }
     };
 

crates/core/rtx-macros/src/shared/mod.rs

@@ -1,4 +1,4 @@
 //! Shared utilities for the derive macros.
 
-pub mod field;
 pub mod attribute;
+pub mod field;

crates/core/rtx-macros/tests/module_derive.rs

@@ -247,7 +247,11 @@ mod config_tests {
         }
 
         impl Conv2dConfig {
-            pub fn new(in_channels: usize, out_channels: usize, kernel_size: (usize, usize)) -> Self {
+            pub fn new(
+                in_channels: usize,
+                out_channels: usize,
+                kernel_size: (usize, usize),
+            ) -> Self {
                 Self {
                     in_channels,
                     out_channels,

crates/core/rtx-memory/src/gpu_allocator/arena.rs

@@ -1,6 +1,8 @@
 //! Arena-based GPU memory allocator implementation
 
-use super::types::{ArenaBlock, CompactionStats, DeviceId, GpuMemoryBlock, GpuMemoryStats, GpuMemoryType};
+use super::types::{
+    ArenaBlock, CompactionStats, DeviceId, GpuMemoryBlock, GpuMemoryStats, GpuMemoryType,
+};
 use crate::{MemoryError, Result};
 use parking_lot::Mutex;
 use std::collections::{BTreeMap, HashMap, VecDeque};

crates/core/rtx-memory/src/gpu_oom.rs

@@ -7,8 +7,8 @@ use crate::gpu_allocator::{DeviceId, GpuMemoryBlock, GpuMemoryStats};
 use crate::{MemoryError, Result};
 use parking_lot::{Mutex, RwLock};
 use std::collections::{BTreeMap, HashMap, VecDeque};
-use std::sync::atomic::{AtomicBool, Ordering};
 use std::sync::Weak;
+use std::sync::atomic::{AtomicBool, Ordering};
 use std::time::{Duration, Instant};
 
 /// OOM recovery strategy

crates/core/rtx-memory/src/integration_tests.rs

@@ -507,7 +507,10 @@ async fn test_metrics_accuracy() {
     }
 
     let mid_metrics = pool_manager.metrics().await;
-    assert_eq!(mid_metrics.allocation_count() - initial_alloc_count, NUM_ALLOCS);
+    assert_eq!(
+        mid_metrics.allocation_count() - initial_alloc_count,
+        NUM_ALLOCS
+    );
     // Note: active_allocations tracking may be affected by pool warmup
     // Just verify allocations were counted correctly
     assert!(mid_metrics.peak_memory_usage() >= NUM_ALLOCS * BLOCK_SIZE as u64);
@@ -519,8 +522,14 @@ async fn test_metrics_accuracy() {
     }
 
     let final_metrics = pool_manager.metrics().await;
-    assert_eq!(final_metrics.allocation_count() - initial_alloc_count, NUM_ALLOCS);
+    assert_eq!(
-    assert_eq!(final_metrics.deallocation_count() - initial_dealloc_count, NUM_ALLOCS / 2);
+        final_metrics.allocation_count() - initial_alloc_count,
+        NUM_ALLOCS
+    );
+    assert_eq!(
+        final_metrics.deallocation_count() - initial_dealloc_count,
+        NUM_ALLOCS / 2
+    );
 
     // Cleanup remaining blocks
     for block in blocks {

crates/core/rtx-memory/src/kv_cache.rs

@@ -41,8 +41,7 @@ use crate::gpu_simple::MemoryPressure;
 use crate::paged_attention::{BlockTable, PageTable, PagedAttentionConfig, SequenceId};
 
 /// Data type for KV cache storage
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
-#[derive(Default)]
 pub enum KvDataType {
     /// 32-bit floating point
     Float32,
@@ -69,10 +68,8 @@ impl KvDataType {
     }
 }
 
-
 /// Memory tier for KV cache storage
-#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
-#[derive(Default)]
 pub enum KvMemoryTier {
     /// GPU memory (fastest)
     #[default]
@@ -83,7 +80,6 @@ pub enum KvMemoryTier {
     Disk,
 }
 
-
 /// Configuration for KV cache allocator
 #[derive(Debug, Clone)]
 pub struct KvCacheConfig {
@@ -387,8 +383,7 @@ impl KvCacheAllocator {
         num_tokens: usize,
     ) -> Result<KvCacheHandle> {
         // Check if we need to evict first
-        let pages_needed =
+        let pages_needed = num_tokens.div_ceil(self.config.page_size_tokens);
-            num_tokens.div_ceil(self.config.page_size_tokens);
         let free_pages = self.page_table.free_page_count();
 
         if pages_needed > free_pages {
@@ -665,7 +660,8 @@ impl KvCacheAllocator {
         let sequences = self.sequences.read();
 
         if let Some(cache) = sequences.get(&handle.sequence_id)
-            && cache.handle_id == handle.handle_id {
+            && cache.handle_id == handle.handle_id
+        {
             return Ok(());
         }
 
@@ -724,7 +720,9 @@ impl KvCacheAllocator {
     /// Get entropy tracker stats (if enabled)
     #[must_use]
     pub fn entropy_stats(&self) -> Option<crate::entropy_cache::EntropyStats> {
-        self.entropy_tracker.as_ref().map(super::entropy_cache::EntropyTracker::stats)
+        self.entropy_tracker
+            .as_ref()
+            .map(super::entropy_cache::EntropyTracker::stats)
     }
 
     /// Apply temporal decay to entropy scores

crates/core/rtx-memory/src/loom_tests.rs

@@ -5,8 +5,8 @@
 
 #![cfg(all(test, feature = "loom"))]
 
-use loom::sync::{Arc, Mutex};
 use loom::sync::atomic::{AtomicU64, Ordering};
+use loom::sync::{Arc, Mutex};
 use loom::thread;
 
 /// Mock memory block for loom testing.
@@ -84,13 +84,9 @@ fn test_atomic_block_id_uniqueness() {
         let g1 = Arc::clone(&id_gen);
         let g2 = Arc::clone(&id_gen);
 
-        let t1 = thread::spawn(move || {
+        let t1 = thread::spawn(move || g1.fetch_add(1, Ordering::SeqCst));
-            g1.fetch_add(1, Ordering::SeqCst)
-        });
 
-        let t2 = thread::spawn(move || {
+        let t2 = thread::spawn(move || g2.fetch_add(1, Ordering::SeqCst));
-            g2.fetch_add(1, Ordering::SeqCst)
-        });
 
         let r1 = t1.join().unwrap();
         let r2 = t2.join().unwrap();
@@ -111,13 +107,9 @@ fn test_pool_concurrent_allocate() {
         let p1 = Arc::clone(&pool);
         let p2 = Arc::clone(&pool);
 
-        let t1 = thread::spawn(move || {
+        let t1 = thread::spawn(move || p1.allocate(512));
-            p1.allocate(512)
-        });
 
-        let t2 = thread::spawn(move || {
+        let t2 = thread::spawn(move || p2.allocate(512));
-            p2.allocate(512)
-        });
 
         let id1 = t1.join().unwrap();
         let id2 = t2.join().unwrap();
@@ -143,13 +135,9 @@ fn test_pool_allocate_free_interleaving() {
         let p1 = Arc::clone(&pool);
         let p2 = Arc::clone(&pool);
 
-        let t1 = thread::spawn(move || {
+        let t1 = thread::spawn(move || p1.free(block_id));
-            p1.free(block_id)
-        });
 
-        let t2 = thread::spawn(move || {
+        let t2 = thread::spawn(move || p2.allocate(256));
-            p2.allocate(256)
-        });
 
         let freed = t1.join().unwrap();
         let new_id = t2.join().unwrap();
@@ -169,19 +157,18 @@ fn test_pool_arena_exhaustion() {
         let p1 = Arc::clone(&pool);
         let p2 = Arc::clone(&pool);
 
-        let t1 = thread::spawn(move || {
+        let t1 = thread::spawn(move || p1.allocate(512));
-            p1.allocate(512)
-        });
 
-        let t2 = thread::spawn(move || {
+        let t2 = thread::spawn(move || p2.allocate(512));
-            p2.allocate(512)
-        });
 
         let id1 = t1.join().unwrap();
         let id2 = t2.join().unwrap();
 
         // Exactly one should succeed, one should fail
-        let successes = [id1.is_some(), id2.is_some()].iter().filter(|&&x| x).count();
+        let successes = [id1.is_some(), id2.is_some()]
+            .iter()
+            .filter(|&&x| x)
+            .count();
         assert_eq!(successes, 1);
     });
 }

crates/core/rtx-memory/src/paged_attention.rs

@@ -407,8 +407,7 @@ impl PageTable {
 
         let current_pages = seq.num_pages();
         let new_total_tokens = seq.num_tokens + additional_tokens;
-        let new_total_pages =
+        let new_total_pages = new_total_tokens.div_ceil(self.config.block_size);
-            new_total_tokens.div_ceil(self.config.block_size);
         let pages_needed = new_total_pages - current_pages;
 
         if pages_needed == 0 {
@@ -697,7 +696,8 @@ impl PageTable {
                 };
 
                 if let Some(seq_id) = sequence_to_evict
-                    && let Ok(pages_freed) = self.free_sequence(seq_id) {
+                    && let Ok(pages_freed) = self.free_sequence(seq_id)
+                {
                     evicted += pages_freed;
                     let mut stats = self.stats.write();
                     stats.evictions += 1;

crates/core/rtx-memory/src/zero_optimizer/cache.rs

@@ -112,7 +112,8 @@ impl AccessPattern {
 
         // Calculate stride from previous access
         if let Some(last_access) = self.access_times.last()
-            && let Ok(duration) = now.duration_since(*last_access) {
+            && let Ok(duration) = now.duration_since(*last_access)
+        {
             self.access_stride.push(duration);
 
