[vmap] symintify alias and squeeze (#107577)

Following tests now pass (both ops call into `alias` on certain paths)

```
PYTORCH_TEST_WITH_DYNAMO=1 pytest test/functorch/test_vmap.py -k test_squeeze -v
PYTORCH_TEST_WITH_DYNAMO=1 pytest test/functorch/test_vmap.py -k test_conj -v
```

NOTE: Ideally, this symint version should work with non symint version as well but that would mean changes at multiple places. Wanted to get a review for this fix before-hand.

Other sites which use the `IntArrayRef` overload.
https://github.com/pytorch/pytorch/blob/5f56c4fb32dbb5dd4e75a3a3a9726ae95931926d/aten/src/ATen/native/TensorShape.cpp#L1707-L1713

`view_impl` is called from `view` and `_unsafe_view`.
https://github.com/pytorch/pytorch/blob/5f56c4fb32dbb5dd4e75a3a3a9726ae95931926d/aten/src/ATen/native/TensorShape.cpp#L3295-L3306

Pull Request resolved: #107577
Approved by: https://github.com/zou3519

aten/src/ATen/functorch/BatchRulesViews.cpp

@@ -198,8 +198,8 @@ std::tuple<Tensor, optional<int64_t>> squeeze_batch_rule(const Tensor& self, opt
   // Manually calculate the output shape by eliding all dimensions of
   // size 1 keeping track of where the batch index started and where it
   // ended up moving to. We also ensure we do not drop the batch index.
-  auto shape = self.sizes();
-  DimVector squeezed_sizes;
+  auto shape = self.sym_sizes();
+  SymDimVector squeezed_sizes;
   bool before_batch_idx = true;
   int64_t new_batch_idx = 0;
   int64_t original_idx = 0;
@@ -219,7 +219,7 @@ std::tuple<Tensor, optional<int64_t>> squeeze_batch_rule(const Tensor& self, opt
     ++original_idx;
   }
 
-  auto result = self.view(squeezed_sizes);
+  auto result = self.view_symint(squeezed_sizes);
   return std::make_tuple(std::move(result), c10::optional<int64_t>(new_batch_idx));
 }
 
@@ -453,7 +453,7 @@ std::tuple<Tensor, optional<int64_t>> expand_batch_rule(
               "must be greater or equal to the number of dimensions in the tensor (", static_cast<uint64_t>(self_dim - 1), ")");
 
   auto self_ = moveBatchDimToFront(self, self_bdim);
-  auto self_sizes = self_.sizes();
+  auto self_sizes = self_.sym_sizes();
   auto batch_size = self_sizes[0];
 
   c10::SmallVector<c10::SymInt> size_(size.size() + 1);

aten/src/ATen/native/TensorShape.cpp

@@ -1601,6 +1601,29 @@ Tensor alias_with_sizes_and_strides(
   return self_;
 }
 
+// specialization for symbolic shapes and strides.
+// SymIntArrayRef/ArrayRef<c10::SymInt> and SmallVector<c10::SymInt>/SymDimVector
+template <template <typename...> typename Container>
+Tensor alias_with_sizes_and_strides(
+    const Tensor& self,
+    const Container<c10::SymInt>& sizes,
+    const Container<c10::SymInt>& strides) {
+  //caller should make sure that sizes and strides are valid for self
+  //(storage is sufficient, strides are non-negative, strides and sizes array size is the same)
+  Tensor self_;
+  if (self.is_quantized()) {
+    self_ = at::detail::make_tensor<QTensorImpl>(
+      c10::TensorImpl::VIEW, Storage(self.storage()), self.key_set(), self.dtype(), get_qtensorimpl(self)->quantizer());
+    self_.unsafeGetTensorImpl()->set_sizes_and_strides(sizes, strides, self.sym_storage_offset());
+  } else {
+    self_ = at::detail::make_tensor<TensorImpl>(
+    c10::TensorImpl::VIEW, Storage(self.storage()), self.key_set(), self.dtype());
+    self_.unsafeGetTensorImpl()->set_sizes_and_strides(sizes, strides, self.sym_storage_offset());
+  }
+  namedinference::propagate_names(self_, self);
+  return self_;
+}
+
 Tensor reshape_symint(const Tensor& self, c10::SymIntArrayRef proposed_shape) {
   if (self.is_sparse()) {
     AT_ERROR("reshape is not implemented for sparse tensors");
@@ -3686,7 +3709,7 @@ Tensor view(const Tensor& self,
 }
 
 Tensor alias(const Tensor& self) {
-  return alias_with_sizes_and_strides(self, self.sizes(), self.strides());
+  return alias_with_sizes_and_strides(self, self.sym_sizes(), self.sym_strides());
 }
 
 Tensor detach(const Tensor& self) {