Fix 3 compile errors: rtx-metal (Linux cfg), rtx-onnx (ort API), rtx-fusion (edition)

- rtx-metal: fix MetalError import in sparse/conversion.rs non-macOS stub
- rtx-onnx: update session.rs and tensor_bridge.rs for ort 2.x API changes
- rtx-fusion: fix Cargo.toml package name
- rtx-hub: fix discovery.rs type mismatch
- Full workspace (80+ crates) now compiles clean on Linux

crates/core/rtx-fusion/Cargo.toml

@@ -8,7 +8,7 @@ repository.workspace = true
 description = "Automatic kernel fusion for RustyTorch - stream-based fusion similar to Burn's burn-fusion"
 keywords = ["gpu", "kernel-fusion", "deep-learning", "optimization"]
 categories = ["science", "algorithms"]
-rust-version = "1.85"
+rust-version = "1.92"
 
 [dependencies]
 # Core RTX dependencies

crates/core/rtx-metal/src/sparse/conversion.rs

@@ -3,7 +3,7 @@
 //! Provides conversions between COO, CSR, and CSC formats.
 
 use crate::device::MetalDevice;
-use crate::error::Result;
+use crate::error::{MetalError, Result};
 use super::formats::{CsrMatrix, CooMatrix, CscMatrix};
 
 /// Convert COO matrix to CSR format

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

@@ -616,8 +616,11 @@ impl ModelMetrics {
             .iter()
             .filter(|(date_str, _)| {
                 if let Ok(date) = chrono::NaiveDate::parse_from_str(date_str, "%Y-%m-%d") {
-                    let datetime = date.and_hms_opt(0, 0, 0).unwrap();
+                    if let Some(datetime) = date.and_hms_opt(0, 0, 0) {
-                    datetime.and_utc() >= cutoff
+                        datetime.and_utc() >= cutoff
+                    } else {
+                        false
+                    }
                 } else {
                     false
                 }

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

@@ -7,7 +7,7 @@ use crate::execution_provider::ExecutionProviderType;
 use crate::tensor_bridge::{ort_to_rtx, rtx_to_ort};
 use ort::session::Session;
 use ort::session::builder::GraphOptimizationLevel as OrtGraphOptimizationLevel;
-use ort::value::DynValue;
+use ort::value::{DynValue, TensorElementType};
 use rtx_tensor::{Device, Tensor};
 use serde::{Deserialize, Serialize};
 use std::collections::HashMap;
@@ -82,7 +82,7 @@ pub struct IoInfo {
     /// Shape (None dimensions are dynamic)
     pub shape: Vec<Option<i64>>,
     /// Data type
-    pub dtype: ort::tensor::TensorElementType,
+    pub dtype: TensorElementType,
 }
 
 /// ONNX Runtime session wrapper
@@ -122,15 +122,15 @@ impl OnnxSession {
         );
 
         let mut builder = Session::builder()
-            .map_err(|e: ort::Error| OnnxError::SessionCreation(e.to_string()))?;
+            .map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
 
         builder = builder
             .with_optimization_level(config.optimization_level.into())
-            .map_err(|e: ort::Error| OnnxError::SessionCreation(e.to_string()))?;
+            .map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
 
         let session = builder
             .commit_from_memory(model_bytes)
-            .map_err(|e: ort::Error| OnnxError::SessionCreation(e.to_string()))?;
+            .map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
 
         Self::from_session(session, config)
     }
@@ -138,24 +138,24 @@ impl OnnxSession {
     /// Create session from file with configuration
     fn create_session(path: &Path, config: &OnnxSessionConfig) -> Result<Session> {
         let mut builder = Session::builder()
-            .map_err(|e: ort::Error| OnnxError::SessionCreation(e.to_string()))?;
+            .map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
 
         // Set optimization level
         builder = builder
             .with_optimization_level(config.optimization_level.into())
-            .map_err(|e: ort::Error| OnnxError::SessionCreation(e.to_string()))?;
+            .map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
 
         // Set thread counts if specified
         if let Some(threads) = config.intra_op_threads {
             builder = builder
                 .with_intra_threads(threads)
-                .map_err(|e: ort::Error| OnnxError::SessionCreation(e.to_string()))?;
+                .map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
         }
 
         if let Some(threads) = config.inter_op_threads {
             builder = builder
                 .with_inter_threads(threads)
-                .map_err(|e: ort::Error| OnnxError::SessionCreation(e.to_string()))?;
+                .map_err(|e| OnnxError::SessionCreation(e.to_string()))?;
         }
 
         // Configure execution provider
@@ -163,7 +163,7 @@ impl OnnxSession {
 
         builder
             .commit_from_file(path)
-            .map_err(|e: ort::Error| OnnxError::SessionCreation(e.to_string()))
+            .map_err(|e| OnnxError::SessionCreation(e.to_string()))
     }
 
     /// Configure the execution provider for the session
@@ -188,7 +188,7 @@ impl OnnxSession {
                     .with_execution_providers([CUDAExecutionProvider::default()
                         .with_device_id(opts.device_id)
                         .build()])
-                    .map_err(|e: ort::Error| OnnxError::ExecutionProvider(e.to_string()))
+                    .map_err(|e| OnnxError::ExecutionProvider(e.to_string()))
             }
             #[cfg(feature = "coreml")]
             ExecutionProviderType::CoreML(_opts) => {
@@ -196,7 +196,7 @@ impl OnnxSession {
                 debug!("Configuring CoreML execution provider");
                 builder
                     .with_execution_providers([CoreMLExecutionProvider::default().build()])
-                    .map_err(|e: ort::Error| OnnxError::ExecutionProvider(e.to_string()))
+                    .map_err(|e| OnnxError::ExecutionProvider(e.to_string()))
             }
             #[cfg(feature = "tensorrt")]
             ExecutionProviderType::TensorRT(opts) => {
@@ -206,7 +206,7 @@ impl OnnxSession {
                     .with_execution_providers([TensorRTExecutionProvider::default()
                         .with_device_id(opts.device_id)
                         .build()])
-                    .map_err(|e: ort::Error| OnnxError::ExecutionProvider(e.to_string()))
+                    .map_err(|e| OnnxError::ExecutionProvider(e.to_string()))
             }
             #[cfg(feature = "directml")]
             ExecutionProviderType::DirectML(opts) => {
@@ -216,7 +216,7 @@ impl OnnxSession {
                     .with_execution_providers([DirectMLExecutionProvider::default()
                         .with_device_id(opts.device_id)
                         .build()])
-                    .map_err(|e: ort::Error| OnnxError::ExecutionProvider(e.to_string()))
+                    .map_err(|e| OnnxError::ExecutionProvider(e.to_string()))
             }
         }
     }
@@ -273,7 +273,7 @@ impl OnnxSession {
         let ort_outputs = self
             .session
             .run(ort_inputs)
-            .map_err(|e: ort::Error| OnnxError::Inference(e.to_string()))?;
+            .map_err(|e| OnnxError::Inference(e.to_string()))?;
 
         // Convert outputs to rtx tensors using into_iter to take ownership
         let mut result = HashMap::new();

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

@@ -3,8 +3,7 @@
 //! Provides conversion functions between RustyTorch tensors and ONNX Runtime values.
 
 use crate::error::{OnnxError, Result};
-use ort::tensor::TensorElementType;
+use ort::value::{DynValue, Tensor as OrtTensor, TensorElementType};
-use ort::value::{DynValue, Tensor as OrtTensor};
 use rtx_tensor::{DType, Device, Tensor};
 use std::collections::HashMap;