diff --git a/Grid/serialisation/BaseIO.h b/Grid/serialisation/BaseIO.h
index 7793b061..11dfee0e 100644
--- a/Grid/serialisation/BaseIO.h
+++ b/Grid/serialisation/BaseIO.h
@@ -42,6 +42,7 @@ namespace Grid {
         : std::integral_constant<bool, std::is_arithmetic<T>::value> {};
 
     // Eigen tensors can be composed of arithmetic scalar and complex types
+    // TODO Support Grid::comples from GPU port
     template<typename T> struct is_scalar : std::integral_constant<bool,
                             std::is_arithmetic<T>::value || is_complex<T>::value> {};
 
@@ -202,10 +203,10 @@ namespace Grid {
     Scalar * pScalar = ET.data();
     for( std::size_t j = 0; j < NumScalars; j++ ) {
       // if constexpr is C++ 17 ... but otherwise need two specialisations (Container vs Scalar)
-      if constexpr ( InnerRank == 0 ) {
+      if constexpr ( EigenIO::is_scalar<Scalar>::value ) {
         lambda( * pScalar, Seq++, MyIndex );
       } else {
-        for( typename Scalar::scalar_type &Source : * pScalar ) {
+        for( typename EigenIO::Traits<Scalar>::scalar_type &Source : * pScalar ) {
           lambda(Source, Seq++, MyIndex );
           // Now increment SubIndex
           for( auto i = rank + InnerRank - 1; i != rank - 1 && ++MyIndex[i] == Dims[i]; i-- )
@@ -244,7 +245,7 @@ namespace Grid {
       std::cout << pName;
     for( auto i = 0 ; i < rank; i++ ) std::cout << "[" << dims[i] << "]";
     std::cout << " in memory order:" << std::endl;
-    for_all( t, [&](typename Traits::scalar_type &c, typename T::Index index, const std::array<size_t, T::NumIndices + Traits::rank_non_trivial> Dims ){
+    for_all( t, [&](typename Traits::scalar_type &c, typename T::Index index, const std::array<size_t, T::NumIndices + Traits::rank_non_trivial> &Dims ){
       std::cout << "  ";
       for( auto dim : Dims )
         std::cout << "[" << dim << "]";
@@ -253,6 +254,16 @@ namespace Grid {
     std::cout << "========================================" << std::endl;
   }
 
+  template <typename T>
+  typename std::enable_if<!EigenIO::is_tensor<T>::value, void>::type
+  dump_tensor_func(T &t, const char * pName = nullptr)
+  {
+    std::cout << "Dumping non-tensor object ";
+    if( pName )
+      std::cout << pName;
+    std::cout << "=" << t;
+  }
+
   // Helper to dump a tensor in memory order
   // Kind of superfluous given the above ... just keeping in case I need to fall back to this
 #define DumpMemoryOrder(args...) DumpMemoryOrder_func(args)
@@ -379,12 +390,12 @@ namespace Grid {
     template <typename ETensor>
     typename std::enable_if<EigenIO::is_tensor_variable<ETensor>::value, void>::type
     Reshape(ETensor &t, const std::array<typename ETensor::Index, ETensor::NumDimensions> &dims );
-    template <typename ETensor>
+    /*template <typename ETensor>
     typename std::enable_if<EigenIO::is_tensor_fixed<ETensor>::value, std::size_t>::type
     DimSize(ETensor &t, std::size_t dim );
     template <typename ETensor>
     typename std::enable_if<EigenIO::is_tensor_variable<ETensor>::value, std::size_t>::type
-    DimSize(ETensor &t, std::size_t dim );
+    DimSize(ETensor &t, std::size_t dim );*/
   protected:
     template <typename U>
     void fromString(U &output, const std::string &s);
@@ -677,43 +688,52 @@ namespace Grid {
   Reader<T>::read(const std::string &s, ETensor &output)
   {
     // alias to element type
-    using Scalar = typename EigenIO::Traits<typename ETensor::Scalar>::scalar_type;
+    using Container = typename ETensor::Scalar;
+    using Traits = EigenIO::Traits<Container>;
+    using Scalar = typename Traits::scalar_type;
 
