Merge pull request #213 from JohanMabille/upgrade

Upgrade

Author: JohanMabille

.appveyor.yml

@@ -21,7 +21,7 @@ install:
   - conda update -q conda
   - conda info -a
   - conda install cmake -c conda-forge
-  - conda install xtensor=0.23.0 -c conda-forge
+  - conda install xtensor=0.24.0 -c conda-forge
   - conda install m2w64-openblas -c msys2
   # Patch OpenBLASConfig.cmake
   - ps: (Get-Content $Env:MINICONDA\Library\mingw-w64\lib\cmake\openblas\OpenBLASConfig.cmake).replace('mingw64', 'mingw-w64') | Set-Content $Env:MINICONDA\Library\mingw-w64\lib\cmake\openblas\OpenBLASConfig.cmake

.azure-pipelines/azure-pipelines-linux-clang.yml

@@ -2,12 +2,6 @@ jobs:
   - job: 'Linux_0'
     strategy:
       matrix:
-        clang_4:
-          llvm_version: '4.0'
-          blas: 'OpenBLAS'
-        clang_5:
-          llvm_version: '5.0'
-          blas: 'OpenBLAS'
         clang_6:
           llvm_version: '6.0'
           blas: 'OpenBLAS'
@@ -24,7 +18,7 @@ jobs:
           llvm_version: '10'
           blas: 'OpenBLAS'
     pool:
-      vmImage: ubuntu-16.04
+      vmImage: ubuntu-18.04
     variables:
       CC: clang-$(llvm_version)
       CXX: clang++-$(llvm_version)

.azure-pipelines/azure-pipelines-linux-gcc.yml

@@ -2,14 +2,8 @@ jobs:
   - job: 'Linux_1'
     strategy:
       matrix:
-        gcc_4_OpenBLAS:
-          gcc_version: '4.9'
-          blas: 'OpenBLAS'
-        gcc_5_OpenBLAS:
-          gcc_version: '5'
-          blas: 'OpenBLAS'
         gcc_6_OpenBLAS:
-          gcc_version: '5'
+          gcc_version: '6'
           blas: 'OpenBLAS'
         gcc_6_bound_checks:
           gcc_version: '6'
@@ -24,12 +18,6 @@ jobs:
         gcc_9_OpenBLAS:
           gcc_version: '9'
           blas: 'OpenBLAS'
-        gcc_4_mkl:
-          gcc_version: '4.9'
-          blas: 'mkl'
-        gcc_5_mkl:
-          gcc_version: '5'
-          blas: 'mkl'
         gcc_6_mkl:
           gcc_version: '6'
           blas: 'mkl'
@@ -43,7 +31,7 @@ jobs:
           gcc_version: '9'
           blas: 'mkl'
     pool:
-      vmImage: ubuntu-16.04
+      vmImage: ubuntu-18.04
     variables:
       CC: gcc-$(gcc_version)
       CXX: g++-$(gcc_version)

.azure-pipelines/azure-pipelines-win.yml

@@ -1,5 +1,5 @@
 jobs:
-  - job: 'build'
+  - job: 'Windows clang'
     pool:
       vmImage: 'vs2017-win2016'
     timeoutInMinutes: 360
@@ -36,7 +36,7 @@ jobs:
           conda install cmake==3.14.0 ^
                         mkl-devel ^
                         ninja ^
-                        xtensor=0.23.0 ^
+                        xtensor=0.24.0 ^
                         python=3.6
           conda list
         displayName: "Install conda packages"

CMakeLists.txt

@@ -44,7 +44,7 @@ message(STATUS "xtensor-blas v${${PROJECT_NAME}_VERSION}")
 # Dependencies
 # ============
 
-set(xtensor_REQUIRED_VERSION 0.23.0)
+set(xtensor_REQUIRED_VERSION 0.24.0)
 if(TARGET xtensor)
     set(xtensor_VERSION ${XTENSOR_VERSION_MAJOR}.${XTENSOR_VERSION_MINOR}.${XTENSOR_VERSION_PATCH})
     # Note: This is not SEMVER compatible comparison

README.md

@@ -64,7 +64,7 @@ http://xtensor-blas.readthedocs.io/
 
 | `xtensor-blas`  | `xtensor` |
 |-----------------|-----------|
-| master          |  ^0.23.3  |
+| master          |  ^0.24.0  |
 | 0.19.2          |  ^0.23.3  |
 | 0.19.1          |  ^0.23.3  |
 | 0.19.0          |  ^0.23.0  |

environment-dev.yml

@@ -3,4 +3,4 @@ channels:
   - conda-forge
 dependencies:
   - cmake
-  - xtensor=0.23.3
+  - xtensor=0.24.0

include/xtensor-blas/xlinalg.hpp

@@ -1326,6 +1326,35 @@ namespace linalg
         return std::make_tuple(std::move(std::get<1>(result)), std::move(std::get<2>(result)), std::move(std::get<3>(result)));
     }
 
+    namespace detail
+    {
+        template <bool>
+        struct conj_eval_impl
+        {
+            template <class E>
+            static const E& run(const E& e)
+            {
+                return e;
+            }
+        };
+
+        template <>
+        struct conj_eval_impl<true>
+        {
+            template <class E>
+            static auto run(const E& e)
+            {
+                return xt::conj(e);
+            }
+        };
+
+        template <class E>
+        inline decltype(auto) conj_eval(const E& e)
+        {
+            return conj_eval_impl<xtl::is_complex<typename E::value_type>::value>::run(e);
+        }
+    }
+
     /**
      * Calculate Moore-Rose pseudo inverse using LAPACK SVD.
      */
@@ -1335,7 +1364,7 @@ namespace linalg
         using value_type = typename T::value_type;
         const auto& dA = A.derived_cast();
 
-        xtensor<value_type, 2, layout_type::column_major> M = xt::conj(dA);
+        xtensor<value_type, 2, layout_type::column_major> M = detail::conj_eval(dA);
 
         auto gesdd_res = svd(M, false, true);