Arm backend: Add meandim support for rank > 4

backends/arm/_passes/decompose_meandim_pass.py

@@ -3,6 +3,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+from copy import copy
 from math import prod
 
 import torch
@@ -75,35 +76,47 @@ def call_operator(self, op, args, kwargs, meta):
             return super().call_operator(op, args, kwargs, meta)
 
         x = get_node_arg(args, 0)
-        input_shape = x.data.size()
-        output_shape = meta["val"].size()
+        input_shape = list(x.data.shape)
+        output_shape = list(meta["val"].shape)
         dims_to_reduce = get_node_arg(args, 1)
         dims_to_reduce = [dim % len(input_shape) for dim in dims_to_reduce]
+        dims_to_reduce = [dim for dim in dims_to_reduce if input_shape[dim] != 1]
 
         dtype = meta["val"].dtype
         view_op = get_view(op)
 
-        if len(input_shape) > 4:
-            raise NotImplementedError(
-                f"{op} with rank > 4 is currently not supported for the TOSA backend."
-            )
+        # Reshape to 4D
+        if len(input_shape) != 4:
+            new_shape = copy(input_shape)
+
+            while len(new_shape) < 4:
+                new_shape.insert(0, 1)
+                dims_to_reduce = [dim + 1 for dim in dims_to_reduce]
 
-        # Unsqueeze to 4D
-        if len(input_shape) < 4:
-            pad_n = 4 - len(input_shape)
-            new_shape = [1] * pad_n + list(input_shape)
-            dims_to_reduce = [dim + pad_n for dim in dims_to_reduce]
+            while len(new_shape) > 4:
+                i = new_shape.pop(0)
+                new_shape[0] = new_shape[0] * i
+                dims_to_reduce = [dim - 1 for dim in dims_to_reduce]
 
             x = super().call_operator(view_op, (x, new_shape), {}, meta, True)
 
         # Reduce (h,w) dims by avg pool if possible
         x, dims_to_reduce = self._reduce_by_average_pool(op, x, dims_to_reduce, meta)
 
+        # Reshape back to 5D if necessary
+        if len(input_shape) > 4:
+            original_dims = input_shape[0:-4]
+            temp_shape = list(x.data.shape)[1:]
+            temp_shape = original_dims + temp_shape
+            dims_to_reduce = [dim + len(original_dims) - 1 for dim in dims_to_reduce]
+
+            x = super().call_operator(view_op, (x, temp_shape), {}, meta, True)
+
         # Reduce remaining dims by sum
         x = self._reduce_by_sum(op, x, dims_to_reduce, meta, dtype)
 
         # Reshape to correct output shape if necessary
-        if x.data.size() != output_shape:
+        if list(x.data.shape) != output_shape:
             x = super().call_operator(view_op, (x, output_shape), {}, meta, True)
 
         return x

backends/arm/test/ops/test_mean_dim.py

@@ -195,6 +195,21 @@ class MeanDim(torch.nn.Module):
             (-4, -3, -2, -1),
             False,
         ),
+        "rank5_01234": lambda: (
+            torch.rand(1, 1, 7, 3, 2),
+            (-5, -4, -3, -2, -1),
+            False,
+        ),
+        "rank5_234": lambda: (
+            torch.rand(1, 1, 7, 3, 2),
+            (-3, -2, -1),
+            False,
+        ),
+        "rank5_12": lambda: (
+            torch.rand(1, 1, 7, 3, 2),
+            (1, 2),
+            False,
+        ),
         "u55_avg_pool_not_supported": lambda: (
             torch.rand(1, 1, 1, 257),
             (0, 1, 2, 3),
@@ -236,7 +251,14 @@ def test_mean_dim_tosa_BI(test_data):
     pipeline.run()
 
 
-@common.parametrize("test_data", MeanDim.test_data_suite)
+xfails = {
+    "rank5_01234": "Rank 5 graph input currently not supported in EthosUBackend (passes since CHW are all averaged over so data order does not matter in this case)",
+    "rank5_234": "Rank 5 graph input currently not supported in EthosUBackend (passes since CHW are all averaged over so data order does not matter in this case)",
+    "rank5_12": "Rank 5 graph input currently not supported in EthosUBackend",
+}
+
+
+@common.parametrize("test_data", MeanDim.test_data_suite, xfails=xfails, strict=False)
 @common.XfailIfNoCorstone300
 def test_mean_dim_u55_BI(test_data):
     test_data, dim, keep_dim = test_data()
@@ -256,7 +278,7 @@ def test_mean_dim_u55_BI(test_data):
     pipeline.run()
 
 
-@common.parametrize("test_data", MeanDim.test_data_suite)
+@common.parametrize("test_data", MeanDim.test_data_suite, xfails=xfails, strict=False)
 @common.XfailIfNoCorstone320
 def test_mean_dim_u85_BI(test_data):
     test_data, dim, keep_dim = test_data()