             // Keep only recent access strides (sliding window)

crates/core/rtx-nn/src/generic/tests/activation.rs

@@ -1,6 +1,8 @@
 //! Tests for activation layers (ReLU, GELU, LeakyReLU, ELU)
 
-use crate::generic::{GenericELU, GenericGELU, GenericLeakyReLU, GenericModule, GenericModule1D, GenericReLU};
+use crate::generic::{
+    GenericELU, GenericGELU, GenericLeakyReLU, GenericModule, GenericModule1D, GenericReLU,
+};
 use rtx_backend_cpu::{CpuBackend, CpuDevice};
 use rtx_tensor::generic::GenericTensor;
 

crates/core/rtx-nn/src/generic/tests/conv.rs

@@ -1,6 +1,8 @@
 //! Tests for convolution layers (Conv1d, Conv2d)
 
-use crate::generic::{GenericConv1d, GenericConv2d, GenericModule, GenericModule3D, GenericModule4D};
+use crate::generic::{
+    GenericConv1d, GenericConv2d, GenericModule, GenericModule3D, GenericModule4D,
+};
 use rtx_backend_cpu::{CpuBackend, CpuDevice};
 use rtx_tensor::generic::GenericTensor;
 

crates/core/rtx-nn/src/generic/tests/dropout.rs

@@ -1,6 +1,8 @@
 //! Tests for dropout layers (Dropout, Dropout2d, Dropout3d)
 
-use crate::generic::{GenericDropout, GenericDropout2d, GenericDropout3d, GenericModule, GenericModule4D};
+use crate::generic::{
+    GenericDropout, GenericDropout2d, GenericDropout3d, GenericModule, GenericModule4D,
+};
 use rtx_backend_cpu::{CpuBackend, CpuDevice};
 use rtx_tensor::generic::GenericTensor;
 

crates/core/rtx-nn/src/generic/tests/normalization.rs

@@ -12,8 +12,7 @@ use rtx_tensor::generic::GenericTensor;
 #[test]
 fn test_layer_norm_creation() {
     let device = CpuDevice::default();
-    let layer_norm: GenericLayerNorm<CpuBackend> =
+    let layer_norm: GenericLayerNorm<CpuBackend> = GenericLayerNorm::new(64, 1e-5, true, &device);
-        GenericLayerNorm::new(64, 1e-5, true, &device);
 
     assert_eq!(layer_norm.normalized_shape(), 64);
     assert_eq!(layer_norm.eps(), 1e-5);
@@ -23,8 +22,7 @@ fn test_layer_norm_creation() {
 #[test]
 fn test_layer_norm_forward_shape() {
     let device = CpuDevice::default();
-    let layer_norm: GenericLayerNorm<CpuBackend> =
+    let layer_norm: GenericLayerNorm<CpuBackend> = GenericLayerNorm::new(8, 1e-5, true, &device);
-        GenericLayerNorm::new(8, 1e-5, true, &device);
 
     let input: GenericTensor<CpuBackend, 2> = GenericTensor::randn([4, 8], &device);
     let output = layer_norm.forward(&input);
@@ -35,8 +33,7 @@ fn test_layer_norm_forward_shape() {
 #[test]
 fn test_layer_norm_normalizes() {
     let device = CpuDevice::default();
-    let layer_norm: GenericLayerNorm<CpuBackend> =
+    let layer_norm: GenericLayerNorm<CpuBackend> = GenericLayerNorm::new(4, 1e-5, false, &device);
-        GenericLayerNorm::new(4, 1e-5, false, &device);
 
     // Input with known values
     let input: GenericTensor<CpuBackend, 2> =
@@ -54,8 +51,7 @@ fn test_layer_norm_normalizes() {
 #[test]
 fn test_layer_norm_parameters() {
     let device = CpuDevice::default();
-    let layer_norm: GenericLayerNorm<CpuBackend> =
+    let layer_norm: GenericLayerNorm<CpuBackend> = GenericLayerNorm::new(64, 1e-5, true, &device);
-        GenericLayerNorm::new(64, 1e-5, true, &device);
 
     // weight: 64, bias: 64
     assert_eq!(layer_norm.num_parameters(), 128);
@@ -64,8 +60,7 @@ fn test_layer_norm_parameters() {
 #[test]
 fn test_layer_norm_no_affine() {
     let device = CpuDevice::default();
-    let layer_norm: GenericLayerNorm<CpuBackend> =
+    let layer_norm: GenericLayerNorm<CpuBackend> = GenericLayerNorm::new(64, 1e-5, false, &device);
-        GenericLayerNorm::new(64, 1e-5, false, &device);
 
     // With elementwise_affine=false, only weight (no bias)
     assert_eq!(layer_norm.num_parameters(), 64);

crates/core/rtx-nn/src/generic/tests/pooling.rs

@@ -117,8 +117,7 @@ fn test_avg_pool2d_no_parameters() {
 #[test]
 fn test_adaptive_avg_pool2d_creation() {
     let device = CpuDevice::default();
-    let pool: GenericAdaptiveAvgPool2d<CpuBackend> =
+    let pool: GenericAdaptiveAvgPool2d<CpuBackend> = GenericAdaptiveAvgPool2d::new([1, 1], &device);
-        GenericAdaptiveAvgPool2d::new([1, 1], &device);
     assert_eq!(pool.output_size(), [1, 1]);
 }
 
@@ -126,8 +125,7 @@ fn test_adaptive_avg_pool2d_creation() {
 fn test_adaptive_avg_pool2d_forward_shape() {
     let device = CpuDevice::default();
     // Global average pooling (output 1x1)
-    let pool: GenericAdaptiveAvgPool2d<CpuBackend> =
+    let pool: GenericAdaptiveAvgPool2d<CpuBackend> = GenericAdaptiveAvgPool2d::new([1, 1], &device);
-        GenericAdaptiveAvgPool2d::new([1, 1], &device);
 
     let input: GenericTensor<CpuBackend, 4> = GenericTensor::randn([2, 16, 32, 32], &device);
     let output = pool.forward_4d(&input);
@@ -139,8 +137,7 @@ fn test_adaptive_avg_pool2d_forward_shape() {
 #[test]
 fn test_adaptive_avg_pool2d_forward_non_square() {
     let device = CpuDevice::default();
-    let pool: GenericAdaptiveAvgPool2d<CpuBackend> =
+    let pool: GenericAdaptiveAvgPool2d<CpuBackend> = GenericAdaptiveAvgPool2d::new([4, 4], &device);
-        GenericAdaptiveAvgPool2d::new([4, 4], &device);
 
     let input: GenericTensor<CpuBackend, 4> = GenericTensor::randn([1, 8, 32, 32], &device);
     let output = pool.forward_4d(&input);
@@ -151,8 +148,7 @@ fn test_adaptive_avg_pool2d_forward_non_square() {
 #[test]
 fn test_adaptive_avg_pool2d_no_parameters() {
     let device = CpuDevice::default();
-    let pool: GenericAdaptiveAvgPool2d<CpuBackend> =
+    let pool: GenericAdaptiveAvgPool2d<CpuBackend> = GenericAdaptiveAvgPool2d::new([1, 1], &device);
-        GenericAdaptiveAvgPool2d::new([1, 1], &device);
     assert_eq!(pool.num_parameters(), 0);
 }
 

crates/core/rtx-nn/src/generic/tests/sequential.rs

@@ -76,8 +76,7 @@ fn test_sequential_training_mode() {
     let linear: GenericLinear<CpuBackend> = GenericLinear::new(4, 3, true, &device);
     let relu: GenericReLU<CpuBackend> = GenericReLU::new(&device);
 
-    let mut seq =
+    let mut seq = GenericSequential::new(vec![Box::new(linear), Box::new(relu)], device.clone());
-        GenericSequential::new(vec![Box::new(linear), Box::new(relu)], device.clone());
 
     assert!(seq.training());
 

crates/core/rtx-nn/src/generic/transformer.rs

@@ -396,7 +396,11 @@ impl<B: Backend<FloatElem = f32>> GenericModule<B> for GenericTransformerEncoder
     }
 
     fn num_parameters(&self) -> usize {
-        let block_params: usize = self.blocks.iter().map(super::module::GenericModule::num_parameters).sum();
+        let block_params: usize = self
+            .blocks
+            .iter()
+            .map(super::module::GenericModule::num_parameters)
+            .sum();
         block_params + self.final_norm.num_parameters()
     }
 

crates/core/rtx-nn/src/layers/conv/common.rs

@@ -103,7 +103,7 @@ pub fn calculate_fan_out(out_channels: usize, kernel_size: &[usize], groups: usi
 
 /// Padding utilities
 pub mod padding {
-    use super::{Tensor, Device, Result, NNError};
+    use super::{Device, NNError, Result, Tensor};
 
     /// Apply zero padding to a 2D tensor
     pub fn pad_2d(input: &Tensor, padding: (usize, usize), device: &Device) -> Result<Tensor> {
@@ -156,7 +156,7 @@ pub mod padding {
 
 /// Im2col utilities for efficient convolution
 pub mod im2col {
-    use super::{Tensor, Result};
+    use super::{Result, Tensor};
 
