Converting old std:: types to cuda::std:: types (#629)

* Converting old std:: types to cuda::std:: types. Paving the way for removing relaxed constexpr.

Author: cliffburdick

docs_input/api/manipulation/joinrepeat/clone.rst

@@ -6,7 +6,7 @@ clone
 Clone one or more dimensions of an operator to a higher rank
 
 .. doxygenfunction:: clone(Op t, const index_t (&shape)[Rank])
-.. doxygenfunction:: clone(Op t, const std::array<index_t, Rank> &shape)
+.. doxygenfunction:: clone(Op t, const cuda::std::array<index_t, Rank> &shape)
 
 Examples
 ~~~~~~~~

docs_input/api/manipulation/rearranging/overlap.rst

@@ -22,7 +22,7 @@ end of the data to make the tensor rectangular.
     Only 1D input operators are accepted at this time
 
 .. doxygenfunction:: overlap( const OpType &op, const index_t (&windows)[N], const index_t (&strides)[N])
-.. doxygenfunction:: overlap( const OpType &op, const std::array<index_t, N> &windows, const std::array<index_t, N> &strides)
+.. doxygenfunction:: overlap( const OpType &op, const cuda::std::array<index_t, N> &windows, const cuda::std::array<index_t, N> &strides)
 
 Examples
 ~~~~~~~~

docs_input/api/manipulation/rearranging/permute.rst

@@ -6,7 +6,7 @@ permute
 Permute the dimensions of an operator
 
 .. doxygenfunction:: permute(const T &op, const int32_t (&dims)[T::Rank()])
-.. doxygenfunction:: permute(const T &op, const std::array<int32_t, T::Rank()> &dims)
+.. doxygenfunction:: permute(const T &op, const cuda::std::array<int32_t, T::Rank()> &dims)
 
 Examples
 ~~~~~~~~

docs_input/api/polynomials/legendre.rst

@@ -7,7 +7,7 @@ Return Legendre polynomial coefficients at the input operator
 
 .. doxygenfunction:: legendre(T1 n, T2 m, const T3 in)
 .. doxygenfunction:: legendre(T1 n, T2 m, const T3 in, int (&axis)[2])
-.. doxygenfunction:: legendre(T1 n, T2 m, const T3 in, std::array<int, 2> axis)  
+.. doxygenfunction:: legendre(T1 n, T2 m, const T3 in, cuda::std::array<int, 2> axis)  
 
 Examples
 ~~~~~~~~

docs_input/api/signalimage/filtering/filter.rst

@@ -10,7 +10,7 @@ used for IIR filters, but it will call the appropriate functions for FIR if the
 .. note::
    This function is currently is not supported with host-based executors (CPU)
 
-.. doxygenfunction:: matx::filter(const OpA &a, const std::array<FilterType, NR> h_rec, const std::array<FilterType, NNR> h_nonrec)
+.. doxygenfunction:: matx::filter(const OpA &a, const cuda::std::array<FilterType, NR> h_rec, const cuda::std::array<FilterType, NNR> h_nonrec)
 
 Examples
 ~~~~~~~~

docs_input/basics/creation.rst

@@ -196,7 +196,7 @@ As mentioned in the descriptor section, any type that conforms to the shape sema
 
 .. code-block:: cpp
 
-    std::array<int, 3> = {10, 20, 30};
+    cuda::std::array<int, 3> = {10, 20, 30};
     auto t = make_tensor<float>(array);
 
 Creating From A Descriptor

examples/recursive_filter.cu

@@ -80,8 +80,8 @@ int main([[maybe_unused]] int argc, [[maybe_unused]] char **argv)
   tensor_t<InType, 1> solView({numSamples});
 
   // Create views into data objects
-  auto rCoeffs = std::array<FilterType, 2>{0.4f, -0.1f};
-  auto nrCoeffs = std::array<FilterType, 2>{2.0f, 1.0f};
+  auto rCoeffs = cuda::std::array<FilterType, 2>{0.4f, -0.1f};
+  auto nrCoeffs = cuda::std::array<FilterType, 2>{2.0f, 1.0f};
 
   // initialize input data
   for (index_t b = 0; b < batches; b++) {

examples/spectrogram.cu

@@ -77,9 +77,9 @@ int main([[maybe_unused]] int argc, [[maybe_unused]] char **argv)
   constexpr uint32_t num_iterations = 100;
   float time_ms;
 
-  std::array<index_t, 1> num_samps{N};
-  std::array<index_t, 1> half_win{nfft / 2 + 1};
-  std::array<index_t, 1> s_time_shape{(N - noverlap) / nstep};
+  cuda::std::array<index_t, 1> num_samps{N};
+  cuda::std::array<index_t, 1> half_win{nfft / 2 + 1};
+  cuda::std::array<index_t, 1> s_time_shape{(N - noverlap) / nstep};
 
   auto time = make_tensor<float>({N});
   auto modulation = make_tensor<float>({N});

examples/spectrogram_graph.cu

@@ -78,9 +78,9 @@ int main([[maybe_unused]] int argc, [[maybe_unused]] char **argv)
   constexpr uint32_t num_iterations = 20;
   float time_ms;
 
-  std::array<index_t, 1> num_samps{N};
-  std::array<index_t, 1> half_win{nfft / 2 + 1};
-  std::array<index_t, 1> s_time_shape{(N - noverlap) / nstep};
+  cuda::std::array<index_t, 1> num_samps{N};
+  cuda::std::array<index_t, 1> half_win{nfft / 2 + 1};
+  cuda::std::array<index_t, 1> s_time_shape{(N - noverlap) / nstep};
 
   tensor_t<float, 1> time({N});
   tensor_t<float, 1> modulation({N});

examples/svd_power.cu

@@ -91,7 +91,7 @@ int main([[maybe_unused]] int argc, [[maybe_unused]] char **argv)
   (A = random<float>({m, n}, NORMAL)).run(exec);
 
 #endif
-  std::array<index_t, U.Rank()> Dshape;
+  cuda::std::array<index_t, U.Rank()> Dshape;
   Dshape.fill(matxKeepDim);
   Dshape[U.Rank()-2] = m;
   // cloning D across