Arm backend: Add support for amax/max/amin/min

backends/arm/_passes/arm_pass_manager.py

@@ -21,6 +21,7 @@
 from executorch.backends.arm._passes.convert_full_like_to_full_pass import (
     ConvertFullLikeToFullPass,
 )
+from executorch.backends.arm._passes.convert_minmax_pass import ConvertMinMaxPass
 from executorch.backends.arm._passes.convert_split_to_slice import (
     ConvertSplitToSlicePass,
 )
@@ -106,6 +107,7 @@ def _tosa_080_BI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         self.add_pass(ConvertMeanDimToAveragePoolPass())
         self.add_pass(ConvertFullLikeToFullPass())
         self.add_pass(ConvertToClampPass())
+        self.add_pass(ConvertMinMaxPass())
 
         self.add_pass(ReplaceScalarWithTensorArgPass())
         self.add_pass(AnnotateDecomposedMatmulPass())
@@ -147,6 +149,7 @@ def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         self.add_pass(DecomposeSoftmaxesPass())
         self.add_pass(ConvertFullLikeToFullPass())
         self.add_pass(ConvertToClampPass())
+        self.add_pass(ConvertMinMaxPass())
 
         self.add_pass(AnnotateDecomposedMatmulPass())
         self.add_pass(QuantizeOperatorArguments())
@@ -190,4 +193,5 @@ def transform_for_annotation_pipeline(self, graph_module: GraphModule):
         self.add_pass(DecomposeMeanDimPass())
         self.add_pass(DecomposeDivPass())
         self.add_pass(DecomposeSoftmaxesPass())
+        self.add_pass(ConvertMinMaxPass())
         return self._transform(graph_module)

backends/arm/_passes/convert_minmax_pass.py

@@ -0,0 +1,136 @@
+# 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 ops as exir_ops
+from executorch.exir.pass_base import ExportPass, PassResult
+
+
+class ConvertMinMaxPass(ExportPass):
+    """
+    Converts min/max to amin/amax and unrolls multi-dimensional reduction and keep-dims arg to be
+    TOSA compliant.
+
+    The difference between max/min and amax/amin is (from pytorch docs):
+        - amax/amin supports reducing on multiple dimensions,
+        - amax/amin does not return indices,
+        - amax/amin evenly distributes gradient between equal values, while max(dim)/min(dim)
+          propagates gradient only to a single index in the source tensor.
+    Since we do not care about gradients post training, convert min/max ops to amin/amax as long as
+    the indices are not used.
+
+    Original:
+        amax([dim1, dim2], keepdim = False)
+    After pass:
+        amax(dim1, keepdim = True)
+        amax(dim2, keepdim = True)
+        squeeze(dim = [dim1, dim2])
+    """
+
+    def check_argmax(self, node):
+        """
+        Raises a RuntimeError if the argmax value returned by the min/max op is used in the graph.
+        """
+        if node.target in [torch.ops.aten.max.dim, torch.ops.aten.min.dim]:
+            no_argmax = len(node.users) == 1
+            no_argmax_users = (len(node.users) == 2) and (
+                len(list(node.users)[1].users) == 0
+            )
+            if not (no_argmax or no_argmax_users):
+                raise RuntimeError("Argmax is not supported by the arm_quantizer")
+
+    def get_variables(self, node):
+        """Returns variables specific for each op handled by the pass."""
+        if node.target in [
+            exir_ops.edge.aten.amax.default,
+            exir_ops.edge.aten.amin.default,
+        ]:
+            replace_node = node
+            op = node.target
+            squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
+        elif node.target == exir_ops.edge.aten.max.dim:
+            replace_node = list(node.users)[0]
+            op = exir_ops.edge.aten.amax.default
+            squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
+        elif node.target == exir_ops.edge.aten.min.dim:
+            replace_node = list(node.users)[0]
+            op = exir_ops.edge.aten.amin.default
+            squeeze_op = exir_ops.edge.aten.squeeze_copy.dims
+        elif node.target == torch.ops.aten.max.dim:
+            replace_node = list(node.users)[0]
+            op = torch.ops.aten.amax.default
+            squeeze_op = torch.ops.aten.squeeze.dims
+        elif node.target == torch.ops.aten.min.dim:
+            replace_node = list(node.users)[0]
+            op = torch.ops.aten.amin.default
+            squeeze_op = torch.ops.aten.squeeze.dims
+        else:
+            raise RuntimeError(
+                f"{node.name} is not an accepted target for ConvertMinMaxPass()"
+            )
+
+        return (replace_node, op, squeeze_op)
+
+    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.amax.default,
+                exir_ops.edge.aten.amin.default,
+                exir_ops.edge.aten.max.dim,
+                exir_ops.edge.aten.min.dim,
+                torch.ops.aten.max.dim,
+                torch.ops.aten.min.dim,
+            ]:
+                continue
+
+            self.check_argmax(
+                node
+            )  # TODO: MLETORCH-718 : Quantization of indices in arm_quantizer
+            replace_node, op, squeeze_op = self.get_variables(node)
+
+            # Unwrap args
+            if len(node.args) == 2:
+                input_node, dims = node.args
+                keepdims = False
+            elif len(node.args) == 3:
+                input_node, dims, keepdims = node.args
+            else:
+                raise RuntimeError(f"Unexpected arg size in {node.name}")
+
+            try:
+                iter(dims)
+            except:
+                dims = [dims]
+            else:
+                dims = list(dims)
+
+            # 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", op, args, node.kwargs
+                    )
+
+                if not keepdims:
+                    input_node = graph_module.graph.create_node(
+                        "call_function",
+                        squeeze_op,
+                        (input_node, dims),
+                    )
+
+            replace_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, True)