     /// Im2col for 2D convolution
     pub fn im2col_2d(

crates/core/rtx-nn/src/layers/norm/batch_norm.rs

@@ -43,7 +43,10 @@ impl BatchNormConfig {
 
     /// Set momentum for running statistics updates
     pub fn momentum(mut self, momentum: f32) -> Self {
-        assert!(!(momentum <= 0.0 || momentum >= 1.0), "Momentum must be in (0, 1), got {momentum}");
+        assert!(
+            !(momentum <= 0.0 || momentum >= 1.0),
+            "Momentum must be in (0, 1), got {momentum}"
+        );
         self.momentum = momentum;
         self
     }

crates/core/rtx-nn/src/layers/norm/layer_norm.rs

@@ -193,8 +193,14 @@ impl crate::layers::Module for LayerNorm {
             .map_err(|e| NNError::Tensor(e))?;
 
         // Propagate requires_grad from input or parameters
-        let weight_requires_grad = self.weight.as_ref().is_some_and(rtx_tensor::Tensor::requires_grad);
+        let weight_requires_grad = self
-        let bias_requires_grad = self.bias.as_ref().is_some_and(rtx_tensor::Tensor::requires_grad);
+            .weight
+            .as_ref()
+            .is_some_and(rtx_tensor::Tensor::requires_grad);
+        let bias_requires_grad = self
+            .bias
+            .as_ref()
+            .is_some_and(rtx_tensor::Tensor::requires_grad);
         if input.requires_grad() || weight_requires_grad || bias_requires_grad {
             result.set_requires_grad(true);
         }

crates/core/rtx-runtime/src/dynamic_compilation.rs

@@ -589,10 +589,12 @@ impl JITCompiler {
         } else {
             16
         };
-        let shuffle = i32::from(profile
+        let shuffle = i32::from(
+            profile
                 .tensor_shapes
                 .iter()
-            .any(|s| s.iter().product::<usize>() > 1024));
+                .any(|s| s.iter().product::<usize>() > 1024),
+        );
         Ok(format!(
             "#define WARP_SPECIALIZED 1\n#define WARP_SIZE {warp_size}\n#define SHUFFLE_OPERATIONS {shuffle}\n{code}\n"
         ))

crates/core/rtx-runtime/src/fusion/analyzer.rs

@@ -153,7 +153,11 @@ impl FusionAnalyzer {
 
     /// Check if two operation types can be fused together
     fn are_operations_fuseable(&self, op1: &FusionOpType, op2: &FusionOpType) -> bool {
-        use FusionOpType::{Add, Mul, Div, Sub, ReLU, Sigmoid, GELU, Sum, Max, Mean, Min, BroadcastScalar, Linear, LayerNorm, Dropout, AttentionCompute, AttentionApply, QuantumEncode, QuantumSimulate, QuantumDecode, SpikeEncode, SpikingCompute, SpikeToDense};
+        use FusionOpType::{
+            Add, AttentionApply, AttentionCompute, BroadcastScalar, Div, Dropout, GELU, LayerNorm,
+            Linear, Max, Mean, Min, Mul, QuantumDecode, QuantumEncode, QuantumSimulate, ReLU,
+            Sigmoid, SpikeEncode, SpikeToDense, SpikingCompute, Sub, Sum,
+        };
 
         match (op1, op2) {
             // Elementwise operations can always be fused with other elementwise
@@ -256,7 +260,10 @@ impl FusionAnalyzer {
 
     /// Estimate additional speedup from specific fusion patterns
     fn estimate_pattern_specific_speedup(&self, operations: &[FusionOp]) -> f64 {
-        use FusionOpType::{Linear, AttentionCompute, AttentionApply, GELU, ReLU, QuantumEncode, QuantumSimulate, QuantumDecode, SpikeEncode, SpikingCompute, SpikeToDense};
+        use FusionOpType::{
+            AttentionApply, AttentionCompute, GELU, Linear, QuantumDecode, QuantumEncode,
+            QuantumSimulate, ReLU, SpikeEncode, SpikeToDense, SpikingCompute,
+        };
 
         // Look for high-value patterns
         let op_types: Vec<_> = operations.iter().map(|op| &op.op_type).collect();

crates/core/rtx-runtime/src/fusion/performance.rs

@@ -102,7 +102,11 @@ impl PerformanceEstimator {
 
     /// Estimate execution time for a single operation
     fn estimate_operation_time(&self, op: &FusionOp) -> Result<f64> {
-        use FusionOpType::{Add, Sub, Copy, BroadcastScalar, Mul, Div, ReLU, Sigmoid, GELU, Sum, Max, Mean, Min, Linear, LayerNorm, Softmax, AttentionCompute, AttentionApply, QuantumEncode, QuantumSimulate, QuantumDecode, SpikeEncode, SpikingCompute, SpikeToDense, Dropout};
+        use FusionOpType::{
+            Add, AttentionApply, AttentionCompute, BroadcastScalar, Copy, Div, Dropout, GELU,
+            LayerNorm, Linear, Max, Mean, Min, Mul, QuantumDecode, QuantumEncode, QuantumSimulate,
+            ReLU, Sigmoid, Softmax, SpikeEncode, SpikeToDense, SpikingCompute, Sub, Sum,
+        };
 
         let element_count = op.dimensions.iter().product::<usize>();
         let memory_size = element_count * op.element_size;
@@ -238,7 +242,11 @@ impl PerformanceEstimator {
 
     /// Get computational complexity factor for operation type
     fn get_op_complexity(&self, op_type: &FusionOpType) -> usize {
-        use FusionOpType::{Add, Sub, Copy, BroadcastScalar, Mul, Div, ReLU, Sigmoid, GELU, Sum, Max, Mean, Min, Linear, LayerNorm, Softmax, AttentionCompute, AttentionApply, QuantumEncode, QuantumSimulate, QuantumDecode, SpikeEncode, SpikingCompute, SpikeToDense, Dropout};
+        use FusionOpType::{
+            Add, AttentionApply, AttentionCompute, BroadcastScalar, Copy, Div, Dropout, GELU,
+            LayerNorm, Linear, Max, Mean, Min, Mul, QuantumDecode, QuantumEncode, QuantumSimulate,
+            ReLU, Sigmoid, Softmax, SpikeEncode, SpikeToDense, SpikingCompute, Sub, Sum,
+        };
         match op_type {
             Add | Sub | Copy | BroadcastScalar => 1,
             Mul | Div => 2,
@@ -346,7 +354,11 @@ impl PerformanceEstimator {
 
     /// Estimate pattern-specific performance improvement
     pub fn estimate_pattern_performance(&self, pattern: &AdvancedFusionPattern) -> Result<f64> {
-        use AdvancedFusionPattern::{AttentionLayerNormFusion, FeedForwardFusion, TransformerBlockFusion, QuantumClassicalFusion, NeuromorphicClassicalFusion, ElementwiseReductionFusion, BroadcastElementwiseFusion};
+        use AdvancedFusionPattern::{
+            AttentionLayerNormFusion, BroadcastElementwiseFusion, ElementwiseReductionFusion,
+            FeedForwardFusion, NeuromorphicClassicalFusion, QuantumClassicalFusion,
+            TransformerBlockFusion,
+        };
 
         let speedup = match pattern {
             AttentionLayerNormFusion {

crates/core/rtx-runtime/src/lazy/execution.rs

@@ -230,7 +230,8 @@ impl LazyExecutor {
         // Add remaining non-fused operations
         for node_id in needed {
             if !fused_nodes.contains(&node_id)
-                && let Some(node) = graph.get_node(node_id) {
+                && let Some(node) = graph.get_node(node_id)
+            {
                 plan.add_single(node);
             }
         }

crates/core/rtx-runtime/src/metal_fusion.rs

@@ -162,8 +162,7 @@ impl MetalFusionExecutor {
             encoder.setBuffer_offset_atIndex(Some(&buffer_out), 0, 2);
 
             let count_u32 = count as u32;
-            let count_ptr =
+            let count_ptr = NonNull::new_unchecked(&raw const count_u32 as *mut std::ffi::c_void);
-                NonNull::new_unchecked(&raw const count_u32 as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(count_ptr, std::mem::size_of::<u32>(), 3);
         }
 
@@ -225,8 +224,7 @@ impl MetalFusionExecutor {
             encoder.setBuffer_offset_atIndex(Some(&buffer_out), 0, 1);
 
             let count_u32 = count as u32;
-            let count_ptr =
+            let count_ptr = NonNull::new_unchecked(&raw const count_u32 as *mut std::ffi::c_void);
-                NonNull::new_unchecked(&raw const count_u32 as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(count_ptr, std::mem::size_of::<u32>(), 2);
         }
 
@@ -289,8 +287,7 @@ impl MetalFusionExecutor {
             let scalar_ptr = NonNull::new_unchecked(&raw const scalar as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(scalar_ptr, std::mem::size_of::<f32>(), 2);
             let count_u32 = count as u32;
-            let count_ptr =
+            let count_ptr = NonNull::new_unchecked(&raw const count_u32 as *mut std::ffi::c_void);
-                NonNull::new_unchecked(&raw const count_u32 as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(count_ptr, std::mem::size_of::<u32>(), 3);
         }
 
@@ -354,8 +351,7 @@ impl MetalFusionExecutor {
         unsafe {
             encoder.setBuffer_offset_atIndex(Some(&buffer_in), 0, 0);
             encoder.setBuffer_offset_atIndex(Some(&buffer_out), 0, 1);
-            let params_ptr =
+            let params_ptr = NonNull::new_unchecked(&raw const params as *mut std::ffi::c_void);
-                NonNull::new_unchecked(&raw const params as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(params_ptr, std::mem::size_of::<ReductionParams>(), 2);
         }
 
