[AUTOGENERATED] [release/2.7] fix large tensor sort on ROCm

aten/src/ATen/native/cuda/SortStable.cu

@@ -225,8 +225,9 @@ void launch_stable_sort_kernel(
     return;
   }
 
-  int64_t numel_or_intmax =
-      std::min(numel, static_cast<int64_t>(std::numeric_limits<int>::max()));
+  const int64_t intmax = static_cast<int64_t>(std::numeric_limits<int>::max());
+  // On ROCm, std::min -> ::min did not work as expected on when input values >= 2147483648
+  int64_t numel_or_intmax = numel < intmax ? numel : intmax;
   int64_t nsort = self.size(dim);
   int64_t nbatch = (numel_or_intmax / nsort) * nsort;
   TORCH_CHECK(nbatch > 0, "Cannot sort dimension of length ", nsort);
@@ -238,7 +239,8 @@ void launch_stable_sort_kernel(
         scalar_t* values_ptr = values.mutable_data_ptr<scalar_t>();
         int64_t remaining = numel;
         while (remaining > 0) {
-          int64_t n = std::min(remaining, nbatch);
+          // On ROCm, std::min -> ::min did not work as expected on when input values >= 2147483648
+          int64_t n = remaining < nbatch ? remaining : nbatch;
           int64_t nsegments = n / nsort;
 
           if (nsegments == 1 ||

test/test_sort_and_select.py

@@ -215,7 +215,7 @@ def test_stable_sort(self, device, dtype):
             )
 
     @onlyCUDA
-    @dtypes(torch.uint8)
+    @dtypes(torch.float16)
     @largeTensorTest("200GB")  # Unfortunately 80GB A100 is not large enough
     def test_sort_large(self, device, dtype):
         t0 = torch.randperm(8192, device=device).to(dtype)