Arm backend: Add adaptive pooling node visitors

backends/arm/_passes/arm_pass_manager.py

@@ -34,6 +34,7 @@
     ConvertToClampPass,
     DecomposeAcoshPass,
     DecomposeAdaptiveAvgPool2dPass,
+    DecomposeAdaptiveMaxPool2dPass,
     DecomposeAddmmPass,
     DecomposeAddSubAlphaPass,
     DecomposeAnyPass,
@@ -527,6 +528,7 @@ def _tosa_pipeline(
             [
                 RewriteUpsamplePass(),
                 RewriteMaxPool2dPass(),
+                DecomposeAdaptiveMaxPool2dPass(),
                 RewriteConvPass(exported_program),
                 RewriteMatmulPass(),
                 RewritePadPass(),

backends/arm/operators/__init__.py

@@ -47,6 +47,7 @@
     op_tanh,
     op_to_dim_order_copy,
     op_tosa_avg_pool2d,
+    op_tosa_avg_pool2d_adaptive,
     op_tosa_conv2d,
     op_tosa_conv3d,
     op_tosa_custom,
@@ -55,6 +56,7 @@
     op_tosa_identity,
     op_tosa_matmul,
     op_tosa_max_pool2d,
+    op_tosa_max_pool2d_adaptive,
     op_tosa_pad,
     op_tosa_rescale,
     op_tosa_resize,

backends/arm/operators/op_tosa_avg_pool2d_adaptive.py

@@ -0,0 +1,71 @@
+# Copyright 2026 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.
+
+from typing import Any, List
+
+import torch
+import tosa_serializer as ts
+
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.operators.operator_validation_utils import (
+    validate_num_inputs,
+    validate_same_dtype,
+    validate_valid_dtype,
+)
+from executorch.backends.arm.tosa.mapping import TosaArg
+
+
+if hasattr(ts.Op, "AVG_POOL2D_ADAPTIVE"):
+
+    @register_node_visitor
+    class AvgPool2dAdaptiveVisitor(NodeVisitor):
+        """Visitor for lowering TOSA AVG_POOL2D_ADAPTIVE operator."""
+
+        target = "tosa.AVG_POOL2D_ADAPTIVE.default"
+
+        def define_node(
+            self,
+            node: torch.fx.Node,
+            tosa_graph: Any,
+            inputs: List[TosaArg],
+            output: TosaArg,
+        ) -> None:
+            validate_num_inputs(self.target, inputs, [7])
+            validate_same_dtype(self.target, [inputs[0], output], ts)
+
+            input_tensor, input_zp, output_zp, kernel, stride, pad, acc_arg = inputs
+
+            supported = [ts.DType.INT8, ts.DType.FP16, ts.DType.FP32, ts.DType.BF16]
+            if self.tosa_spec.support_extension("int16"):
+                supported.append(ts.DType.INT16)
+            if self.tosa_spec.support_extension("fp8e4m3"):
+                supported.append(ts.DType.FP8E4M3)
+            if self.tosa_spec.support_extension("fp8e5m2"):
+                supported.append(ts.DType.FP8E5M2)
+            validate_valid_dtype(
+                self.target, [input_tensor, output], supported, self.tosa_spec
+            )
+
+            attr = ts.TosaSerializerAttribute()
+            attr.AvgPool2dAdaptiveAttribute(acc_type=acc_arg.dtype)
+
+            self._serialize_operator(
+                node,
+                tosa_graph,
+                ts.Op.AVG_POOL2D_ADAPTIVE,
+                [
+                    input_tensor.name,
+                    input_zp.name,
+                    output_zp.name,
+                    kernel.name,
+                    stride.name,
+                    pad.name,
+                ],
+                [output.name],
+                attr,
+            )

backends/arm/operators/op_tosa_max_pool2d_adaptive.py

@@ -0,0 +1,60 @@
+# Copyright 2026 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.
+
+from typing import Any, List
+
+import torch
+import tosa_serializer as ts
+
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.operators.operator_validation_utils import (
+    validate_num_inputs,
+    validate_same_dtype,
+    validate_valid_dtype,
+)
+from executorch.backends.arm.tosa.mapping import TosaArg
+
+
+if hasattr(ts.Op, "MAX_POOL2D_ADAPTIVE"):
+
+    @register_node_visitor
+    class MaxPool2dAdaptiveVisitor(NodeVisitor):
+        """Visitor for lowering TOSA MAX_POOL2D_ADAPTIVE operator."""
+
+        target = "tosa.MAX_POOL2D_ADAPTIVE.default"
+
+        def define_node(
+            self,
+            node: torch.fx.Node,
+            tosa_graph: Any,
+            inputs: List[TosaArg],
+            output: TosaArg,
+        ) -> None:
+            validate_num_inputs(self.target, inputs, [4])
+            validate_same_dtype(self.target, [inputs[0], output], ts)
+
+            input_tensor, kernel, stride, pad = inputs
+
+            supported = [ts.DType.INT8, ts.DType.FP16, ts.DType.FP32, ts.DType.BF16]
+            if self.tosa_spec.support_extension("int16"):
+                supported.append(ts.DType.INT16)
+            validate_valid_dtype(
+                self.target, [input_tensor, output], supported, self.tosa_spec
+            )
+
+            attr = ts.TosaSerializerAttribute()
+            attr.MaxPool2dAdaptiveAttribute(nan_mode=ts.NanPropagationMode.PROPAGATE)
+
+            self._serialize_operator(
+                node,
+                tosa_graph,
+                ts.Op.MAX_POOL2D_ADAPTIVE,
+                [input_tensor.name, kernel.name, stride.name, pad.name],
+                [output.name],
+                attr,
+            )