Arm backend: Add TOSA support for any.default, any.dim and any.dims

backends/arm/_passes/arm_pass_manager.py

@@ -15,6 +15,9 @@
 )
 from executorch.backends.arm._passes.cast_int64_pass import CastInt64ToInt32Pass
 from executorch.backends.arm._passes.conv1d_unsqueeze_pass import Conv1dUnsqueezePass
+from executorch.backends.arm._passes.convert_any_default_dim_dims_pass import (
+    ConvertAnyDefaultDimDimsPass,
+)
 from executorch.backends.arm._passes.convert_expand_copy_to_repeat import (
     ConvertExpandCopyToRepeatPass,
 )
@@ -110,6 +113,7 @@ def _tosa_080_BI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         self.add_pass(ConvertFullLikeToFullPass())
         self.add_pass(ConvertToClampPass())
         self.add_pass(ConvertMinMaxPass())
+        self.add_pass(ConvertAnyDefaultDimDimsPass())
 
         self.add_pass(ReplaceScalarWithTensorArgPass())
         self.add_pass(AnnotateDecomposedMatmulPass())
@@ -155,6 +159,7 @@ def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         self.add_pass(ConvertFullLikeToFullPass())
         self.add_pass(ConvertToClampPass())
         self.add_pass(ConvertMinMaxPass())
+        self.add_pass(ConvertAnyDefaultDimDimsPass())
 
         self.add_pass(AnnotateDecomposedMatmulPass())
         self.add_pass(QuantizeOperatorArguments())

backends/arm/_passes/convert_any_default_dim_dims_pass.py

@@ -0,0 +1,106 @@
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from executorch.exir.dialects._ops import (  # type: ignore[import-not-found]
+    ops as exir_ops,
+)
+from executorch.exir.pass_base import (  # type: ignore[import-not-found]
+    ExportPass,
+    PassResult,
+)
+
+
+class ConvertAnyDefaultDimDimsPass(ExportPass):
+    """
+    Converts any.default, any.dim and any.dims to a sequence of any.dim by unrolling multi-dimensional reduction.
+    Please refer to KeepDimsFalseToSqueezePass for an explanation of this coversion.
+
+    Example 1
+    Original:
+        any()  # x.shape: [dim1, dim2, ..., dimn]
+    After pass:
+        any.dim(dim1, keepdim = True)
+        any.dim(dim2, keepdim = True)
+        ...
+        any.dim(dimn, keepdim = True)
+        squeeze(dim = [dim1, dim2, ...., dimn])
+
+    Example 2
+    Original:
+        any.dim(dim1, keepdim = False)
+    After pass:
+        any.dim(dim1, keepdim = True)
+        squeeze(dim = [dim1])
+
+    Example 3
+    Original:
+        any.dims([dim1, dim2], keepdim = False)
+    After pass:
+        any.dim(dim1, keepdim = True)
+        any.dim(dim2, keepdim = True)
+        squeeze(dim = [dim1, dim2])
+    """
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        modified = False
+        for node in graph_module.graph.nodes:
+            if node.op != "call_function":
+                continue
+            if node.target not in [
+                exir_ops.edge.aten.any.default,
+                exir_ops.edge.aten.any.dim,
+                exir_ops.edge.aten.any.dims,
+            ]:
+                continue
+
+            if len(node.args) == 1:
+                # any.default(input)
+                input_node = (node.args)[0]
+                dims = range(len(input_node.meta["val"].shape))
+                keepdim = False
+            elif len(node.args) == 2:
+                # any.dim/dims(input, dims=dims)
+                input_node, dims = node.args
+                keepdim = False
+            elif len(node.args) == 3:
+                # any.dim/dims(input, dims=dims, keepdim=keepdim)
+                input_node, dims, keepdim = node.args
+            else:
+                raise RuntimeError(
+                    f"Unexpected arg size {len(node.args)} in {node.name}"
+                )
+            try:
+                iter(dims)
+            except:
+                dims = [dims]  # type: ignore[assignment]
+            else:
+                dims = list(dims)  # type: ignore[assignment]
+
+            # Unroll multi-dimensional reduction and keep-dims arg
+            with graph_module.graph.inserting_before(node):
+                for dim in dims:
+                    args = (input_node, dim, True)
+                    input_node = graph_module.graph.create_node(
+                        "call_function", exir_ops.edge.aten.any.dim, args, node.kwargs
+                    )
+
+                if not keepdim:
+                    args = (input_node, dims)  # type: ignore[assignment]
+                    input_node = graph_module.graph.create_node(
+                        "call_function",
+                        exir_ops.edge.aten.squeeze_copy.dims,
+                        args,
+                    )
+
+            node.replace_all_uses_with(input_node)
+            modified = True
+
+        if modified:
+            graph_module.graph.eliminate_dead_code()
+            graph_module.recompile()
+            graph_module = super().call(graph_module).graph_module
+
+        return PassResult(graph_module, modified)

backends/arm/_passes/keep_dims_false_to_squeeze_pass.py

@@ -35,14 +35,15 @@ class KeepDimsFalseToSqueezePass(ExportPass):
     """
 