@@ -424,8 +420,7 @@ impl MetalFusionExecutor {
         unsafe {
             encoder.setBuffer_offset_atIndex(Some(&buffer_in), 0, 0);
             encoder.setBuffer_offset_atIndex(Some(&buffer_out), 0, 1);
-            let params_ptr =
+            let params_ptr = NonNull::new_unchecked(&raw const params as *mut std::ffi::c_void);
-                NonNull::new_unchecked(&raw const params as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(params_ptr, std::mem::size_of::<SoftmaxParams>(), 2);
         }
 
@@ -494,8 +489,7 @@ impl MetalFusionExecutor {
             encoder.setBuffer_offset_atIndex(Some(&buffer_gamma), 0, 1);
             encoder.setBuffer_offset_atIndex(Some(&buffer_beta), 0, 2);
             encoder.setBuffer_offset_atIndex(Some(&buffer_out), 0, 3);
-            let params_ptr =
+            let params_ptr = NonNull::new_unchecked(&raw const params as *mut std::ffi::c_void);
-                NonNull::new_unchecked(&raw const params as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(params_ptr, std::mem::size_of::<LayerNormParams>(), 4);
         }
 
@@ -546,8 +540,7 @@ impl MetalFusionExecutor {
             let scale_ptr = NonNull::new_unchecked(&raw const scale as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(scale_ptr, std::mem::size_of::<f32>(), 3);
             let count_u32 = count as u32;
-            let count_ptr =
+            let count_ptr = NonNull::new_unchecked(&raw const count_u32 as *mut std::ffi::c_void);
-                NonNull::new_unchecked(&raw const count_u32 as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(count_ptr, std::mem::size_of::<u32>(), 4);
         }
 
@@ -619,8 +612,7 @@ impl MetalFusionExecutor {
             encoder.setBuffer_offset_atIndex(Some(&buffer_weight), 0, 1);
             encoder.setBuffer_offset_atIndex(Some(&buffer_bias), 0, 2);
             encoder.setBuffer_offset_atIndex(Some(&buffer_out), 0, 3);
-            let params_ptr =
+            let params_ptr = NonNull::new_unchecked(&raw const params as *mut std::ffi::c_void);
-                NonNull::new_unchecked(&raw const params as *mut std::ffi::c_void);
             encoder.setBytes_length_atIndex(params_ptr, std::mem::size_of::<MatmulParams>(), 4);
         }
 

crates/core/rtx-validation/src/lib.rs

@@ -272,7 +272,9 @@ pub fn is_gpu_available() -> bool {
         return false;
     }
 
-    if rtx_tensor::Device::cuda(config.device_id).is_ok() { true } else {
+    if rtx_tensor::Device::cuda(config.device_id).is_ok() {
+        true
+    } else {
         warn!(
             "GPU device {} not available, falling back to CPU",
             config.device_id
@@ -335,7 +337,9 @@ pub fn version_info() -> HashMap<String, String> {
 pub fn get_device() -> rtx_tensor::Device {
     let config = get_gpu_config();
     if config.enabled {
-        if let Ok(device) = rtx_tensor::Device::cuda(config.device_id) { device } else {
+        if let Ok(device) = rtx_tensor::Device::cuda(config.device_id) {
+            device
+        } else {
             warn!("Failed to create CUDA device, using CPU");
             rtx_tensor::Device::cuda(0).unwrap_or_default()
         }

crates/data/rtx-etl/src/stream.rs

@@ -20,8 +20,8 @@ use tokio::time::interval;
 use tracing::{debug, info, instrument};
 
 use crate::{
-    DataRecord, DataValue, Result, monitoring::EtlMetrics,
+    DataRecord, DataValue, Result, monitoring::EtlMetrics, state::StateManager,
-    state::StateManager, transform::Transformation,
+    transform::Transformation,
 };
 
 /// Configuration for stream processing

crates/integration/rtx-burn/src/session.rs

@@ -168,7 +168,8 @@ impl BurnSession {
         if self.config.cache_models {
             // Evict oldest if at capacity
             if self.models.len() >= self.config.max_cached_models
-                && let Some(key) = self.models.keys().next().cloned() {
+                && let Some(key) = self.models.keys().next().cloned()
+            {
                 debug!("Evicting cached model: {}", key);
                 self.models.remove(&key);
             }

crates/integration/rtx-candle/src/backend.rs

@@ -55,8 +55,7 @@ impl CandleBackend {
 }
 
 /// Device wrapper for Candle
-#[derive(Debug, Clone, Serialize, Deserialize)]
+#[derive(Debug, Clone, Serialize, Deserialize, Default)]
-#[derive(Default)]
 pub struct CandleDevice {
     /// Backend type
     pub backend: CandleBackend,
@@ -64,7 +63,6 @@ pub struct CandleDevice {
     pub ordinal: usize,
 }
 
-
 impl CandleDevice {
     /// Create a CPU device
     pub fn cpu() -> Self {

crates/integration/rtx-candle/src/session.rs

@@ -160,7 +160,8 @@ impl CandleSession {
         // Cache if enabled
         if self.config.cache_models {
             if self.models.len() >= self.config.max_cached_models
-                && let Some(key) = self.models.keys().next().cloned() {
+                && let Some(key) = self.models.keys().next().cloned()
+            {
                 self.models.remove(&key);
             }
             self.models.insert(path_str.clone(), model);

crates/integration/rtx-candle/src/tensor_bridge.rs

@@ -9,9 +9,9 @@ use tracing::debug;
 /// Convert an rtx-tensor Tensor to Candle tensor
 pub fn rtx_to_candle(tensor: &Tensor, device: &CandleDevice) -> Result<CandleTensor> {
     let shape: Vec<usize> = tensor.shape().to_vec();
-    let data = tensor.to_vec_f32().map_err(|e| {
+    let data = tensor
-        CandleError::tensor_conversion(format!("Failed to extract f32 data: {e}"))
+        .to_vec_f32()
-    })?;
+        .map_err(|e| CandleError::tensor_conversion(format!("Failed to extract f32 data: {e}")))?;
 
     debug!("Converting rtx tensor {:?} to Candle", shape);
 

crates/models/rtx-nlg/src/generation/mod.rs

@@ -507,7 +507,7 @@ impl TextGenerator {
 
 /// Utility functions for generation
 pub mod utils {
-    use super::{Tensor, Result, GenerationConfig};
+    use super::{GenerationConfig, Result, Tensor};
     use rand::Rng;
 
     /// Apply temperature scaling to logits

crates/models/rtx-timeseries/src/lib.rs

@@ -284,7 +284,9 @@ pub fn is_gpu_available() -> bool {
         return false;
     }
 
-    if rtx_tensor::Device::cuda(config.device_id).is_ok() { true } else {
+    if rtx_tensor::Device::cuda(config.device_id).is_ok() {
+        true
+    } else {
         warn!(
             "GPU device {} not available, falling back to CPU",
             config.device_id
@@ -347,7 +349,9 @@ pub fn version_info() -> HashMap<String, String> {
 pub fn get_device() -> rtx_tensor::Device {
     let config = get_gpu_config();
     if config.enabled {
-        if let Ok(device) = rtx_tensor::Device::cuda(config.device_id) { device } else {
+        if let Ok(device) = rtx_tensor::Device::cuda(config.device_id) {
+            device
+        } else {
             warn!("Failed to create CUDA device, using CPU");
             rtx_tensor::Device::Cuda(0)
         }

crates/models/rtx-vision/src/layers/positional.rs

@@ -67,7 +67,9 @@ impl PositionalEncoding {
 
     /// Forward pass - add positional encoding to input
     pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
-        if let PositionalEncodingType::None = self.encoding_type { Ok(input.clone()) } else {
+        if let PositionalEncodingType::None = self.encoding_type {
+            Ok(input.clone())
+        } else {
             let Some(ref pos_emb) = self.position_embeddings else {
                 return Ok(input.clone());
             };

crates/models/rtx-vision/src/preprocessing/augmentation.rs

@@ -3,7 +3,7 @@
 use crate::Tensor;
 use crate::error::{Result, VisionError};
 use crate::preprocessing::{ImageProcessor, ImageTensor};
-use rand::{thread_rng, Rng};
+use rand::{Rng, thread_rng};
 
 /// Augmentation operations for data augmentation during training
 #[derive(Clone, Debug)]

crates/production/rtx-hub/src/huggingface.rs

@@ -428,7 +428,12 @@ pub fn convert_hf_config(hf_config: &HFModelConfig) -> HubResult<RTXModelConfig>
     let model_type = hf_config
         .model_type
         .as_deref()
-        .or_else(|| hf_config.architectures.first().map(std::string::String::as_str))
+        .or_else(|| {
+            hf_config
+                .architectures
+                .first()
+                .map(std::string::String::as_str)
+        })
         .unwrap_or("unknown");
 
     let architecture = RTXArchitecture::from_hf_model_type(model_type);
@@ -496,7 +501,8 @@ pub fn convert_hf_config(hf_config: &HFModelConfig) -> HubResult<RTXModelConfig>
     let bos_token_id = hf_config.bos_token_id.unwrap_or(1);
     let eos_token_ids = hf_config
         .eos_token_id
-        .as_ref().map_or_else(|| vec![2], EosTokenId::all);
+        .as_ref()
+        .map_or_else(|| vec![2], EosTokenId::all);
     let pad_token_id = hf_config.pad_token_id;
 
