Adopt nestedtensor to structured cumsum

Summary: https://www.internalfb.com/diff/D29939489 broke NT

Reviewed By: ezyang

Differential Revision: D30110005

fbshipit-source-id: 2fe58d0a7295283483fc7e3b05f97fa55e4b37ca

Author: cpuhrsch

nestedtensor/csrc/BinaryOps.cpp

@@ -72,7 +72,7 @@ Tensor NestedTensor_add_Tensor(
       }
       at::Tensor numbers_t = torch::tensor(numbers).to(torch::kInt32);
       Tensor nt_sizes_cumsum =
-          at::native::cumsum(numbers_t, 0).to(torch::kInt32).reshape({-1});
+          at::cumsum(numbers_t, 0).to(torch::kInt32).reshape({-1});
       TORCH_CHECK(nt_sizes_.dim() == 2, "NestedTensor metadata of unexpected dimension.")
       Tensor nt_sizes = at::cat({torch::tensor({0}, torch::kInt32), nt_sizes_cumsum});
       nt_sizes = nt_sizes.to(torch::kCUDA);
@@ -108,7 +108,7 @@ Tensor NestedTensor_add_Tensor(
   }
   std::tie(self, other) = _expand_other_as(self_, other_);
   return map_nested_tensor(
-      [&alpha](Tensor s, Tensor o) { 
+      [&alpha](Tensor s, Tensor o) {
       return at::add(s, o, alpha); },
       self,
       other);
@@ -270,7 +270,7 @@ Tensor NestedTensor_mul_Tensor(const Tensor& self_, const Tensor& other_) {
       }
       at::Tensor numbers_t = torch::tensor(numbers).to(torch::kInt32);
       Tensor nt_sizes_cumsum =
-          at::native::cumsum(numbers_t, 0).to(torch::kInt32).reshape({-1});
+          at::cumsum(numbers_t, 0).to(torch::kInt32).reshape({-1});
       TORCH_CHECK(nt_sizes_.dim() == 2, "NestedTensor metadata of unexpected dimension.")
       Tensor nt_sizes = at::cat({torch::tensor({0}, torch::kInt32), nt_sizes_cumsum});
       nt_sizes = nt_sizes.to(torch::kCUDA);
@@ -295,7 +295,7 @@ Tensor NestedTensor_mul_Tensor(const Tensor& self_, const Tensor& other_) {
   }
   std::tie(self, other) = _expand_other_as(self_, other_);
   return map_nested_tensor(
-      [](Tensor s, Tensor o) { 
+      [](Tensor s, Tensor o) {
       return at::mul(s, o); }, self, other);
 }
 
@@ -389,7 +389,7 @@ Tensor NestedTensor_sub_Tensor(
       }
       at::Tensor numbers_t = torch::tensor(numbers).to(torch::kInt32);
       Tensor nt_sizes_cumsum =
-          at::native::cumsum(numbers_t, 0).to(torch::kInt32).reshape({-1});
+          at::cumsum(numbers_t, 0).to(torch::kInt32).reshape({-1});
       TORCH_CHECK(nt_sizes_.dim() == 2, "NestedTensor metadata of unexpected dimension.")
       Tensor nt_sizes = at::cat({torch::tensor({0}, torch::kInt32), nt_sizes_cumsum});
       nt_sizes = nt_sizes.to(torch::kCUDA);
@@ -414,7 +414,7 @@ Tensor NestedTensor_sub_Tensor(
   }
   std::tie(self, other) = _expand_other_as(self_, other_);
   return map_nested_tensor(
-      [&alpha](Tensor s, Tensor o) { 
+      [&alpha](Tensor s, Tensor o) {
       return at::sub(s, o, alpha); },
       self,
       other);

nestedtensor/csrc/masking.cpp

@@ -261,7 +261,7 @@ std::tuple<Tensor, Tensor> to_tensor_mask(
       Tensor nt_sizes = at::native::narrow(nt_sizes_, 1, 0, 1);
       int max_size_1 = nt_sizes.max().item<int>();
       nt_sizes =
-          at::native::cumsum(nt_sizes, 0).to(torch::kInt32).reshape({-1});
+          at::cumsum(nt_sizes, 0).to(torch::kInt32).reshape({-1});
       nt_sizes = at::cat({torch::tensor({0}, torch::kInt32), nt_sizes});
       Tensor output = torch::zeros(
           {*nt_opt_size[0], max_size_1, *nt_opt_size[2]}, nt_buffer.options());
@@ -477,7 +477,7 @@ Tensor from_padded_tensor(Tensor padded, EfficientSizeNode target_size) {
   std::vector<at::Tensor> all_sizes = target_size.sizes().unbind();
   for (int64_t i = 0; i < all_sizes.size(); i++) {
     std::vector<int64_t> sizes_i(
-        all_sizes[i].data_ptr<int64_t>(), 
+        all_sizes[i].data_ptr<int64_t>(),
         all_sizes[i].data_ptr<int64_t>() + all_sizes[i].numel());
     at::Tensor mask_i = padded.new_full(
                                     IntArrayRef(sizes_i),