Auto stash before merge of "dev_bdd_selector" and "origin/dev_bdd_selector"

poulpy-core/src/operations/glwe.rs

@@ -1,18 +1,26 @@
 use poulpy_hal::{
     api::{
-        ModuleN, VecZnxAdd, VecZnxAddInplace, VecZnxCopy, VecZnxMulXpMinusOne, VecZnxMulXpMinusOneInplace, VecZnxNegateInplace,
+        Convolution, ModuleN, VecZnxAdd, VecZnxAddInplace, VecZnxCopy, VecZnxMulXpMinusOne, VecZnxMulXpMinusOneInplace, VecZnxNegateInplace, VecZnxNormalize, VecZnxNormalizeInplace, VecZnxRotate, VecZnxRotateInplace, VecZnxRshInplace, VecZnxSub, VecZnxSubInplace, VecZnxSubNegateInplace, VecZnxZero
-        VecZnxNormalize, VecZnxNormalizeInplace, VecZnxRotate, VecZnxRotateInplace, VecZnxRshInplace, VecZnxSub,
-        VecZnxSubInplace, VecZnxSubNegateInplace, VecZnxZero,
     },
     layouts::{Backend, Module, Scratch, VecZnx, ZnxZero},
     reference::vec_znx::vec_znx_rotate_inplace_tmp_bytes,
 };
 
 use crate::{
-    ScratchTakeCore,
+    layouts::{GLWEInfos, GLWETensor, GLWETensorToMut, GLWEToMut, GLWEToRef, LWEInfos, SetGLWEInfos, TorusPrecision, GLWE}, ScratchTakeCore
-    layouts::{GLWE, GLWEInfos, GLWEToMut, GLWEToRef, LWEInfos, SetGLWEInfos, TorusPrecision},
 };
 
+pub trait GLWETensoring<BE: Backend> where Self: Convolution<BE>, Scratch<BE>: ScratchTakeCore<BE> {
+    fn glwe_tensor<R, A, B>(&self, res: &mut R, res_scale: i64, a: &A, b: &B, scratch: &mut Scratch<BE>) where R: GLWETensorToMut, A: GLWEToRef, B: GLWEToRef{
+
+        let res: &mut GLWETensor<&mut [u8]> = &mut res.to_mut();
+        let a: &mut GLWE<&[u8]> = &mut a.to_ref();
+        let b: &GLWE<&[u8]> = &b.to_ref();
+
+        self.bivariate_convolution(res.data_mut(), res_scale, a, b, scratch);
+    }
+}
+
 pub trait GLWEAdd
 where
     Self: ModuleN + VecZnxAdd + VecZnxCopy + VecZnxAddInplace,

poulpy-hal/src/api/convolution.rs

@@ -123,6 +123,7 @@ where
             self.svp_apply_dft_to_dft(&mut res_tmp, 0, &ppol, 0, b, b_col);
             self.vec_znx_dft_add_scaled_inplace(res, res_col, &res_tmp, 0, -(1 + a_limb as i64) + k);
         }
+  
     }
 
     fn bivariate_convolution_single<R, A, B>(