Arm backend: fix incorrect output shapes for var and expand (#7414)

Fix incorrect output shapes for var and expand

Decompositions of var and expand_copy produced different output shapes
than the original ops.

Signed-off-by: Oscar Andersson <[email protected]>

Author: oscarandersson8218

backends/arm/_passes/convert_expand_copy_to_repeat.py

@@ -8,7 +8,6 @@
 
 from typing import cast
 
-from executorch.backends.arm.tosa_mapping import extract_tensor_meta
 from executorch.exir.dialects._ops import ops as exir_ops
 from executorch.exir.pass_base import ExportPass
 
@@ -25,14 +24,14 @@ def call_operator(self, op, args, kwargs, meta):
         if op != self.expand_copy:
             return super().call_operator(op, args, kwargs, meta)
 
-        _, shape, _ = extract_tensor_meta(meta.data)
+        input_shape = args[0].data.shape
         multiples = cast(list[int], args[1])
         expanded_rank = len(multiples)
 
-        # Expanded shape is 'shape' front-padded with ones.
-        padding = expanded_rank - len(shape)
+        # Expanded shape is 'input_shape' front-padded with ones.
+        padding = expanded_rank - len(input_shape)
         extended_shape = [
-            shape[i] if i >= 0 else 1 for i in range(-padding, len(shape))
+            input_shape[i] if i >= 0 else 1 for i in range(-padding, len(input_shape))
         ]
 
         # To convert expand arg to repeat arg, non-repeated dims should have

backends/arm/_passes/decompose_var_pass.py

@@ -83,7 +83,7 @@ def call_operator(self, op, args, kwargs, meta):
         sum = super().call_operator(sum_op, (squared_diff, dim, keepdim), {}, meta)
         full = super().call_operator(
             full_op,
-            ([1 for _ in shape], 1 / max(0, N - correction)),
+            ([], 1 / max(0, N - correction)),
             {"dtype": dtype},
             meta,
         )

backends/arm/_passes/match_arg_ranks_pass.py

@@ -90,7 +90,7 @@ def call(self, graph_module: GraphModule) -> PassResult:
                 continue
 
             # Calculate max rank of all inputs to node
-            max_rank = 1
+            max_rank = 0
             for arg in node.args:
                 if isinstance(arg, Node):
                     shape = get_first_fake_tensor(arg).shape

backends/arm/test/ops/test_expand.py

@@ -34,12 +34,13 @@ class TestSimpleExpand(unittest.TestCase):
     class Expand(torch.nn.Module):
         # (input tensor, multiples)
         test_parameters = [
-            (torch.ones(1), (2,)),
-            (torch.ones(1, 4), (1, -1)),
-            (torch.ones(1, 1, 2, 2), (4, 3, -1, 2)),
-            (torch.ones(1), (2, 2, 4)),
-            (torch.ones(3, 2, 4, 1), (-1, -1, -1, 3)),
-            (torch.ones(1, 1, 192), (1, -1, -1)),
+            (torch.rand(1), (2,)),
+            (torch.randn(1, 4), (1, -1)),
+            (torch.rand(1, 1, 2, 2), (4, 3, -1, 2)),
+            (torch.randn(1), (2, 2, 4)),
+            (torch.rand(3, 2, 4, 1), (-1, -1, -1, 3)),
+            (torch.randn(1, 1, 192), (1, -1, -1)),
+            (torch.randn(10, 1, 1, 97), (-1, 4, -1, -1)),
         ]
 
         def forward(self, x: torch.Tensor, multiples: Sequence):