diff --git a/aten/src/ATen/WrapDimUtils.h b/aten/src/ATen/WrapDimUtils.h
index 3b4b0ae02bec..aa000b118daa 100644
--- a/aten/src/ATen/WrapDimUtils.h
+++ b/aten/src/ATen/WrapDimUtils.h
@@ -121,7 +121,7 @@ inline int64_t legacy_cat_wrap_dim_symint(
     const std::vector<std::vector<c10::SymInt>>& tensor_sizes) {
   for (auto& sizes : tensor_sizes) {
     if (sizes.size() == 1) {
-      if (TORCH_GUARD_SIZE_OBLIVIOUS(sizes[0].sym_eq(0))) {
+      if (TORCH_GUARD_OR_FALSE(sizes[0].sym_eq(0))) {
         continue;
       }
     }
@@ -135,7 +135,7 @@ inline int64_t legacy_cat_wrap_dim(
     const MaterializedITensorListRef& tensors) {
   for (const Tensor& tensor : tensors) {
     if (tensor.dim() == 1) {
-      if (TORCH_GUARD_SIZE_OBLIVIOUS(tensor.sym_sizes()[0].sym_eq(0))) {
+      if (TORCH_GUARD_OR_FALSE(tensor.sym_sizes()[0].sym_eq(0))) {
         continue;
       }
     }
diff --git a/torch/_inductor/decomposition.py b/torch/_inductor/decomposition.py
index d903d851ee87..e7f8e3c31964 100644
--- a/torch/_inductor/decomposition.py
+++ b/torch/_inductor/decomposition.py
@@ -34,11 +34,7 @@
     ELEMENTWISE_TYPE_PROMOTION_KIND,
     type_to_dtype,
 )
-from torch.fx.experimental.symbolic_shapes import (
-    guard_or_false,
-    guard_size_oblivious,
-    statically_known_true,
-)
+from torch.fx.experimental.symbolic_shapes import guard_or_false, statically_known_true
 
 from . import config, inductor_prims
 from .utils import (
@@ -404,10 +400,10 @@ def non_empty_tensor(x: torch.Tensor) -> bool:
         # runtime assert forcing u0 to be zero.  So if this hasn't happened,
         # we know that the unbacked SymInt has appropriate size and there are
         # no problems.
-        if len(x.shape) == 1 and guard_size_oblivious(x.shape[0] == 0):
+        if len(x.shape) == 1 and guard_or_false(x.shape[0] == 0):
             return False
 
-        if dim < len(x.shape) and guard_size_oblivious(x.shape[dim] == 0):
+        if dim < len(x.shape) and guard_or_false(x.shape[dim] == 0):
             return False
 
         return True
diff --git a/torch/_inductor/fx_passes/split_cat.py b/torch/_inductor/fx_passes/split_cat.py
index 327f96ae34ac..a5fc96a5ec4e 100644
--- a/torch/_inductor/fx_passes/split_cat.py
+++ b/torch/_inductor/fx_passes/split_cat.py
@@ -9,7 +9,7 @@
 
 import torch
 from torch._dynamo.utils import counters
-from torch.fx.experimental.symbolic_shapes import free_symbols
+from torch.fx.experimental.symbolic_shapes import free_symbols, guard_or_false
 from torch.utils._ordered_set import OrderedSet
 
 from ..pattern_matcher import (
@@ -307,8 +307,6 @@ def normalize_unbind_default(match: Match, *args, **kwargs):
     pass_dict=construct_pattern_matcher_pass("normalization_pass"),
 )
 def normalize_cat_default(match: Match, *args, **kwargs):
-    from torch.fx.experimental.symbolic_shapes import guard_size_oblivious
-
     cat_node = match.nodes[0]
     graph = match.graph
     tensors = get_arg_value(cat_node, 0, "tensors")
@@ -333,7 +331,7 @@ def normalize_cat_default(match: Match, *args, **kwargs):
     def is_empty_tensor(x):
         # special case where torch.cat supports cat'ing with an empty tensor
         x_shape = x.meta["example_value"].shape
-        return len(x_shape) == 1 and guard_size_oblivious(x_shape[0] == 0)
+        return len(x_shape) == 1 and guard_or_false(x_shape[0] == 0)
 
     assert all(
         ndim == x.meta["example_value"].dim() or is_empty_tensor(x) for x in tensors
diff --git a/torch/_refs/__init__.py b/torch/_refs/__init__.py
index c82d7aaecb85..c5e175e0d7df 100644
--- a/torch/_refs/__init__.py
+++ b/torch/_refs/__init__.py
@@ -2782,7 +2782,6 @@ def cat_compute_output_memory_format(inputs):
 
     from torch.fx.experimental.symbolic_shapes import (
         guard_or_false,
-        guard_size_oblivious,
     )
 
     # This is a bit tricky.  Naively, you would expect to just pick one
@@ -2837,7 +2836,7 @@ def cat_compute_output_memory_format(inputs):
                 # through), and is load bearing for our Inductor lowerings
                 # (which assume that size oblivious tests are OK to determine
                 # if a shape is permissibly zero.)
-                guard_size_oblivious(tensor.shape[0] == 0),
+                guard_or_false(tensor.shape[0] == 0),
                 lambda: f"Number of dimensions of tensors must match.  "
                 f"Expected {example.ndim}-D tensors, but got 1-D for "
                 f"tensor number {tensor_idx} in the list",
diff --git a/torch/csrc/autograd/FunctionsManual.cpp b/torch/csrc/autograd/FunctionsManual.cpp
index 8e13d4267edb..29dd6702ce31 100644
--- a/torch/csrc/autograd/FunctionsManual.cpp
+++ b/torch/csrc/autograd/FunctionsManual.cpp
@@ -1078,7 +1078,7 @@ std::vector<Tensor> cat_tensors_backward(
     auto& shape = sizes[i];
     // If input was empty tensor, gradInput should be empty tensor.
     if (shape.size() == 1) {
-      if (TORCH_GUARD_SIZE_OBLIVIOUS(shape[0].sym_eq(0))) {
+      if (TORCH_GUARD_OR_FALSE(shape[0].sym_eq(0))) {
         grad_inputs[i] = at::zeros({0}, grad_val.options());
         continue;
       }