Arm backend: Add POW operator

Implement support for torch.pow in the MI and BI profile of TOSA.

For MI, the operator works as Pytorch's reference implementation except
for that the base operand cannot be a scalar but must be a tensor.

For BI, the exponent operand must be a scalar and a constant value. The
base operand must be a tensor.

Split the ReplaceScalarWithTensorArgsPass into two subclasses: one for
MI and one for BI. For MI, the pow operator's exponent will converted to
a tensor in case it is a scalar. For BI, the scalar will be kept, but
instead it will be consumed in the InsertTableOpsPass, meaning that the
operator will be converted into a table operation with one input and
output. This still enforces the exponent to be constant for the BI
profile.

Change-Id: I464ab91ff46c0a6ad28d0fb84735a403a74e6323

Author: Martin Lindström

backends/arm/_passes/arm_pass_manager.py

@@ -75,6 +75,11 @@
     ConvertMmToBmmPass,
 )
 from executorch.backends.arm._passes.remove_clone_pass import RemoveClonePass
+
+from executorch.backends.arm._passes.replace_scalar_with_tensor_pass import (
+    ReplaceScalarWithTensorArgPassTOSABI,
+    ReplaceScalarWithTensorArgPassTOSAMI,
+)
 from executorch.backends.arm._passes.scalars_to_attribute_pass import (
     ScalarsToAttributePass,
 )
@@ -87,10 +92,6 @@
 )
 from executorch.backends.arm.tosa_specification import Tosa_0_80, TosaSpecification
 from executorch.backends.transforms.fuse_view_copy import FuseViewCopyTransform
-
-from executorch.backends.transforms.replace_scalar_with_tensor import (
-    ReplaceScalarWithTensorArgPass,
-)
 from executorch.backends.xnnpack._passes.remove_getitem_op import RemoveGetItemPass
 from executorch.exir import ExportedProgram
 from executorch.exir.pass_manager import PassManager
@@ -119,7 +120,7 @@ def _tosa_080_BI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         self.add_pass(ConvertMinMaxPass())
         self.add_pass(ConvertAnyDefaultDimDimsPass())
 
-        self.add_pass(ReplaceScalarWithTensorArgPass())
+        self.add_pass(ReplaceScalarWithTensorArgPassTOSABI())
         self.add_pass(AnnotateDecomposedMatmulPass())
         self.add_pass(QuantizeOperatorArguments())
         self.add_pass(FoldAndAnnotateQParamsPass())  # type: ignore[call-arg]
@@ -148,7 +149,7 @@ def _tosa_080_BI_pipeline(self, exported_program: ExportedProgram) -> GraphModul
         return self._transform(exported_program.graph_module)
 
     def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModule:
-        self.add_pass(ReplaceScalarWithTensorArgPass())
+        self.add_pass(ReplaceScalarWithTensorArgPassTOSAMI())
         self.add_pass(FuseQuantizedActivationPass())
         self.add_pass(RemoveGetItemPass())
         self.add_pass(ConvertSplitToSlicePass())
@@ -205,7 +206,7 @@ def transform_to_backend_pipeline(self, exported_program: ExportedProgram):
             )
 
     def transform_for_annotation_pipeline(self, graph_module: GraphModule):
-        self.add_pass(ReplaceScalarWithTensorArgPass())
+        self.add_pass(ReplaceScalarWithTensorArgPassTOSABI())
         self.add_pass(ScalarsToAttributePass())
         self.add_pass(DecomposeLayerNormPass())
         self.add_pass(DecomposeVarPass())

backends/arm/_passes/insert_table_ops.py

@@ -5,7 +5,8 @@
 
 # pyre-unsafe
 
-from typing import Callable, Dict
+from itertools import chain
+from typing import Callable, cast, Dict, Iterator, Set
 
 import torch
 from executorch.backends.arm._passes.arm_pass_utils import create_node
@@ -17,7 +18,7 @@
 
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch.fx import GraphModule
-
+from torch.fx.node import Node
 from torch.library import impl, Library
 
 lib = Library("tosa", "DEF")
@@ -32,15 +33,13 @@ def _table_impl(*args, **kwargs):  # pyre-ignore
     return args[0].to(dtype=torch.int32)
 
 
-class InsertTableOpsPass(ExportPass):
+class TableOps:
     """
-    For ops in self.table_ops they need to be serialized as a TOSA TABLE. This pass replaces these
-    edge ops with a tosa._table(input: Tensor, target_str: str) where target_str == str(node.target).
-    When lowering the _table node target_str will be used to find the corresponding torch operator
-    which will be used to produce the table values in operators/op_table.py.
+    Helper class for finding the corresponding table operator for a given Node.
     """
 