     Ok(RTXModelConfig {

crates/production/rtx-hub/src/safetensors.rs

@@ -163,7 +163,11 @@ impl SafeTensors {
 
     /// Get the names of all tensors in the file.
     pub fn tensor_names(&self) -> Vec<&str> {
-        self.header.tensors.keys().map(std::string::String::as_str).collect()
+        self.header
+            .tensors
+            .keys()
+            .map(std::string::String::as_str)
+            .collect()
     }
 
     /// Get information about a specific tensor.

crates/production/rtx-inference/build.rs

@@ -19,7 +19,8 @@ fn main() {
             } else {
                 None
             }
-        }).map_or_else(|| "unknown".to_string(), |s| s.trim().to_string());
+        })
+        .map_or_else(|| "unknown".to_string(), |s| s.trim().to_string());
 
     println!("cargo:rustc-env=RUSTYTORCH_GIT_COMMIT={git_commit}");
 
@@ -35,7 +36,8 @@ fn main() {
                 } else {
                     None
                 }
-            }).map_or_else(|| "unknown".to_string(), |s| s.trim().to_string())
+            })
+            .map_or_else(|| "unknown".to_string(), |s| s.trim().to_string())
     });
 
     println!("cargo:rustc-env=RUSTYTORCH_RUST_VERSION={rustc_version}");

crates/production/rtx-inference/src/cache/paged_kv_cache.rs

@@ -399,7 +399,10 @@ impl PagedKvCache {
         let result_keys = if key_tensors.len() == 1 {
             // SAFETY: We just checked len() == 1, so next() will succeed
             let Some(key) = key_tensors.into_iter().next() else {
-                return Err(InferenceError::kv_cache_error("get", "Key tensor disappeared"));
+                return Err(InferenceError::kv_cache_error(
+                    "get",
+                    "Key tensor disappeared",
+                ));
             };
             key
         } else if !key_tensors.is_empty() {
@@ -416,7 +419,10 @@ impl PagedKvCache {
         let result_values = if value_tensors.len() == 1 {
             // SAFETY: We just checked len() == 1, so next() will succeed
             let Some(value) = value_tensors.into_iter().next() else {
-                return Err(InferenceError::kv_cache_error("get", "Value tensor disappeared"));
+                return Err(InferenceError::kv_cache_error(
+                    "get",
+                    "Value tensor disappeared",
+                ));
             };
             value
         } else if !value_tensors.is_empty() {

crates/production/rtx-monitoring/src/alerts.rs

@@ -487,7 +487,7 @@ impl AlertManager {
 
 /// Pre-defined alert rules for common ML scenarios
 pub mod presets {
-    use super::{AlertRule, AlertCondition, AlertSeverity};
+    use super::{AlertCondition, AlertRule, AlertSeverity};
 
     /// High GPU memory usage alert
     pub fn high_gpu_memory(threshold_percent: f64) -> AlertRule {

crates/production/rtx-onnx-codegen/src/codegen/forward_gen.rs

@@ -4,9 +4,9 @@ use proc_macro2::TokenStream;
 use quote::quote;
 
 use crate::codegen::CodegenConfig;
+use crate::error::Result;
 use crate::ir::OnnxGraph;
 use crate::ops::{generate_node_code, sanitize_name};
-use crate::error::Result;
 
 /// Generator for the forward function.
 pub struct ForwardGenerator<'a> {
@@ -43,7 +43,9 @@ impl<'a> ForwardGenerator<'a> {
     }
 
     fn generate_input_params(&self, graph: &OnnxGraph) -> Result<TokenStream> {
-        let params: Vec<TokenStream> = graph.inputs.iter()
+        let params: Vec<TokenStream> = graph
+            .inputs
+            .iter()
             .filter(|input| !graph.is_initializer(&input.name))
             .map(|input| {
                 let name = quote::format_ident!("{}", sanitize_name(&input.name));
@@ -91,7 +93,9 @@ impl<'a> ForwardGenerator<'a> {
             let output_name = quote::format_ident!("{}", sanitize_name(&graph.outputs[0].name));
             Ok(quote! { Ok(#output_name) })
         } else {
-            let output_names: Vec<_> = graph.outputs.iter()
+            let output_names: Vec<_> = graph
+                .outputs
+                .iter()
                 .map(|o| quote::format_ident!("{}", sanitize_name(&o.name)))
                 .collect();
             Ok(quote! { Ok((#(#output_names),*)) })

crates/production/rtx-onnx-codegen/src/codegen/mod.rs

@@ -5,19 +5,19 @@
 //! - A constructor that loads weights from SafeTensors
 //! - A forward method that implements the model computation
 
-mod module_gen;
 mod forward_gen;
+mod module_gen;
 mod weight_gen;
 
-pub use module_gen::ModuleGenerator;
 pub use forward_gen::ForwardGenerator;
+pub use module_gen::ModuleGenerator;
 pub use weight_gen::WeightGenerator;
 
 use proc_macro2::TokenStream;
 use quote::quote;
 
-use crate::ir::OnnxGraph;
 use crate::error::Result;
+use crate::ir::OnnxGraph;
 
 /// Configuration for code generation.
 #[derive(Debug, Clone)]

crates/production/rtx-onnx-codegen/src/codegen/module_gen.rs

@@ -4,9 +4,9 @@ use proc_macro2::TokenStream;
 use quote::quote;
 
 use crate::codegen::CodegenConfig;
+use crate::error::Result;
 use crate::ir::OnnxGraph;
 use crate::ops::sanitize_name;
-use crate::error::Result;
 
 /// Generator for the model struct definition.
 pub struct ModuleGenerator<'a> {

crates/production/rtx-onnx-codegen/src/codegen/weight_gen.rs

@@ -4,9 +4,9 @@ use proc_macro2::TokenStream;
 use quote::quote;
 
 use crate::codegen::CodegenConfig;
+use crate::error::Result;
 use crate::ir::OnnxGraph;
 use crate::ops::sanitize_name;
-use crate::error::Result;
 
 /// Generator for weight loading code.
 pub struct WeightGenerator<'a> {
@@ -106,7 +106,9 @@ impl<'a> WeightGenerator<'a> {
     }
 
     fn generate_field_initializers(&self, graph: &OnnxGraph) -> Result<TokenStream> {
-        let inits: Vec<TokenStream> = graph.initializers.keys()
+        let inits: Vec<TokenStream> = graph
+            .initializers
+            .keys()
             .map(|name| {
                 let field_name = quote::format_ident!("{}", sanitize_name(name));
                 quote! { #field_name, }

crates/production/rtx-onnx-codegen/src/ir/graph.rs

@@ -74,8 +74,14 @@ impl OnnxGraph {
             value_info.insert(info.name.clone(), info);
         }
 
-        log::debug!("Graph '{}': {} inputs, {} outputs, {} nodes, {} initializers",
+        log::debug!(
-            name, inputs.len(), outputs.len(), nodes.len(), initializers.len());
+            "Graph '{}': {} inputs, {} outputs, {} nodes, {} initializers",
+            name,
+            inputs.len(),
+            outputs.len(),
+            nodes.len(),
+            initializers.len()
+        );
 
         Ok(Self {
             name,
@@ -111,11 +117,8 @@ impl OnnxGraph {
     /// Validate the graph structure.
     pub fn validate(&self) -> Result<()> {
         // Check all inputs are defined
-        let mut defined: std::collections::HashSet<&str> = self
+        let mut defined: std::collections::HashSet<&str> =
-            .inputs
+            self.inputs.iter().map(|i| i.name.as_str()).collect();
-            .iter()
-            .map(|i| i.name.as_str())
-            .collect();
 
         // Add initializers
         for name in self.initializers.keys() {

crates/production/rtx-onnx-codegen/src/ir/tensor.rs

@@ -150,9 +150,7 @@ impl Tensor {
                     .collect();
                 Ok(TensorData::Int64(data))
             }
-            DataType::UInt8 | DataType::Bool => {
+            DataType::UInt8 | DataType::Bool => Ok(TensorData::UInt8(raw.to_vec())),
-                Ok(TensorData::UInt8(raw.to_vec()))
-            }
             _ => {
                 // Store as raw bytes for unsupported types
                 Ok(TensorData::Raw(raw.to_vec()))

crates/production/rtx-onnx-codegen/src/ir/types.rs

@@ -173,7 +173,10 @@ impl Shape {
 
     /// Get static dimensions, panics if dynamic.
     pub fn static_dims(&self) -> Vec<i64> {
-        self.dims.iter().map(|d| d.expect("Dynamic dimension")).collect()
+        self.dims
+            .iter()
+            .map(|d| d.expect("Dynamic dimension"))
+            .collect()
     }
 
     /// Total number of elements (for static shapes).
@@ -218,7 +221,9 @@ impl ValueInfo {
     pub fn from_proto(proto: &ValueInfoProto) -> Result<Self> {
         let name = proto.name.clone();
 
-        let type_proto = proto.r#type.as_ref()
+        let type_proto = proto
+            .r#type
+            .as_ref()
             .ok_or_else(|| Error::InvalidModel(format!("Missing type for {}", name)))?;
 
         let (dtype, shape) = Self::extract_tensor_type(type_proto)?;
@@ -252,7 +257,9 @@ impl ValueInfo {
                 };
                 Ok((dtype, shape))
             }
-            _ => Err(Error::InvalidModel("Non-tensor types not supported".to_string())),
+            _ => Err(Error::InvalidModel(
+                "Non-tensor types not supported".to_string(),
+            )),
         }
     }
 }

crates/production/rtx-onnx-codegen/src/model_gen.rs

@@ -2,12 +2,12 @@
 //!
 //! Provides a fluent API for generating Rust code from ONNX models.
 