     // read the (flat) data and dimensionality
     std::vector<std::size_t>   dimData;
     std::vector<Scalar>  buf;
     upcast->readMultiDim( s, buf, dimData );
     // Make sure that the number of elements read matches dimensions read
-    const std::size_t NumElements{buf.size()};
-    std::size_t NumElements_check = 1;
+    std::size_t NumElements = 1;
     std::size_t RankRequired = 0;
     std::vector<typename ETensor::Index>   dimNonTrivial;
     dimNonTrivial.reserve(dimData.size());
     for( auto d : dimData ) {
-      NumElements_check *= d;
+      NumElements *= d;
       if( d > 1 ) {
         RankRequired++;
         dimNonTrivial.push_back(d);
       }
     }
-    //if( RankRequired == 0 ) RankRequired++;
-    assert( NumElements_check == NumElements );
+    assert( NumElements == buf.size() && "Number of elements read back <> product of dimensions" );
+    // If our scalar object is a Container, make sure it's dimensions match what we read back
+    const auto InnerRank{Traits::rank_non_trivial};
+    if ( InnerRank > 0 ) {
+      assert( RankRequired >= InnerRank && "Tensor Container too complex for data" );
+      for( auto i = InnerRank - 1 ; i != -1 ; i-- ) {
+        auto d = dimNonTrivial[--RankRequired];
+        assert( d == Traits::DimensionNT(i) && "Tensor Container dimensions don't match data" );
+        NumElements /= d;
+        dimNonTrivial.pop_back();
+      }
+    }
     // Make sure our object has the right rank
-    using Container = typename ETensor::Scalar;
-    const auto InnerRank = EigenIO::Traits<Container>::rank_non_trivial;
-    assert( ETensor::NumDimensions + InnerRank >= RankRequired );
+    assert( ETensor::NumDimensions >= RankRequired );
     bool bShapeOK = true;
     std::size_t RankNonTrivial = 0;
-    // Make sure fixed dimension objects have allocated memory
+    const auto & dims{output.dimensions()};
     using ETDims = std::array<typename ETensor::Index, ETensor::NumDimensions>;
     ETDims dimsNew;
+    // Make sure fixed dimension objects have allocated memory
     /*if constexpr( EigenIO::is_tensor_fixed<ETensor>::value ) {
       for( auto &d : dimsNew ) d = 0;
       output( dimsNew ) = 0;
     }*/
-    //const auto & dims{output.dimensions()};
     for( auto i = 0, j = 0 ; bShapeOK && i < ETensor::NumDimensions ; i++ ) {
-      auto d = DimSize( output, i );
+      auto d = dims[i];
       if( d < 1 )
         bShapeOK = false;
       else if( d > 1 ) {
@@ -737,14 +757,14 @@ namespace Grid {
     std::size_t idx = 0;
     for( auto n = 0 ; n < NumElements ; n++ ) {
       Container & c = output( MyIndex );
-      if constexpr( InnerRank == 0 ) {
+      if constexpr ( EigenIO::is_scalar<Container>::value ) {
         c = buf[idx++];
       } else {
         for( Scalar & s : c )
           s = buf[idx++];
       }
       // Now increment the index
-      for( int i = output.NumDimensions - 1; i >= 0 && ++MyIndex[i] == output.dimension(i); i-- )
+      for( int i = ETensor::NumDimensions - 1; i >= 0 && ++MyIndex[i] == dims[i]; i-- )
         MyIndex[i] = 0;
     }
   }
@@ -766,7 +786,7 @@ namespace Grid {
     t.resize( dims );
   }
 
-  template <typename T>
+  /*template <typename T>
   template <typename ETensor>
   typename std::enable_if<EigenIO::is_tensor_fixed<ETensor>::value, std::size_t>::type
   Reader<T>::DimSize(ETensor &t, std::size_t dim )
@@ -780,7 +800,7 @@ namespace Grid {
   Reader<T>::DimSize(ETensor &t, std::size_t dim )
   {
     return t.dimension(dim);
-  }
+  }*/
 