-    table_ops: Dict[EdgeOpOverload, Callable[[torch.Tensor], torch.Tensor]] = {
+    # Targets that follow a straigtforward one-to-one mapping to their table op
+    unary_table_ops: Dict[EdgeOpOverload, Callable[[torch.Tensor], torch.Tensor]] = {
         exir_ops.edge.aten.ceil.default: torch.ceil,
         exir_ops.edge.aten.exp.default: torch.exp,
         exir_ops.edge.aten.floor.default: torch.floor,
@@ -53,9 +52,52 @@ class InsertTableOpsPass(ExportPass):
         exir_ops.edge.aten.hardswish.default: torch.nn.functional.hardswish,
     }
 
+    # Targets that must be treated explicitly
+    special_table_ops: Set[EdgeOpOverload] = {
+        exir_ops.edge.aten.pow.Tensor_Scalar,
+    }
+
+    def __init__(self, exported_program: ExportedProgram):
+        self.exported_program = exported_program
+
+    def __contains__(self, node: Node) -> bool:
+        return (
+            node.target in self.unary_table_ops or node.target in self.special_table_ops
+        )
+
+    def __getitem__(self, node: Node):
+        target = cast(EdgeOpOverload, node.target)
+        if target in self.unary_table_ops:
+            return self.unary_table_ops[target]
+        elif target in self.special_table_ops:
+            match target:
+                case exir_ops.edge.aten.pow.Tensor_Scalar:
+                    # Exponent is a constant. Embed it into a lambda.
+                    exp = cast(int, node.args[1])
+                    return lambda x: torch.pow(x, exp).flatten()
+                case _:
+                    # Op must be handled if it's inside self.special_ops
+                    raise AssertionError("Unhandled table operation")
+        else:
+            raise KeyError("Table op for {target} does not exist")
+
+    @staticmethod
+    def included_ops() -> Iterator[EdgeOpOverload]:
+        return chain(TableOps.unary_table_ops, TableOps.special_table_ops)
+
+
+class InsertTableOpsPass(ExportPass):
+    """
+    For ops in self.table_ops they need to be serialized as a TOSA TABLE. This pass replaces these
+    edge ops with a tosa._table(input: Tensor, target_str: str) where target_str == str(node.target).
+    When lowering the _table node target_str will be used to find the corresponding torch operator
+    which will be used to produce the table values in operators/op_table.py.
+    """
+
     def __init__(self, exported_program: ExportedProgram) -> None:
         super().__init__()
         self.exported_program = exported_program
+        self.table_ops = TableOps(exported_program)
 
     def register_buffer(self, buffer_name: str, buffer: torch.Tensor) -> None:
         """
@@ -166,7 +208,7 @@ def generate_table_values(
     def call(self, graph_module: GraphModule) -> PassResult:
         modified = False
         for node in graph_module.graph.nodes:
-            if node.op != "call_function" or node.target not in self.table_ops:
+            if node.op != "call_function" or node not in self.table_ops:
                 continue
             input_qparams = node.meta["input_qparams"]
             output_qparams = node.meta["output_qparams"]
@@ -186,7 +228,7 @@ def call(self, graph_module: GraphModule) -> PassResult:
 
                 # Generate table buffer and how much to lshift the table output.
                 buffer, lshift = self.generate_table_values(
-                    torch_op=self.table_ops[node.target],
+                    torch_op=self.table_ops[node],
                     in_quantargs=input_qparams[0],
                     out_quantargs=output_qparams[0],
                 )
@@ -207,7 +249,9 @@ def call(self, graph_module: GraphModule) -> PassResult:
                     output_node = rescale_node
 
                 node.replace_all_uses_with(output_node)
+
             graph_module.graph.erase_node(node)
+
             output_node.meta["input_qparams"] = input_qparams
             output_node.meta["output_qparams"] = output_qparams
             modified = True

backends/arm/_passes/match_arg_ranks_pass.py

@@ -45,6 +45,7 @@ def __init__(self, exported_program):
         exir_ops.edge.aten.sub.Tensor,
         exir_ops.edge.aten.mul.Tensor,
         exir_ops.edge.aten.div.Tensor,
+        exir_ops.edge.aten.pow.Tensor_Tensor,
     ]
 
     def _match_op_rank(self, graph_module, node, arg, max_rank):