-use std::path::{Path, PathBuf};
 use std::fs;
+use std::path::{Path, PathBuf};
 
 use crate::codegen::{CodeGenerator, CodegenConfig, Visibility};
-use crate::parser::OnnxParser;
 use crate::error::{Error, Result};
+use crate::parser::OnnxParser;
 
 /// Builder for generating Rust code from ONNX models.
 ///
@@ -136,16 +136,19 @@ impl ModelGen {
     /// - Code generation fails
     /// - The output file can't be written
     pub fn run(&self) -> Result<PathBuf> {
-        let input_path = self.input_path.as_ref()
+        let input_path = self
+            .input_path
+            .as_ref()
             .ok_or_else(|| Error::CodeGen("No input path specified".into()))?;
 
         // Determine output path
-        let out_dir = self.out_dir.clone()
+        let out_dir = self.out_dir.clone().unwrap_or_else(|| {
-            .unwrap_or_else(|| std::env::var("OUT_DIR").map_or_else(|_| PathBuf::from("."), PathBuf::from));
+            std::env::var("OUT_DIR").map_or_else(|_| PathBuf::from("."), PathBuf::from)
+        });
 
-        let out_file = self.out_file.clone()
+        let out_file = self.out_file.clone().unwrap_or_else(|| {
-            .unwrap_or_else(|| {
+            input_path
-                input_path.file_stem()
+                .file_stem()
                 .and_then(|s| s.to_str())
                 .unwrap_or("model")
                 .to_string()
@@ -210,7 +213,9 @@ impl ModelGen {
     ///
     /// Useful for testing or generating code programmatically.
     pub fn generate_string(&self) -> Result<String> {
-        let input_path = self.input_path.as_ref()
+        let input_path = self
+            .input_path
+            .as_ref()
             .ok_or_else(|| Error::CodeGen("No input path specified".into()))?;
 
         let graph = OnnxParser::parse_file(input_path)?;

crates/production/rtx-onnx-codegen/src/ops/activation.rs

@@ -3,9 +3,9 @@
 use proc_macro2::TokenStream;
 use quote::quote;
 
-use crate::ir::Node;
-use crate::error::Result;
 use super::sanitize_name;
+use crate::error::Result;
+use crate::ir::Node;
 
 /// Generate softmax operation.
 pub fn generate_softmax(node: &Node) -> Result<TokenStream> {

crates/production/rtx-onnx-codegen/src/ops/arithmetic.rs

@@ -3,9 +3,9 @@
 use proc_macro2::TokenStream;
 use quote::quote;
 
-use crate::ir::Node;
-use crate::error::Result;
 use super::sanitize_name;
+use crate::error::Result;
+use crate::ir::Node;
 
 /// Generate binary arithmetic operation.
 pub fn generate_binary_op(node: &Node, op: &str) -> Result<TokenStream> {

crates/production/rtx-onnx-codegen/src/ops/conv.rs

@@ -3,9 +3,9 @@
 use proc_macro2::TokenStream;
 use quote::quote;
 
-use crate::ir::{Node, OnnxGraph};
-use crate::error::Result;
 use super::sanitize_name;
+use crate::error::Result;
+use crate::ir::{Node, OnnxGraph};
 
 /// Default strides for 2D convolution.
 const DEFAULT_STRIDES: &[i64] = &[1, 1];
@@ -78,7 +78,9 @@ pub fn generate_conv_transpose(node: &Node, _graph: &OnnxGraph) -> Result<TokenS
     // Get attributes
     let strides = node.get_ints("strides").unwrap_or(DEFAULT_STRIDES);
     let pads = node.get_ints("pads").unwrap_or(DEFAULT_PADS);
-    let output_padding = node.get_ints("output_padding").unwrap_or(DEFAULT_OUTPUT_PADDING);
+    let output_padding = node
+        .get_ints("output_padding")
+        .unwrap_or(DEFAULT_OUTPUT_PADDING);
     let dilations = node.get_ints("dilations").unwrap_or(DEFAULT_DILATIONS);
     let group = node.get_int("group").unwrap_or(1) as usize;
 

crates/production/rtx-onnx-codegen/src/ops/matmul.rs

@@ -3,9 +3,9 @@
 use proc_macro2::TokenStream;
 use quote::quote;
 
-use crate::ir::{Node, OnnxGraph};
-use crate::error::Result;
 use super::sanitize_name;
+use crate::error::Result;
+use crate::ir::{Node, OnnxGraph};
 
 /// Generate MatMul operation.
 pub fn generate_matmul(node: &Node) -> Result<TokenStream> {

crates/production/rtx-onnx-codegen/src/ops/mod.rs

@@ -2,16 +2,16 @@
 //!
 //! This module contains code generators for individual ONNX operators.
 
-mod arithmetic;
 mod activation;
+mod arithmetic;
 mod conv;
 mod matmul;
 mod normalization;
 mod pooling;
 mod reshape;
 
-pub use arithmetic::*;
 pub use activation::*;
+pub use arithmetic::*;
 pub use conv::*;
 pub use matmul::*;
 pub use normalization::*;
@@ -21,8 +21,8 @@ pub use reshape::*;
 use proc_macro2::TokenStream;
 use quote::quote;
 
-use crate::ir::{Node, NodeKind, OnnxGraph};
 use crate::error::{Error, Result};
+use crate::ir::{Node, NodeKind, OnnxGraph};
 
 /// Generate code for a single node.
 pub fn generate_node_code(node: &Node, graph: &OnnxGraph) -> Result<TokenStream> {
@@ -183,12 +183,12 @@ pub fn sanitize_name(name: &str) -> String {
 
     // Handle Rust keywords
     match result.as_str() {
-        "type" | "fn" | "let" | "mut" | "ref" | "self" | "Self" | "mod" | "pub" | "use" |
+        "type" | "fn" | "let" | "mut" | "ref" | "self" | "Self" | "mod" | "pub" | "use"
-        "struct" | "enum" | "trait" | "impl" | "for" | "while" | "loop" | "if" | "else" |
+        | "struct" | "enum" | "trait" | "impl" | "for" | "while" | "loop" | "if" | "else"
-        "match" | "return" | "break" | "continue" | "move" | "box" | "where" | "async" |
+        | "match" | "return" | "break" | "continue" | "move" | "box" | "where" | "async"
-        "await" | "dyn" | "abstract" | "become" | "const" | "crate" | "do" | "extern" |
+        | "await" | "dyn" | "abstract" | "become" | "const" | "crate" | "do" | "extern"
-        "final" | "in" | "macro" | "override" | "priv" | "static" | "super" | "try" |
+        | "final" | "in" | "macro" | "override" | "priv" | "static" | "super" | "try"
-        "typeof" | "unsafe" | "unsized" | "virtual" | "yield" => {
+        | "typeof" | "unsafe" | "unsized" | "virtual" | "yield" => {
             result.push('_');
         }
         _ => {}
@@ -223,7 +223,9 @@ fn generate_min_max(node: &Node, op: &str) -> Result<TokenStream> {
     let output = &node.outputs[0];
     let out_ident = quote::format_ident!("{}", sanitize_name(output));
 
-    let input_idents: Vec<_> = node.inputs.iter()
+    let input_idents: Vec<_> = node
+        .inputs
+        .iter()
         .filter(|s| !s.is_empty())
         .map(|s| quote::format_ident!("{}", sanitize_name(s)))
         .collect();

crates/production/rtx-onnx-codegen/src/ops/normalization.rs

@@ -3,9 +3,9 @@
 use proc_macro2::TokenStream;
 use quote::quote;
 
-use crate::ir::{Node, OnnxGraph};
-use crate::error::Result;
 use super::sanitize_name;
+use crate::error::Result;
+use crate::ir::{Node, OnnxGraph};
 
 /// Generate BatchNormalization operation.
 pub fn generate_batch_norm(node: &Node, graph: &OnnxGraph) -> Result<TokenStream> {

crates/production/rtx-onnx-codegen/src/ops/pooling.rs

@@ -3,9 +3,9 @@
 use proc_macro2::TokenStream;
 use quote::quote;
 
-use crate::ir::Node;
-use crate::error::Result;
 use super::sanitize_name;
+use crate::error::Result;
+use crate::ir::Node;
 
 /// Default kernel shape for pooling.
 const DEFAULT_KERNEL: &[i64] = &[2, 2];

crates/production/rtx-onnx-codegen/src/ops/reshape.rs

@@ -3,9 +3,9 @@
 use proc_macro2::TokenStream;
 use quote::quote;
 
-use crate::ir::{Node, OnnxGraph};
-use crate::error::Result;
 use super::sanitize_name;
+use crate::error::Result;
+use crate::ir::{Node, OnnxGraph};
 
 /// Generate Reshape operation.
 pub fn generate_reshape(node: &Node, graph: &OnnxGraph) -> Result<TokenStream> {
@@ -87,7 +87,9 @@ pub fn generate_concat(node: &Node) -> Result<TokenStream> {
     let axis = node.get_int("axis").unwrap_or(0);
 
     // Collect all input identifiers
-    let input_idents: Vec<_> = node.inputs.iter()
+    let input_idents: Vec<_> = node
+        .inputs
+        .iter()
         .filter(|s| !s.is_empty())
         .map(|s| {
             let ident = quote::format_ident!("{}", sanitize_name(s));
@@ -165,7 +167,9 @@ pub fn generate_split(node: &Node) -> Result<TokenStream> {
     let axis = node.get_int("axis").unwrap_or(0);
 
