remove symint from tbe_input_combine_with_length_abstract

Summary:
as titled, the number of offsets is not dynamic, rather it is the sum of the number of elements in the concatenated offset tensors

additionally, make sure new tensors are created on the same device as the indices

Differential Revision: D53862184

fbgemm_gpu/fbgemm_gpu/sparse_ops.py

@@ -175,6 +175,7 @@ def tbe_input_combine_with_length_abstract(
     torch._check(len(indices_list) == len(offsets_list))
     torch._check(len(indices_list) == len(per_sample_weights))
     total_indices = 0
+    total_offsets = 0
     need_weight = False
     for index, offset, weight in zip(indices_list, offsets_list, per_sample_weights):
         torch._check(index.dtype == torch.int or index.dtype == torch.long)
@@ -184,20 +185,20 @@ def tbe_input_combine_with_length_abstract(
         torch._check(index.is_contiguous())
         torch._check(offset.is_contiguous())
         total_indices = total_indices + index.numel()
+        total_offsets = total_offsets + offset.numel()
         if weight.numel() > 0:
             torch._check(weight.dim() == 1)
             torch._check(weight.numel() == index.numel())
             torch._check(weight.is_contiguous())
             need_weight = True
-    total_offsets = torch.library.get_ctx().new_dynamic_size()
     combined_indices = indices_list[0].new_empty([total_indices], dtype=torch.int)
     combined_offsets = offsets_list[0].new_empty([total_offsets], dtype=torch.int)
     if need_weight:
         combined_weights = per_sample_weights[0].new_empty(
             [total_indices], dtype=torch.float
         )
     else:
-        combined_weights = torch.empty(0)
+        combined_weights = torch.empty(0, device=indices_list[0].device)
     return combined_indices, combined_offsets, combined_weights