[dynamic shapes] guard_or_false for cat, repeat (pytorch#155290)

Summary:
assumes:
- specified repeats are non-negative
- 1d cat arguments like [u0] aren't non-zero sized (replaces existing size-oblivious)

Test Plan:
test_export

Rollback Plan:

Differential Revision: D76092011

Pull Request resolved: pytorch#155290
Approved by: https://github.com/laithsakka

Author: pianpwk

test/dynamo/test_misc.py

@@ -579,6 +579,16 @@ def fn(x):
             self.assertEqual(obj.y, x + 1)
         self.assertEqual(obj.__dict__.keys(), {"pfx_x", "pfx_y"})
 
+    @torch._dynamo.config.patch(capture_scalar_outputs=True)
+    def test_unbacked_repeat_cat(self):
+        def f(x, n):
+            m = x.item()
+            x = torch.empty(x).repeat(n)  # s0*u0
+            return torch.cat([x, x], dim=0)
+
+        fn = torch.compile(f, backend="eager", dynamic=True, fullgraph=True)
+        fn(torch.tensor([5]), 5)
+
     def test_tensor_setattr_getset_descriptor(self):
         # Tensor attribute `real` has special getter/setter for complex dtype.
         def f(x):

torch/_meta_registrations.py

@@ -4078,6 +4078,11 @@ def meta_repeat(self, repeats):
         len(repeats) >= self.dim(),
         lambda: "Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor",
     )
+    for i, rep in enumerate(repeats):
+        torch._check(
+            rep >= 0,
+            lambda: f"Repeats cannot be negative, found {rep} at index {i}",
+        )
     # Add new leading dimensions to the tensor if the
     # number of target dimensions is larger than the
     # number of source dimensions.

torch/_refs/__init__.py

@@ -2769,7 +2769,10 @@ def cat_compute_output_memory_format(inputs):
 
     utils.check_same_device(*tensors, allow_cpu_scalar_tensors=False)
 
-    from torch.fx.experimental.symbolic_shapes import guard_size_oblivious
+    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
     # arbitrary tensor and check that all tensors match this tensor.  However,
@@ -2830,7 +2833,7 @@ def cat_compute_output_memory_format(inputs):
             )
         else:
             # Remove inputs that are 1-D, zero size
-            if tensor.ndim == 1 and guard_size_oblivious(tensor.shape[0] == 0):
+            if tensor.ndim == 1 and guard_or_false(tensor.shape[0] == 0):
                 continue
             # Don't bother checking size match, prims.cat will handle it
             filtered.append(tensor)