   template <typename T>
   template <typename U>
diff --git a/tests/IO/Test_serialisation.cc b/tests/IO/Test_serialisation.cc
index b330d133..73045155 100644
--- a/tests/IO/Test_serialisation.cc
+++ b/tests/IO/Test_serialisation.cc
@@ -98,6 +98,8 @@ void ioTest(const std::string &filename, const O &object, const std::string &nam
   bool good = Serializable::CompareMember(object, buf);
   if (!good) {
     std::cout << " failure!" << std::endl;
+    if constexpr (EigenIO::is_tensor<O>::value)
+      dump_tensor(buf,"???");
     exit(EXIT_FAILURE);
   }
   std::cout << " done." << std::endl;
@@ -109,21 +111,28 @@ typedef std::complex<double> TestScalar;
 typedef Eigen::Tensor<TestScalar, 3, Eigen::StorageOptions::RowMajor> TestTensor;
 typedef Eigen::TensorFixedSize<TestScalar, Eigen::Sizes<9,4,2>, Eigen::StorageOptions::RowMajor> TestTensorFixed;
 typedef std::vector<TestTensorFixed> aTestTensorFixed;
-typedef Eigen::TensorFixedSize<SpinColourVector, Eigen::Sizes<11,3,2>> LSCTensor;
-typedef Eigen::TensorFixedSize<LorentzColourMatrix, Eigen::Sizes<5,7,2>> LCMTensor;
+typedef Eigen::TensorFixedSize<SpinColourVector, Eigen::Sizes<11,3,2>, Eigen::StorageOptions::RowMajor> LSCTensor;
+typedef Eigen::TensorFixedSize<LorentzColourMatrix, Eigen::Sizes<5,7,2>, Eigen::StorageOptions::RowMajor> LCMTensor;
 // From Test_serialisation.cc
-class ETSerClass: Serializable {
+class PerambIOTestClass: Serializable {
 public:
-  GRID_SERIALIZABLE_CLASS_MEMBERS(ETSerClass
-                                  , SpinColourVector, scv
-                                  , SpinColourMatrix, scm
-                                  , TestTensor, Critter
-                                  , TestTensorFixed, FixedCritter
-                                  , aTestTensorFixed, aFixedCritter
-                                  , LSCTensor, MyLSCTensor
-                                  , LCMTensor, MyLCMTensor
+  using PerambTensor = Eigen::Tensor<SpinColourVector, 6, Eigen::StorageOptions::RowMajor>;
+  GRID_SERIALIZABLE_CLASS_MEMBERS(PerambIOTestClass
+                                  //, SpinColourVector, scv
+                                  //, SpinColourMatrix, scm
+                                  , PerambTensor,               Perambulator
+                                  , std::vector<std::string>, DistilParameterNames
+                                  , std::vector<int>,         DistilParameterValues
+                                  //, TestTensor,       Critter
+                                  //, TestTensorFixed,  FixedCritter
+                                  //, aTestTensorFixed, aFixedCritter
+                                  //, LSCTensor, MyLSCTensor
+                                  //, LCMTensor, MyLCMTensor
                                   );
-  ETSerClass() : Critter(7,3,2), aFixedCritter(3) {}
+  PerambIOTestClass() : Perambulator(2,3,1,4,5,1),
+                        DistilParameterNames {"alpha", "beta", "gamma", "delta", "epsilon", "what's f?"},
+                        DistilParameterValues{2,3,1,4,5,1}//, Critter(7,3,2), aFixedCritter(3)
+  {}
 };
 
 bool EigenIOTest(void) {
@@ -157,7 +166,7 @@ bool EigenIOTest(void) {
         Val += Inc;
       }
   ioTest<Hdf5Writer, Hdf5Reader, TestTensor>("iotest_tensor.h5", t, "eigen_tensor_instance_name");
-  dump_tensor(t, "t");
+  //dump_tensor(t, "t");
 
   // Now serialise a fixed size tensor
   using FixedTensor = Eigen::TensorFixedSize<TestScalar, Eigen::Sizes<8,4,3>>;
@@ -170,11 +179,26 @@ bool EigenIOTest(void) {
         Val += Inc;
       }
   ioTest<Hdf5Writer, Hdf5Reader, FixedTensor>("iotest_tensor_fixed.h5", tf, "eigen_tensor_fixed_name");
-  dump_tensor(tf, "tf");
+  //dump_tensor(tf, "tf");
+
+  PerambIOTestClass o;
+  for_all( o.Perambulator, [&](TestScalar &c, float f, const std::array<size_t,PerambIOTestClass::PerambTensor::NumIndices + EigenIO::Traits<SpinColourVector>::rank_non_trivial> &Dims ){
+    c = TestScalar{f,-f};
+    //std::cout << " a(" << Dims[0] << "," << Dims[1] << "," << Dims[2] << ")=" << c;
+  } );
+  dump_tensor(o.Perambulator, "PerambIOTestClass" );
+  /*for_all( o.FixedCritter, [&](TestScalar &c, float f, const std::array<size_t,TestTensorFixed::NumIndices> &Dims ){
+    c = TestScalar{f,-f};
+    //std::cout << " a(" << Dims[0] << "," << Dims[1] << "," << Dims[2] << ")=" << c;
+  } );
+  for( auto &z : o.aFixedCritter )
+    for_all( z, [&](TestScalar &c, float f, const std::array<size_t,TestTensorFixed::NumIndices> &Dims ){
+      c = TestScalar{f,-f};
+      //std::cout << " a(" << Dims[0] << "," << Dims[1] << "," << Dims[2] << ")=" << c;
+    } );*/
+  ioTest<Hdf5Writer, Hdf5Reader, PerambIOTestClass>("iotest_object.h5", o, "PerambIOTestClass_object_instance_name");
+  //DumpMemoryOrder(o.Perambulator);
 
