Fix semantic merge confict in vortex-tensor

vortex-tensor/src/encodings/turboquant/array/data.rs

@@ -14,8 +14,8 @@ use vortex_error::vortex_ensure_eq;
 
 use crate::encodings::turboquant::array::slots::Slot;
 use crate::encodings::turboquant::vtable::TurboQuant;
-use crate::utils::extension_element_ptype;
-use crate::utils::extension_list_size;
+use crate::utils::tensor_element_ptype;
+use crate::utils::tensor_list_size;
 
 /// TurboQuant array data.
 ///
@@ -117,7 +117,7 @@ impl TurboQuantData {
 
         let dimension = dtype
             .as_extension_opt()
-            .and_then(|ext| extension_list_size(ext).ok())
+            .and_then(|ext| tensor_list_size(ext).ok())
             .vortex_expect("dtype must be a Vector extension type with FixedSizeList storage");
 
         let bit_width = if centroids.is_empty() {
@@ -154,7 +154,7 @@ impl TurboQuantData {
         rotation_signs: &ArrayRef,
     ) -> VortexResult<()> {
         let ext = TurboQuant::validate_dtype(dtype)?;
-        let dimension = extension_list_size(ext)?;
+        let dimension = tensor_list_size(ext)?;
         let padded_dim = dimension.next_power_of_two();
 
         // Codes must be a non-nullable FixedSizeList<u8> with list_size == padded_dim.
@@ -209,7 +209,7 @@ impl TurboQuantData {
 
         // Norms dtype must match the element ptype of the Vector, with the parent's nullability.
         // Norms carry the validity of the entire TurboQuant array.
-        let element_ptype = extension_element_ptype(ext)?;
+        let element_ptype = tensor_element_ptype(ext)?;
         let expected_norms_dtype = DType::Primitive(element_ptype, dtype.nullability());
         vortex_ensure_eq!(
             *norms.dtype(),

vortex-tensor/src/encodings/turboquant/array/scheme.rs

@@ -14,8 +14,8 @@ use vortex_error::VortexResult;
 use crate::encodings::turboquant::TurboQuant;
 use crate::encodings::turboquant::TurboQuantConfig;
 use crate::encodings::turboquant::turboquant_encode;
-use crate::utils::extension_element_ptype;
-use crate::utils::extension_list_size;
+use crate::utils::tensor_element_ptype;
+use crate::utils::tensor_list_size;
 
 /// TurboQuant compression scheme for [`Vector`] extension types.
 ///
@@ -59,8 +59,8 @@ impl Scheme for TurboQuantScheme {
         let len = data.array().len();
 
         let ext = TurboQuant::validate_dtype(dtype)?;
-        let element_ptype = extension_element_ptype(ext)?;
-        let dimension = extension_list_size(ext)?;
+        let element_ptype = tensor_element_ptype(ext)?;
+        let dimension = tensor_list_size(ext)?;
 
         Ok(estimate_compression_ratio(
             element_ptype.bit_width(),

vortex-tensor/src/encodings/turboquant/compute/cosine_similarity.rs

@@ -43,7 +43,7 @@ use vortex_error::vortex_ensure_eq;
 
 use crate::encodings::turboquant::TurboQuant;
 use crate::encodings::turboquant::array::float_from_f32;
-use crate::utils::extension_element_ptype;
+use crate::utils::tensor_element_ptype;
 
 /// Compute the per-row unit-norm dot products in f32 (centroids are always f32).
 ///
@@ -107,7 +107,7 @@ pub fn cosine_similarity_quantized_column(
         "TurboQuant quantized dot product requires matching dimensions",
     );
 
-    let element_ptype = extension_element_ptype(lhs.dtype().as_extension())?;
+    let element_ptype = tensor_element_ptype(lhs.dtype().as_extension())?;
     let validity = lhs.norms().validity()?.and(rhs.norms().validity()?)?;
     let dots = compute_unit_dots(&lhs, &rhs, ctx)?;
 
@@ -145,7 +145,7 @@ pub fn dot_product_quantized_column(
         "TurboQuant quantized dot product requires matching dimensions",
     );
 
