[JIT] Cat shape analysis fix for -1 dim (pytorch#72616)

Summary:
Pull Request resolved: pytorch#72616

Following logic [here](https://codebrowser.bddppq.com/pytorch/pytorch/aten/src/ATen/WrapDimUtils.h.html#_ZN2atL19legacy_cat_wrap_dimElN3c108ArrayRefINS_6TensorEEE)

The prior version was checking if dim was not None, we should be checking if it's None. Strangely, the shape analysis still worked because the negative indexing just wrapped around, however it would lead to errors in executing shape functions. In follow up I will extend shape functions testing to actually invoke shape functions as well to catch this type of bug.

This wasn't caught in the nnc opinfo tests bc nnc was already failing for cat single-node :'(

Test Plan: Imported from OSS

Reviewed By: Krovatkin

Differential Revision: D34117930

Pulled By: eellison

fbshipit-source-id: 2c60430d7144dc828a6a4789e0015b83153f7a32

test/jit/test_symbolic_shape_analysis.py

@@ -460,21 +460,27 @@ def test_sym_ir_parsing(self):
         self.assertEqual(out, [-2, -3])
 
     def test_stitching_concat(self):
+
         @torch.jit.script
-        def foo(a, b, x, y):
+        def foo1(a, b, x, y):
             return (a / b) + torch.cat([x, y])
 
-        g = foo.graph
-        for inp in foo.graph.inputs():
-            inp.setType(inp.type().with_sizes([None, None]))
+        @torch.jit.script
+        def foo2(a, b, x, y):
+            return (a / b) + torch.cat([x, y], dim=-2)
+
+        for foo in [foo1, foo2]:
+            g = foo.graph
+            for inp in foo.graph.inputs():
+                inp.setType(inp.type().with_sizes([None, None]))
 
-        shape_compute_graph = torch._C._jit_pass_propagate_shapes_on_graph_and_build_compute(foo.graph)
-        nodes = [g.findNode("aten::div")] + [g.findNode("aten::add")] + [g.findNode("aten::cat")]
+            shape_compute_graph = torch._C._jit_pass_propagate_shapes_on_graph_and_build_compute(foo.graph)
+            nodes = [g.findNode("aten::div")] + [g.findNode("aten::add")] + [g.findNode("aten::cat")]
 
-        inps = [1, 10], [20, 10], [15, 1], [5, 1]
-        output_shapes = [[20, 10], [20, 10], [20, 1]]
+            inps = [1, 10], [20, 10], [15, 1], [5, 1]
+            output_shapes = [[20, 10], [20, 10], [20, 1]]
 
-        self.checkSymShapeCompute(shape_compute_graph, nodes, output_shapes, inps)
+            self.checkSymShapeCompute(shape_compute_graph, nodes, output_shapes, inps)
 
     @unittest.skipIf(not hasattr(torch.jit, "_shapes"), "shape functions not loaded in python")
     def test_shape_function_includes(self):

torch/csrc/jit/runtime/shape_functions.h

@@ -93,12 +93,12 @@ def check_cat_no_zero_dim(tensors: List[List[int]]):
     for tensor in tensors:
         assert len(tensor) > 0
 
-
 def legacy_cat_wrap_dim(dim: int, tensor_sizes: List[List[int]]):
     out_dim: Optional[int] = None
     for size in tensor_sizes:
-        if len(size) != 0 and size != [0] and out_dim is not None:
-            out_dim = maybe_wrap_dim(dim, len(size))
+        if not (len(size) == 1 and size[0] == 0):
+            if out_dim is None:
+                out_dim = maybe_wrap_dim(dim, len(size))
     if out_dim is None:
         out_dim = dim
     return out_dim