     # CURRENTLY NOT HANDLED OPS
-    # exir_ops.edge.aten.any.dim,
-    # exir_ops.edge.aten.any.dims,
     # exir_ops.edge.aten.argmax,
     # exir_ops.edge.aten.argmin,
     # exir_ops.edge.aten.prod.dim_int,
 
     # HANDLED OPS
     # exir_ops.edge.aten.sum.dim_IntList
+    # exir_ops.edge.aten.any.default (decomposed in convert_any_default_dim_dims_pass)
+    # exir_ops.edge.aten.any.dim (decomposed in convert_any_default_dim_dims_pass)
+    # exir_ops.edge.aten.any.dims (decomposed in convert_any_default_dim_dims_pass)
     # exir_ops.edge.aten.max.dim (decomposed in convert_minmax_pass)
     # exir_ops.edge.aten.min.dim (decomposed in convert_minmax_pass)
     # exir_ops.edge.aten.amin (decomposed in convert_minmax_pass)

backends/arm/operator_support/tosa_supported_operators.py

@@ -112,6 +112,9 @@ def is_node_supported(
         supported = node.op == "call_function" and node.target in [
             exir_ops.edge.aten.abs.default,
             exir_ops.edge.aten.add.Tensor,
+            exir_ops.edge.aten.any.default,
+            exir_ops.edge.aten.any.dim,
+            exir_ops.edge.aten.any.dims,
             exir_ops.edge.aten.logical_and.default,
             exir_ops.edge.aten.logical_or.default,
             exir_ops.edge.aten.logical_xor.default,
@@ -194,6 +197,9 @@ def is_node_supported(
     ) -> bool:
         if isinstance(self.tosa_spec, Tosa_0_80) and self.tosa_spec.is_U55_subset:
             unsupported_ops = [
+                exir_ops.edge.aten.any.default,
+                exir_ops.edge.aten.any.dim,
+                exir_ops.edge.aten.any.dims,
                 exir_ops.edge.aten.bitwise_and.Tensor,
                 exir_ops.edge.aten.bitwise_or.Tensor,
                 exir_ops.edge.aten.bitwise_xor.Tensor,

backends/arm/operators/__init__.py

@@ -11,6 +11,7 @@
     op_add,
     op_amax,
     op_amin,
+    op_any,
     op_avg_pool2d,
     op_bmm,
     op_cat,

backends/arm/operators/op_any.py

@@ -0,0 +1,53 @@
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+from typing import cast, List
+
+import serializer.tosa_serializer as ts  # type: ignore
+from executorch.backends.arm.operators.node_visitor import (  # type: ignore
+    NodeVisitor,
+    register_node_visitor,
+)
+
+from executorch.backends.arm.tosa_mapping import TosaArg  # type: ignore
+from serializer.tosa_serializer import TosaOp
+from torch.fx import Node
+
+
+@register_node_visitor
+class AnyVisitor(NodeVisitor):
+    target = "aten.any.dim"
+
+    def define_node(
+        self,
+        node: Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+    ) -> None:
+
+        if not (inputs[0].dtype == output.dtype):
+            raise ValueError(
+                "All inputs and outputs need same dtype."
+                f"Got {ts.DTypeNames[inputs[0].dtype]=}, {ts.DTypeNames[output.dtype]=}."
+            )
+        if not (inputs[0].dtype == ts.DType.BOOL):
+            raise ValueError("All inputs need to be BOOL." f"Got {inputs[0].dtype=}")
+
+        input_shape = list(inputs[0].shape)
+        dim = cast(int, inputs[1].number) % len(
+            input_shape
+        )  # process the negative index
+        keep_dim = cast(bool, inputs[2].number if len(inputs) > 2 else False)
+        if not keep_dim:
+            raise ValueError("This case should be handled by ConvertAnyDimDimsPass")
+
+        attr = ts.TosaSerializerAttribute()
+        attr.AxisAttribute(inputs[0].dim_order.index(dim))
+
+        tosa_graph.addOperator(
+            TosaOp.Op().REDUCE_ANY, [inputs[0].name], [output.name], attr
+        )

backends/arm/test/models/test_conformer.py

@@ -34,11 +34,10 @@ class TestConformer(unittest.TestCase):
         "executorch_exir_dialects_edge__ops_aten_max_default": 1,
         "executorch_exir_dialects_edge__ops_aten_eq_Scalar": 2,
         "executorch_exir_dialects_edge__ops_aten_where_self": 4,
-        "executorch_exir_dialects_edge__ops_aten_any_dim": 2,
         "torch.ops.aten._assert_scalar.default": 10,
         "torch.ops.aten._local_scalar_dense.default": 1,
         "torch.ops.aten.scalar_tensor.default": 2,
-        "torch.ops.higher_order.executorch_call_delegate": 4,
+        "torch.ops.higher_order.executorch_call_delegate": 6,
     }
 
     dim = 16