backends/arm/_passes/keep_dims_false_to_squeeze_pass.py

@@ -1,5 +1,4 @@
 # Copyright 2024-2025 Arm Limited and/or its affiliates.
-# All rights reserved.
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
@@ -36,18 +35,18 @@ class KeepDimsFalseToSqueezePass(ExportPass):
     """
 
     # CURRENTLY NOT HANDLED OPS
-    # exir_ops.edge.aten.amax,
-    # exir_ops.edge.aten.amin,
     # 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.max.dim,
-    # exir_ops.edge.aten.min.dim,
     # exir_ops.edge.aten.prod.dim_int,
 
     # HANDLED OPS
     # exir_ops.edge.aten.sum.dim_IntList
+    # 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)
+    # exir_ops.edge.aten.amax (decomposed in convert_minmax_pass)
     # exir_ops.edge.aten.var.correction (decomposed in decompose_var_pass)
     # exir_ops.edge.aten.var.dim (decomposed in decompose_var_pass)
     # exir_ops.edge.aten.mean.dim (decomposed in decompose_meandim_pass)

backends/arm/operator_support/__init__.py

@@ -7,6 +7,7 @@
 
 from . import (  # noqa
     convolution_support,
+    minmax_support,
     pool_2d_support,
     reduce_sum_support,
     right_shift_support,

backends/arm/operator_support/minmax_support.py

@@ -0,0 +1,37 @@
+# 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.fx as fx
+from executorch.backends.arm.operator_support.tosa_supported_operators import (
+    register_tosa_support_check,
+    SupportedTOSAOperatorCheck,
+)
+from executorch.backends.arm.tosa_specification import TosaSpecification
+from executorch.exir.dialects._ops import ops as exir_ops
+
+
+@register_tosa_support_check
+class MinMaxSupported(SupportedTOSAOperatorCheck):
+    targets = [
+        exir_ops.edge.aten.max.dim,
+        exir_ops.edge.aten.min.dim,
+    ]
+
+    # TODO : "MLETORCH-718 : Quantization of indices in arm_quantizer"
+    tosa_specs = [
+        TosaSpecification.create_from_string("TOSA-0.80+MI"),
+    ]
+
+    def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification):
+        if node.target in [exir_ops.edge.aten.max.dim, exir_ops.edge.aten.min.dim]:
+            no_argmax = len(node.users) == 1
+            no_argmax_users = (len(node.users) == 2) and (
+                len(list(node.users)[1].users) == 0
+            )
+
+            if not (no_argmax or no_argmax_users):
+                return False
+
+        return True

