gso cat ops

laithsakka · laithsakka · commit b4026d3a6cd2 · 2025-08-09T08:56:16.000-07:00
ghstack-source-id: 667e71e Pull-Request: #160250
diff --git 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
@@ -404,10 +404,10 @@ 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
@@ -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 (
@@ -307,7 +307,7 @@ 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
+    from torch.fx.experimental.symbolic_shapes import guard_or_false
 
     cat_node = match.nodes[0]
     graph = match.graph
@@ -333,7 +333,7 @@ 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
@@ -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
@@ -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;
       }

Original file line number	Diff line number	Diff line change
`@@ -121,7 +121,7 @@ inline int64_t legacy_cat_wrap_dim_symint(`
`121`	`121`	`const std::vector<std::vector<c10::SymInt>>& tensor_sizes) {`
`122`	`122`	`for (auto& sizes : tensor_sizes) {`
`123`	`123`	`if (sizes.size() == 1) {`
`124`		`- if (TORCH_GUARD_SIZE_OBLIVIOUS(sizes[0].sym_eq(0))) {`
	`124`	`+ if (TORCH_GUARD_OR_FALSE(sizes[0].sym_eq(0))) {`
`125`	`125`	`continue;`
`126`	`126`	`}`
`127`	`127`	`}`
`@@ -135,7 +135,7 @@ inline int64_t legacy_cat_wrap_dim(`
`135`	`135`	`const MaterializedITensorListRef& tensors) {`
`136`	`136`	`for (const Tensor& tensor : tensors) {`
`137`	`137`	`if (tensor.dim() == 1) {`
`138`		`- if (TORCH_GUARD_SIZE_OBLIVIOUS(tensor.sym_sizes()[0].sym_eq(0))) {`
	`138`	`+ if (TORCH_GUARD_OR_FALSE(tensor.sym_sizes()[0].sym_eq(0))) {`
`139`	`139`	`continue;`
`140`	`140`	`}`
`141`	`141`	`}`
Original file line number	Diff line number	Diff line change
`@@ -1078,7 +1078,7 @@ std::vector<Tensor> cat_tensors_backward(`
`1078`	`1078`	`auto& shape = sizes[i];`
`1079`	`1079`	`// If input was empty tensor, gradInput should be empty tensor.`
`1080`	`1080`	`if (shape.size() == 1) {`
`1081`		`- if (TORCH_GUARD_SIZE_OBLIVIOUS(shape[0].sym_eq(0))) {`
	`1081`	`+ if (TORCH_GUARD_OR_FALSE(shape[0].sym_eq(0))) {`
`1082`	`1082`	`grad_inputs[i] = at::zeros({0}, grad_val.options());`
`1083`	`1083`	`continue;`
`1084`	`1084`	`}`