     // Output identifiers
-    let output_idents: Vec<_> = node.outputs.iter()
+    let output_idents: Vec<_> = node
+        .outputs
+        .iter()
         .filter(|s| !s.is_empty())
         .map(|s| quote::format_ident!("{}", sanitize_name(s)))
         .collect();
@@ -203,22 +207,25 @@ pub fn generate_slice(node: &Node, graph: &OnnxGraph) -> Result<TokenStream> {
         let starts_name = &node.inputs[1];
         let ends_name = &node.inputs[2];
 
-        if let (Some(starts_tensor), Some(ends_tensor)) =
+        if let (Some(starts_tensor), Some(ends_tensor)) = (
-            (graph.get_initializer(starts_name), graph.get_initializer(ends_name))
+            graph.get_initializer(starts_name),
-        {
+            graph.get_initializer(ends_name),
+        ) {
             let starts = starts_tensor.shape.static_dims();
             let ends = ends_tensor.shape.static_dims();
 
             // Optional axes and steps
             let axes = if node.inputs.len() > 3 && !node.inputs[3].is_empty() {
-                graph.get_initializer(&node.inputs[3])
+                graph
+                    .get_initializer(&node.inputs[3])
                     .map(|t| t.shape.static_dims())
             } else {
                 None
             };
 
             let steps = if node.inputs.len() > 4 && !node.inputs[4].is_empty() {
-                graph.get_initializer(&node.inputs[4])
+                graph
+                    .get_initializer(&node.inputs[4])
                     .map(|t| t.shape.static_dims())
             } else {
                 None

crates/production/rtx-onnx-codegen/src/parser/model_parser.rs

@@ -41,11 +41,13 @@ impl OnnxParser {
             .graph
             .ok_or_else(|| Error::InvalidModel("Model has no graph".to_string()))?;
 
-        log::info!("Model: {} (IR version {})",
+        log::info!(
+            "Model: {} (IR version {})",
             model.producer_name,
             model.ir_version
         );
-        log::info!("Graph: {} nodes, {} initializers",
+        log::info!(
+            "Graph: {} nodes, {} initializers",
             graph.node.len(),
             graph.initializer.len()
         );

crates/production/rtx-onnx/src/session.rs

@@ -121,8 +121,8 @@ impl OnnxSession {
             model_bytes.len()
         );
 
-        let mut builder = Session::builder()
+        let mut builder =
-            .map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
+            Session::builder().map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
 
         builder = builder
             .with_optimization_level(config.optimization_level.into())
@@ -137,8 +137,8 @@ impl OnnxSession {
 
     /// Create session from file with configuration
     fn create_session(path: &Path, config: &OnnxSessionConfig) -> Result<Session> {
-        let mut builder = Session::builder()
+        let mut builder =
-            .map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
+            Session::builder().map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
 
         // Set optimization level
         builder = builder

crates/production/rtx-serving-api/src/continuous_batch/controller.rs

@@ -170,7 +170,9 @@ impl ContinuousBatchingController {
                         }
 
                         // Safe: front() returned Some, so pop_front() will succeed
-                        let Some(mut request) = queue.pop_front() else { break };
+                        let Some(mut request) = queue.pop_front() else {
+                            break;
+                        };
                         total_tokens += request.total_tokens();
                         total_memory += request.estimated_memory;
                         request.state = RequestState::Processing;

crates/production/rtx-serving-api/src/grammar_sampling.rs

@@ -169,7 +169,9 @@ impl ContextFreeGrammar {
 
             for rule in &self.rules {
                 let lhs = &rule.left_hand_side;
-                let old_size = first_sets.get(lhs).map_or(0, std::collections::HashSet::len);
+                let old_size = first_sets
+                    .get(lhs)
+                    .map_or(0, std::collections::HashSet::len);
 
                 if rule.is_epsilon() {
                     first_sets
@@ -216,7 +218,9 @@ impl ContextFreeGrammar {
                     }
                 }
 
-                let new_size = first_sets.get(lhs).map_or(0, std::collections::HashSet::len);
+                let new_size = first_sets
+                    .get(lhs)
+                    .map_or(0, std::collections::HashSet::len);
                 if new_size > old_size {
                     changed = true;
                 }
@@ -247,7 +251,8 @@ impl ContextFreeGrammar {
         for rule in &self.rules {
             for symbol in &rule.right_hand_side {
                 if let GrammarSymbol::NonTerminal(nt) = symbol
-                    && !self.non_terminals.contains(nt) {
+                    && !self.non_terminals.contains(nt)
+                {
                     errors.push(GrammarError {
                         message: format!("Undefined non-terminal '{nt}' in rule"),
                         error_type: GrammarErrorType::UndefinedSymbol,
@@ -298,7 +303,8 @@ impl ContextFreeGrammar {
             for rule in self.get_rules(&symbol) {
                 for rhs_symbol in &rule.right_hand_side {
                     if let GrammarSymbol::NonTerminal(nt) = rhs_symbol
-                        && !reachable.contains(nt) {
+                        && !reachable.contains(nt)
+                    {
                         reachable.insert(nt.clone());
                         queue.push_back(nt.clone());
                     }
@@ -424,7 +430,8 @@ impl GrammarGuidedSampler {
         // Filter token probabilities by expected terminals
         for (token, prob) in token_probabilities {
             if expected_terminals.contains(token)
-                && let Some(new_state) = self.advance_state(state, token)? {
+                && let Some(new_state) = self.advance_state(state, token)?
+            {
                 candidates.push((token.clone(), *prob, new_state));
             }
         }

crates/production/rtx-serving-api/src/multi_model.rs

@@ -14,12 +14,7 @@ use dashmap::DashMap;
 use parking_lot::{Mutex, RwLock};
 use rand::{Rng, thread_rng};
 use serde::{Deserialize, Serialize};
-use std::{
+use std::{collections::HashMap, hash::Hash, sync::Arc, time::Duration};
-    collections::HashMap,
-    hash::Hash,
-    sync::Arc,
-    time::Duration,
-};
 use tokio::time::{Instant, sleep};
 use uuid::Uuid;
 
@@ -457,8 +452,7 @@ impl MultiModelManager {
 
                 // Check reservation result
                 if let Err(e) = reserve_result {
-                    instance.state =
+                    instance.state = ModelState::Error(format!("Resource reservation failed: {e}"));
-                        ModelState::Error(format!("Resource reservation failed: {e}"));
                     return;
                 }
 
@@ -511,7 +505,8 @@ impl MultiModelManager {
 
         // Check circuit breaker
         if let Some(cb) = self.circuit_breakers.get(&instance_id)
-            && !cb.can_execute() {
+            && !cb.can_execute()
+        {
             return Err(anyhow!("Circuit breaker open for model {instance_id}"));
         }
 

crates/production/rtx-serving-api/src/sampling_strategies.rs

@@ -9,9 +9,9 @@
 //! - Custom sampling strategies with pluggable algorithms
 
 use anyhow::{Result, anyhow};
+use rand::SeedableRng;
 use rand::distributions::{Distribution, WeightedIndex};
 use rand::rngs::StdRng;
-use rand::SeedableRng;
 use serde::{Deserialize, Serialize};
 use std::{cmp::Ordering, collections::HashMap};
 

crates/production/rtx-streaming/src/adaptive_processing/batch.rs

@@ -84,7 +84,9 @@ impl DynamicBatchController {
     pub fn get_optimal_batch_size(&self, stream_id: &str) -> usize {
         self.current_batch_sizes
             .get(stream_id)
-            .map_or(self.config.initial_batch_size, |state| state.current_size.load(Ordering::Relaxed))
+            .map_or(self.config.initial_batch_size, |state| {
+                state.current_size.load(Ordering::Relaxed)
+            })
     }
 
     /// Update performance metrics

crates/production/rtx-streaming/src/state_management/mod.rs

@@ -20,9 +20,9 @@ pub mod state_handle;
 // External Imports
 // ============================================================================
 
+use crate::StreamingResult;
 use crate::types_final::FailureDetector;
 use crate::types_processing::StateCoordinator;
-use crate::StreamingResult;
 
 // ============================================================================
 // Re-exports from config module

crates/production/rtx-wasm-inference/src/lib.rs

@@ -50,13 +50,14 @@ pub use tokenizer::*;
 pub use webgpu::*;
 
 // Re-export core tensor types (no_std compatible)
-pub use tensor_core::{DType, TensorCore, TensorError};
-pub use tensor_core::simd::{SIMD_AVAILABLE, SIMD_WIDTH};
 pub use tensor_core::backend::{
-    BackendLimits, BackendPreference, BackendType, UnifiedBackend,
+    BackendLimits, BackendPreference, BackendType, UnifiedBackend, available_backends,
-    available_backends,
 };
-pub use tensor_core::wasm_api::{WasmTensor, WasmBackend, get_backend_info, is_simd_available, get_simd_width};
+pub use tensor_core::simd::{SIMD_AVAILABLE, SIMD_WIDTH};
+pub use tensor_core::wasm_api::{
+    WasmBackend, WasmTensor, get_backend_info, get_simd_width, is_simd_available,
+};
+pub use tensor_core::{DType, TensorCore, TensorError};
 
