tensor: add narrow() view and relax is_contiguous for size-1 dims

narrow(dim, start, len) creates a zero-copy slice along any dimension.
is_contiguous() now ignores stride mismatches on dimensions of size 1,
since those dimensions are never stepped. This avoids unnecessary GPU
strided copies when slicing fused projection outputs at batch=1.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

crates/xserv-tensor/src/shape.rs

@@ -18,12 +18,21 @@ pub fn contiguous_strides(shape: &[usize]) -> Dims {
 }
 
 /// Check if the given strides represent contiguous (row-major) layout for the shape.
+/// A stride mismatch on a dimension of size 1 is allowed because that
+/// dimension is never stepped.
 pub fn is_contiguous(shape: &[usize], strides: &[usize]) -> bool {
     if shape.is_empty() {
         return true;
     }
-    let expected = contiguous_strides(shape);
-    strides == expected.as_slice()
+    let ndim = shape.len();
+    let mut expected_stride = 1usize;
+    for d in (0..ndim).rev() {
+        if shape[d] != 1 && strides[d] != expected_stride {
+            return false;
+        }
+        expected_stride *= shape[d];
+    }
+    true
 }
 
 /// Total number of elements given a shape.