-  ETSerClass o;
-  ioTest<Hdf5Writer, Hdf5Reader, ETSerClass>("iotest_object.h5", o, "ETSerClass_object_instance_name");
-  
   // Tensor of spin colour
   LSCTensor l;
   Val = 0;
@@ -188,7 +212,7 @@ bool EigenIOTest(void) {
             Val += Inc;
           }
   ioTest<Hdf5Writer, Hdf5Reader, LSCTensor>("iotest_LSCTensor.h5", l, "LSCTensor_object_instance_name");
-  dump_tensor(l, "l");
+  //dump_tensor(l, "l");
 
   // Tensor of spin colour
   LCMTensor l2;
@@ -204,7 +228,8 @@ bool EigenIOTest(void) {
               Val += Inc;
             }
   ioTest<Hdf5Writer, Hdf5Reader, LCMTensor>("iotest_LCMTensor.h5", l2, "LCMTensor_object_instance_name");
-  
+  //dump_tensor(l2, "l2");
+
   std::cout << "Wow!" << std::endl;
   
   return true;
diff --git a/tests/hadrons/Test_hadrons_distil.cc b/tests/hadrons/Test_hadrons_distil.cc
index 3c38e915..00a681f0 100644
--- a/tests/hadrons/Test_hadrons_distil.cc
+++ b/tests/hadrons/Test_hadrons_distil.cc
@@ -315,28 +315,30 @@ bool bNumber( int &ri, const char * & pstr, bool bGobbleWhiteSpace = true )
 
 typedef Grid::Hadrons::MDistil::NamedTensor<Complex,3,sizeof(Real)> MyTensor;
 
-void DebugShowTensor(MyTensor &x, const char * n)
+template<typename T>
+void DebugShowTensor(T &x, const char * n)
 {
   const MyTensor::Index s{x.size()};
   std::cout << n << ".size() = " << s << std::endl;
   std::cout << n << ".NumDimensions = " << x.NumDimensions << " (TensorBase)" << std::endl;
   std::cout << n << ".NumIndices = " << x.NumIndices << std::endl;
-  const MyTensor::Dimensions & d{x.dimensions()};
-  std::cout << n << ".dimensions().size() = " << d.size() << std::endl;
+  const auto d{x.dimensions()};
+  //std::cout << n << ".dimensions().size() = " << d.size() << std::endl;
   std::cout << "Dimensions are ";
-  for(auto i : d ) std::cout << "[" << i << "]";
+  for(auto i = 0; i < x.NumDimensions ; i++)
+    std::cout << "[" << d[i] << "]";
   std::cout << std::endl;
   MyTensor::Index SizeCalculated{1};
   std::cout << "Dimensions again";
-  for(int i=0 ; i < d.size() ; i++ ) {
-    std::cout << " : [" << i << ", " << x.IndexNames[i] << "]=" << d[i];
+  for(int i=0 ; i < x.NumDimensions ; i++ ) {
+    std::cout << " : [" << i << /*", " << x.IndexNames[i] << */"]=" << x.dimension(i);
     SizeCalculated *= d[i];
   }
   std::cout << std::endl;
   std::cout << "SizeCalculated = " << SizeCalculated << std::endl;\
   assert( SizeCalculated == s );
   // Initialise
-  assert( d.size() == 3 );
+  assert( x.NumDimensions == 3 );
   for( int i = 0 ; i < d[0] ; i++ )
     for( int j = 0 ; j < d[1] ; j++ )
       for( int k = 0 ; k < d[2] ; k++ ) {
@@ -345,7 +347,7 @@ void DebugShowTensor(MyTensor &x, const char * n)
       }
   // Show raw data
   std::cout << "Data follow : " << std::endl;
-  Complex * p = x.data();
+  typename T::Scalar * p = x.data();
   for( auto i = 0 ; i < s ; i++ ) {
     if( i ) std::cout << ", ";
     std::cout << n << ".data()[" << i << "]=" << * p++;
@@ -415,6 +417,10 @@ void DebugTestTypeEqualities(void)
 
 bool DebugEigenTest()
 {
+  {
+    Eigen::TensorFixedSize<std::complex<double>,Eigen::Sizes<3,4,5>> x;
+    DebugShowTensor(x, "fixed");
+  }
   const char pszTestFileName[] = "test_tensor.bin";
   std::array<std::string,3> as={"Alpha", "Beta", "Gamma"};
   MyTensor x(as, 2,1,4);
@@ -636,7 +642,7 @@ int main(int argc, char *argv[])
   << ", sizeof(std::size_t) = " << sizeof(std::size_t)
   << ", sizeof(std::streamsize) = " << sizeof(std::streamsize)
   << ", sizeof(Eigen::Index) = " << sizeof(Eigen::Index) << std::endl;
-  //if( DebugEigenTest() ) return 0;
+  if( DebugEigenTest() ) return 0;
   if(DebugGridTensorTest()) return 0;
 #endif