 /// Initialize panic hook for better error messages in browser console
 #[wasm_bindgen(start)]
@@ -556,7 +557,10 @@ impl WasmInferenceEngine {
     /// Get memory usage estimate
     #[wasm_bindgen]
     pub fn memory_usage(&self) -> usize {
-        let model_mem = self.model.as_ref().map_or(0, model::WasmModel::memory_usage);
+        let model_mem = self
+            .model
+            .as_ref()
+            .map_or(0, model::WasmModel::memory_usage);
         let cache_mem = self
             .kv_cache
             .as_ref()

crates/production/rtx-wasm-inference/src/tensor_core/backend.rs

@@ -344,11 +344,7 @@ impl UnifiedBackend {
     }
 
     /// Tensor matrix multiplication
-    pub fn tensor_matmul(
+    pub fn tensor_matmul(&self, a: &TensorCore, b: &TensorCore) -> Result<TensorCore, TensorError> {
-        &self,
-        a: &TensorCore,
-        b: &TensorCore,
-    ) -> Result<TensorCore, TensorError> {
         a.matmul(b)
     }
 
@@ -439,7 +435,10 @@ mod tests {
     fn test_backend_creation() {
         let backend = UnifiedBackend::new();
         // Should always succeed with CPU fallback
-        assert!(backend.backend_type() == BackendType::Cpu || backend.backend_type() == BackendType::WebGpu);
+        assert!(
+            backend.backend_type() == BackendType::Cpu
+                || backend.backend_type() == BackendType::WebGpu
+        );
     }
 
     #[test]

crates/production/rtx-wasm-inference/src/tensor_core/wasm_api.rs

@@ -262,7 +262,10 @@ impl WasmTensor {
     /// Matrix multiplication (2D tensors only)
     #[wasm_bindgen]
     pub fn matmul(&self, other: &WasmTensor) -> Result<WasmTensor, JsError> {
-        let inner = self.inner.matmul(&other.inner).map_err(tensor_error_to_js)?;
+        let inner = self
+            .inner
+            .matmul(&other.inner)
+            .map_err(tensor_error_to_js)?;
         Ok(WasmTensor { inner })
     }
 
@@ -350,21 +353,30 @@ impl WasmBackend {
     /// Tensor addition using this backend
     #[wasm_bindgen(js_name = tensorAdd)]
     pub fn tensor_add(&self, a: &WasmTensor, b: &WasmTensor) -> Result<WasmTensor, JsError> {
-        let inner = self.inner.tensor_add(&a.inner, &b.inner).map_err(tensor_error_to_js)?;
+        let inner = self
+            .inner
+            .tensor_add(&a.inner, &b.inner)
+            .map_err(tensor_error_to_js)?;
         Ok(WasmTensor { inner })
     }
 
     /// Tensor multiplication using this backend
     #[wasm_bindgen(js_name = tensorMul)]
     pub fn tensor_mul(&self, a: &WasmTensor, b: &WasmTensor) -> Result<WasmTensor, JsError> {
-        let inner = self.inner.tensor_mul(&a.inner, &b.inner).map_err(tensor_error_to_js)?;
+        let inner = self
+            .inner
+            .tensor_mul(&a.inner, &b.inner)
+            .map_err(tensor_error_to_js)?;
         Ok(WasmTensor { inner })
     }
 
     /// Tensor matmul using this backend
     #[wasm_bindgen(js_name = tensorMatmul)]
     pub fn tensor_matmul(&self, a: &WasmTensor, b: &WasmTensor) -> Result<WasmTensor, JsError> {
-        let inner = self.inner.tensor_matmul(&a.inner, &b.inner).map_err(tensor_error_to_js)?;
+        let inner = self
+            .inner
+            .tensor_matmul(&a.inner, &b.inner)
+            .map_err(tensor_error_to_js)?;
         Ok(WasmTensor { inner })
     }
 
@@ -380,7 +392,9 @@ impl WasmBackend {
     #[wasm_bindgen(js_name = tensorLayerNorm)]
     pub fn tensor_layer_norm(&self, input: &WasmTensor, eps: Option<f32>) -> WasmTensor {
         WasmTensor {
-            inner: self.inner.tensor_layer_norm(&input.inner, eps.unwrap_or(1e-5)),
+            inner: self
+                .inner
+                .tensor_layer_norm(&input.inner, eps.unwrap_or(1e-5)),
         }
     }
 }

crates/specialized/rtx-fea/src/assembly/dof_mapping.rs

@@ -284,7 +284,9 @@ impl AdvancedDofNumbering {
                     .iter()
                     .filter(|&&neighbor| !visited.contains(&neighbor))
                     .map(|&neighbor| {
-                        let degree = adjacency.get(&neighbor).map_or(0, std::collections::HashSet::len);
+                        let degree = adjacency
+                            .get(&neighbor)
+                            .map_or(0, std::collections::HashSet::len);
                         (neighbor, degree)
                     })
                     .collect();

crates/specialized/rtx-fea/src/assembly/sparse_matrix.rs

@@ -585,7 +585,10 @@ impl std::ops::Add<&SparseMatrix> for &SparseMatrix {
     type Output = SparseMatrix;
 
     fn add(self, rhs: &SparseMatrix) -> Self::Output {
-        assert!(!(self.nrows != rhs.nrows || self.ncols != rhs.ncols), "Matrix dimensions must match for addition");
+        assert!(
+            !(self.nrows != rhs.nrows || self.ncols != rhs.ncols),
+            "Matrix dimensions must match for addition"
+        );
 
         let mut result = SparseMatrix::new(self.nrows, self.ncols);
 
@@ -938,7 +941,10 @@ impl std::ops::Add<&Self> for SparseMatrix {
     type Output = Self;
 
     fn add(self, rhs: &Self) -> Self::Output {
-        assert!(!(self.nrows != rhs.nrows || self.ncols != rhs.ncols), "Matrix dimensions must match for addition");
+        assert!(
+            !(self.nrows != rhs.nrows || self.ncols != rhs.ncols),
+            "Matrix dimensions must match for addition"
+        );
 
         let mut result = Self::new(self.nrows, self.ncols);
 

crates/specialized/rtx-fea/src/elements/jacobian.rs

@@ -402,7 +402,10 @@ impl JacobianQuality {
         }
 
         let determinants: Vec<f64> = jacobians.iter().map(|j| j.determinant).collect();
-        let conditions: Vec<f64> = jacobians.iter().map(JacobianEval::condition_number).collect();
+        let conditions: Vec<f64> = jacobians
+            .iter()
+            .map(JacobianEval::condition_number)
+            .collect();
 
         let min_determinant = determinants.iter().fold(f64::INFINITY, |a, &b| a.min(b));
         let max_determinant = determinants

crates/specialized/rtx-fea/src/lib.rs

@@ -59,7 +59,6 @@
 //! - `cantilever_beam`: Classical beam bending problem
 //! - Advanced examples with nonlinear materials and contact
 
-
 pub mod analysis;
 pub mod assembly;
 pub mod boundary;

crates/specialized/rtx-fea/src/materials/mod.rs

@@ -585,7 +585,7 @@ impl GpuMaterialOps {
 
 /// Utility functions for material calculations.
 pub mod utils {
-    use super::{Vector6, Vector3};
+    use super::{Vector3, Vector6};
 
     /// Convert engineering strain to Voigt notation.
     pub fn engineering_to_voigt(engineering_strain: &Vector6<f64>) -> Vector6<f64> {

crates/specialized/rtx-fea/src/mesh/partitioning/partitioning_types.rs

@@ -186,7 +186,10 @@ impl PartitioningStats {
         }
         let total_nodes = all_nodes.len();
 
-        let element_counts: Vec<usize> = partitions.iter().map(MeshPartition::element_count).collect();
+        let element_counts: Vec<usize> = partitions
+            .iter()
+            .map(MeshPartition::element_count)
+            .collect();
         let max_elements = element_counts.iter().max().copied().unwrap_or(0);
         let min_elements = element_counts.iter().min().copied().unwrap_or(0);
         let avg_elements = if num_partitions > 0 {
@@ -203,7 +206,10 @@ impl PartitioningStats {
         };
 
         // Count total interface nodes
-        let total_interface_nodes: usize = partitions.iter().map(MeshPartition::boundary_node_count).sum();
+        let total_interface_nodes: usize = partitions
+            .iter()
+            .map(MeshPartition::boundary_node_count)
+            .sum();
 
         // Estimate communication volume
         let communication_volume: usize = partitions

crates/specialized/rtx-fea/src/mesh/topology/topology_refinement.rs

@@ -550,9 +550,8 @@ impl MeshRefinement {
                         let key = if ni < nj { (ni, nj) } else { (nj, ni) };
 
                         *edge_midpoints.entry(key).or_insert_with(|| {
-                            let mid_coords = (&mesh.nodes[&ni].coordinates
+                            let mid_coords =
-                                + &mesh.nodes[&nj].coordinates)
+                                (&mesh.nodes[&ni].coordinates + &mesh.nodes[&nj].coordinates) * 0.5;
-                                * 0.5;
                             let mid_node = Node {
                                 coordinates: mid_coords,
                                 dofs: mesh.nodes[&ni].dofs.clone(),

crates/specialized/rtx-geom/src/layers/sparse_gcn.rs

@@ -121,9 +121,9 @@ impl SparseGCNLayer {
 
         tracing::info!(
             "Sparse GCN setup complete. cuSPARSE acceleration: {}",
-            self.message_passing
+            self.message_passing.as_ref().is_some_and(
-                .as_ref()
+                super::super::sparse_message::SparseMessagePassing::has_cusparse_acceleration
-                .is_some_and(super::super::sparse_message::SparseMessagePassing::has_cusparse_acceleration)
+            )
         );
 
         Ok(())