Revert "Replace is_same with is_same_v for concise syntax (pytorch#145450)"

This reverts commit 5205158.

Reverted pytorch#145450 on behalf of https://github.com/jeanschmidt due to testing to see if reverting would fix timeout in inductor jobs ([comment](pytorch#145450 (comment)))

Author: pytorchmergebot

aten/src/ATen/core/List_inl.h

@@ -47,7 +47,7 @@ List<T>::List(TypePtr elementType)
 : List(make_intrusive<c10::detail::ListImpl>(
     typename c10::detail::ListImpl::list_type(),
     std::move(elementType))) {
-  static_assert(std::is_same_v<T, IValue> || std::is_same_v<T, c10::intrusive_ptr<ivalue::Future>>,
+  static_assert(std::is_same_v<T, IValue> || std::is_same<T, c10::intrusive_ptr<ivalue::Future>>::value,
                 "This constructor is only valid for c10::impl::GenericList or List<Future>.");
 }
 

aten/src/ATen/cpu/vec/vec256/zarch/vec256_zarch.h

@@ -38,8 +38,8 @@ constexpr bool is_zarch_implemented_quant() {
 
 template <typename T>
 constexpr bool is_zarch_implemented_complex() {
-  return std::is_same_v<T, c10::complex<float>> ||
-      std::is_same_v<T, c10::complex<double>>;
+  return std::is_same<T, c10::complex<float>>::value ||
+      std::is_same<T, c10::complex<double>>::value;
 }
 
 constexpr int offset0 = 0;

aten/src/ATen/cuda/cub.cuh

@@ -478,7 +478,7 @@ constexpr int block_threads(){
 
 template<typename scalar_t, typename ScanOpT>
 inline void inclusive_deterministic_scan(const scalar_t *  input, scalar_t * output, ScanOpT scan_op, int64_t num_items) {
-  static_assert(std::is_same_v<ScanOpT, std::plus<scalar_t>>, "");
+  static_assert(std::is_same<ScanOpT, std::plus<scalar_t>>::value, "");
   constexpr int BLOCK_THREADS = block_threads<sizeof(scalar_t)>();
   constexpr int ITEMS_PER_THREAD = 16;
   auto grid_size = (num_items + BLOCK_THREADS * ITEMS_PER_THREAD - 1) / (BLOCK_THREADS * ITEMS_PER_THREAD);

aten/src/ATen/native/cuda/ScanUtils.cuh

@@ -451,7 +451,7 @@ void scan_dim(const TensorBase& self, const TensorBase& result,
   TORCH_INTERNAL_ASSERT(result.is_contiguous());
 
   if (self.numel() == self.size(dim)) {
-    if constexpr (std::is_same_v<BinaryFunction, std::plus<scalar_t>>) {
+    if constexpr (std::is_same<BinaryFunction, std::plus<scalar_t>>::value) {
       if (C10_UNLIKELY(at::globalContext().deterministicAlgorithms()) && (self.is_floating_point() || self.is_complex())) {
 # if (defined(CUDA_VERSION) && CUDA_VERSION > 11040) || defined(USE_ROCM)
         cuda::cub::inclusive_deterministic_scan(self_->const_data_ptr<scalar_t>(), result.mutable_data_ptr<scalar_t>(), binary_op, self.numel());

aten/src/ATen/native/sparse/cuda/SparseMatMul.cu

@@ -211,8 +211,8 @@ struct CusparseMatrixMultiplyOp {
           std::is_same_v<c10::BFloat16, scalar_t> ||
           std::is_same_v<float, scalar_t> ||
           std::is_same_v<double, scalar_t> ||
-          std::is_same_v<c10::complex<float>, scalar_t> ||
-          std::is_same_v<c10::complex<double>, scalar_t>,
+          std::is_same<c10::complex<float>, scalar_t>::value ||
+          std::is_same<c10::complex<double>, scalar_t>::value,
       "cusparseSpGEMM only supports data type of half, bfloat16, float, double and complex float, double.");
     // SpGEMM Computation
     TORCH_CUDASPARSE_CHECK(cusparseSpGEMM_createDescr(&spgemmDesc));
@@ -673,8 +673,8 @@ void sparse_sparse_matmul_cuda_kernel(
         std::is_same_v<c10::BFloat16, scalar_t> ||
         std::is_same_v<float, scalar_t> ||
         std::is_same_v<double, scalar_t> ||
-        std::is_same_v<c10::complex<float>, scalar_t> ||
-        std::is_same_v<c10::complex<double>, scalar_t>,
+        std::is_same<c10::complex<float>, scalar_t>::value ||
+        std::is_same<c10::complex<double>, scalar_t>::value,
     "sparse_sparse_matmul_cuda_kernel only supports data type of half, bfloat16, float, double and complex float, double.");
 
   // older versions of cusparse on Windows segfault for complex128 dtype

aten/src/ATen/templates/TensorBody.h

@@ -582,7 +582,7 @@ class TORCH_API Tensor: public TensorBase {
   template <typename T>
   using hook_return_void_t = std::enable_if_t<std::is_void<typename std::invoke_result_t<T&, Tensor>>::value, unsigned>;
   template <typename T>
-  using hook_return_var_t = std::enable_if_t<std::is_same_v<typename std::invoke_result_t<T&, Tensor>, Tensor>, unsigned>;
+  using hook_return_var_t = std::enable_if_t<std::is_same<typename std::invoke_result_t<T&, Tensor>, Tensor>::value, unsigned>;
 
   /// Registers a backward hook.
   ///