backends/arm/_passes/replace_scalar_with_tensor_pass.py

@@ -0,0 +1,51 @@
+# 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 Dict
+
+import torch
+from executorch.backends.transforms.replace_scalar_with_tensor import (
+    ReplaceScalarWithTensorArgPass,
+)
+from executorch.exir.dialects._ops import ops as exir_ops
+
+from executorch.exir.dialects.edge._ops import EdgeOpOverload
+
+
+class ReplaceScalarWithTensorArgPassTOSAMI(ReplaceScalarWithTensorArgPass):
+    scalar_to_tensor_ops: Dict[EdgeOpOverload, EdgeOpOverload] = {
+        exir_ops.edge.aten.add.Scalar: exir_ops.edge.aten.add.Tensor,
+        exir_ops.edge.aten.sub.Scalar: exir_ops.edge.aten.sub.Tensor,
+        exir_ops.edge.aten.mul.Scalar: exir_ops.edge.aten.mul.Tensor,
+        exir_ops.edge.aten.div.Scalar: exir_ops.edge.aten.div.Tensor,
+        exir_ops.edge.aten.pow.Tensor_Scalar: exir_ops.edge.aten.pow.Tensor_Tensor,
+        torch.ops.aten.add.Scalar: torch.ops.aten.add.Tensor,
+        torch.ops.aten.sub.Scalar: torch.ops.aten.sub.Tensor,
+        torch.ops.aten.mul.Scalar: torch.ops.aten.mul.Tensor,
+        torch.ops.aten.div.Scalar: torch.ops.aten.div.Tensor,
+        torch.ops.aten.pow.Tensor_Scalar: torch.ops.aten.pow.Tensor_Tensor,
+    }
+
+    def __init__(self):
+        super().__init__(self.scalar_to_tensor_ops)
+
+
+class ReplaceScalarWithTensorArgPassTOSABI(ReplaceScalarWithTensorArgPass):
+    scalar_to_tensor_ops: Dict[EdgeOpOverload, EdgeOpOverload] = {
+        exir_ops.edge.aten.add.Scalar: exir_ops.edge.aten.add.Tensor,
+        exir_ops.edge.aten.sub.Scalar: exir_ops.edge.aten.sub.Tensor,
+        exir_ops.edge.aten.mul.Scalar: exir_ops.edge.aten.mul.Tensor,
+        exir_ops.edge.aten.div.Scalar: exir_ops.edge.aten.div.Tensor,
+        torch.ops.aten.add.Scalar: torch.ops.aten.add.Tensor,
+        torch.ops.aten.sub.Scalar: torch.ops.aten.sub.Tensor,
+        torch.ops.aten.mul.Scalar: torch.ops.aten.mul.Tensor,
+        torch.ops.aten.div.Scalar: torch.ops.aten.div.Tensor,
+    }
+
+    def __init__(self):
+        super().__init__(self.scalar_to_tensor_ops)

backends/arm/operator_support/tosa_supported_operators.py

@@ -197,6 +197,8 @@ def is_node_supported(
             exir_ops.edge.aten.clone.default,
             exir_ops.edge.aten.unsqueeze_copy.default,
             exir_ops.edge.aten.squeeze_copy.dims,
+            exir_ops.edge.aten.pow.Tensor_Scalar,
+            exir_ops.edge.aten.pow.Tensor_Tensor,
             operator.getitem,
             exir_ops.edge.quantized_decomposed.quantize_per_tensor.default,
             exir_ops.edge.quantized_decomposed.dequantize_per_tensor.default,

backends/arm/operators/__init__.py

@@ -32,6 +32,7 @@
     op_minimum,
     op_mul,
     op_permute,
+    op_pow,
     op_reciprocal,
     op_repeat,
     op_rescale,

backends/arm/operators/op_pow.py

@@ -0,0 +1,57 @@
+# 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 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 executorch.backends.arm.tosa_specification import TosaSpecification
+from serializer.tosa_serializer import TosaOp
+from torch.fx import Node
+
+
+@register_node_visitor
+class PowVisitor_080_MI(NodeVisitor):
+    target = "aten.pow.Tensor_Tensor"
+
+    tosa_specs = [
+        TosaSpecification.create_from_string("TOSA-0.80+MI"),
+    ]
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+    ) -> None:
+        if not (inputs[0].dtype == inputs[1].dtype == output.dtype):
+            raise ValueError(
+                "All inputs and outputs need same dtype."
+                f"Got {inputs[0].dtype=}, {inputs[1].dtype=}, {output.dtype=}"
+            )
+        if inputs[0].dtype not in [ts.DType.FP32, ts.DType.FP16]:
+            raise ValueError(
+                f"All inputs need to be FP32 or FP16. Got {inputs[0].dtype}"
+            )
+
+        tosa_graph.addOperator(
+            TosaOp.Op().POW,
+            [
+                inputs[0].name,
+                inputs[1].name,
+            ],
+            [output.name],
+            None,
+        )

backends/arm/quantizer/quantization_annotator.py

@@ -138,6 +138,7 @@ def _match_pattern(
     torch.ops.aten.hardsigmoid.default,
     torch.ops.aten.hardswish.default,
     torch.ops.aten.full_like.default,
+    torch.ops.aten.pow.Tensor_Scalar,
 ]
 
 _one_to_one_shared_input_qspec = [