-    let element_ptype = extension_element_ptype(lhs.dtype().as_extension())?;
+    let element_ptype = tensor_element_ptype(lhs.dtype().as_extension())?;
     let validity = lhs.norms().validity()?.and(rhs.norms().validity()?)?;
     let dots = compute_unit_dots(&lhs, &rhs, ctx)?;
     let num_rows = lhs.norms().len();

vortex-tensor/src/encodings/turboquant/decompress.rs

@@ -22,7 +22,7 @@ use vortex_error::VortexResult;
 use crate::encodings::turboquant::TurboQuant;
 use crate::encodings::turboquant::array::float_from_f32;
 use crate::encodings::turboquant::array::rotation::RotationMatrix;
-use crate::utils::extension_element_ptype;
+use crate::utils::tensor_element_ptype;
 
 /// Decompress a `TurboQuantArray` into a [`Vector`] extension array.
 ///
@@ -38,7 +38,7 @@ pub fn execute_decompress(
     let padded_dim = array.padded_dim() as usize;
     let num_rows = array.norms().len();
     let ext_dtype = array.dtype().as_extension().clone();
-    let element_ptype = extension_element_ptype(&ext_dtype)?;
+    let element_ptype = tensor_element_ptype(&ext_dtype)?;
 
     if num_rows == 0 {
         let fsl_validity = Validity::from(ext_dtype.storage_dtype().nullability());

vortex-tensor/src/encodings/turboquant/vtable.rs

@@ -39,8 +39,8 @@ use crate::encodings::turboquant::array::slots::Slot;
 use crate::encodings::turboquant::compute::rules::PARENT_KERNELS;
 use crate::encodings::turboquant::compute::rules::RULES;
 use crate::encodings::turboquant::decompress::execute_decompress;
-use crate::utils::extension_element_ptype;
-use crate::utils::extension_list_size;
+use crate::utils::tensor_element_ptype;
+use crate::utils::tensor_list_size;
 use crate::vector::Vector;
 
 /// Encoding marker type for TurboQuant.
@@ -66,7 +66,7 @@ impl TurboQuant {
                 vortex_err!("TurboQuant dtype must be a Vector extension type, got {dtype}")
             })?;
 
-        let dimension = extension_list_size(ext)?;
+        let dimension = tensor_list_size(ext)?;
         vortex_ensure!(
             dimension >= Self::MIN_DIMENSION,
             "TurboQuant requires dimension >= {}, got {dimension}",
@@ -113,7 +113,7 @@ impl VTable for TurboQuant {
                 vortex_err!("TurboQuant dtype must be a Vector extension type, got {dtype}")
             })?;
 
-        let dimension = extension_list_size(ext)?;
+        let dimension = tensor_list_size(ext)?;
         vortex_ensure!(
             dimension >= Self::MIN_DIMENSION,
             "TurboQuant requires dimension >= {}, got {dimension}",
@@ -208,8 +208,8 @@ impl VTable for TurboQuant {
 
         // Validate and derive dimension and element ptype from the Vector extension dtype.
         let ext = TurboQuant::validate_dtype(dtype)?;
-        let dimension = extension_list_size(ext)?;
-        let element_ptype = extension_element_ptype(ext)?;
+        let dimension = tensor_list_size(ext)?;
+        let element_ptype = tensor_element_ptype(ext)?;
 
         let padded_dim = dimension.next_power_of_two();
 

vortex-tensor/src/scalar_fns/l2_norm.rs

@@ -133,7 +133,7 @@ impl ScalarFnVTable for L2Norm {
         // (e.g., if the input extension has f64 elements).
         if let Some(tq) = input_ref.as_opt::<TurboQuant>() {
             let ext = input_ref.dtype().as_extension();
-            let target_ptype = extension_element_ptype(ext)?;
+            let target_ptype = tensor_element_ptype(ext)?;
             let norms: PrimitiveArray = tq.norms().clone().execute(ctx)?;
             let target_dtype = DType::Primitive(target_ptype, input_ref.dtype().nullability());
             return norms.into_array().cast(target_dtype);