backends/arm/operator_support/tosa_supported_operators.py

@@ -169,6 +169,8 @@ def is_node_supported(
             exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
             exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,
             exir_ops.edge.aten.constant_pad_nd.default,
+            exir_ops.edge.aten.amax.default,
+            exir_ops.edge.aten.amin.default,
         ]
 
         return supported
@@ -191,6 +193,8 @@ def is_node_supported(
                 exir_ops.edge.aten.bitwise_and.Tensor,
                 exir_ops.edge.aten.bitwise_or.Tensor,
                 exir_ops.edge.aten.bitwise_xor.Tensor,
+                exir_ops.edge.aten.amax.default,
+                exir_ops.edge.aten.amin.default,
             ]
 
             if node.target in unsupported_ops:

backends/arm/operators/__init__.py

@@ -9,6 +9,8 @@
     node_visitor,
     op_abs,
     op_add,
+    op_amax,
+    op_amin,
     op_avg_pool2d,
     op_bmm,
     op_cat,
@@ -24,9 +26,9 @@
     op_le,
     op_log,
     op_lt,
-    op_max,
     op_max_pool2d,
-    op_min,
+    op_maximum,
+    op_minimum,
     op_mul,
     op_permute,
     op_reciprocal,

backends/arm/operators/op_amax.py

@@ -0,0 +1,45 @@
+# 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.
+from typing import List
+
+import serializer.tosa_serializer as ts
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.tosa_mapping import TosaArg
+from serializer.tosa_serializer import TosaOp
+from torch.fx import Node
+
+
+@register_node_visitor
+class MaxVisitor(NodeVisitor):
+    target = "aten.amax.default"
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+    ) -> None:
+
+        input = inputs[0]
+        dim = inputs[1].number
+        keep_dims = inputs[2].number
+        if not keep_dims:
+            raise RuntimeError(
+                "TOSA only supports keepdims == True; Did you run the convert_minmax pass?"
+            )
+
+        attr = ts.TosaSerializerAttribute()
+        attr.AxisAttribute(input.dim_order.index(dim))
+
+        tosa_graph.addOperator(
+            TosaOp.Op().REDUCE_MAX, [input.name], [output.name], attr
+        )

backends/arm/operators/op_amin.py

@@ -0,0 +1,45 @@
+# 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.
+from typing import List
+
+import serializer.tosa_serializer as ts
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.tosa_mapping import TosaArg
+from serializer.tosa_serializer import TosaOp
+from torch.fx import Node
+
+
+@register_node_visitor
+class MinVisitor(NodeVisitor):
+    target = "aten.amin.default"
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+    ) -> None:
+
+        input = inputs[0]
+        dim = inputs[1].number
+        keep_dims = inputs[2].number
+        if not keep_dims:
+            raise RuntimeError(
+                "TOSA only supports keepdims == True; Did you run the convert_minmax pass?"
+            )
+
+        attr = ts.TosaSerializerAttribute()
+        attr.AxisAttribute(input.dim_order.index(dim))
+
+        tosa_graph.addOperator(
+            TosaOp.Op().REDUCE_MIN, [input.name], [output.name], attr
+        )

backends/arm/quantizer/quantization_annotator.py

@@ -175,6 +175,8 @@ def _match_pattern(
     torch.ops.aten.contiguous.default,
     torch.ops.aten.upsample_nearest2d.vec,
     torch.ops.aten.pad.default,
+    torch.ops.aten.amax.default,
+    torch.ops.aten.amin.default,
 ]
 
 # Operators that can inherit the quantization specs from its parent node