Arm backend: Decompose sub/add with alpha!=1 (#14932)

This was previously not supported, causing crashes in quantization, and
incorrect output in floating point.

Signed-off-by: Erik Lundell <[email protected]>

Author: Erik-Lundell

backends/arm/_passes/__init__.py

@@ -27,6 +27,7 @@
 from .convert_to_clamp import ConvertToClampPass  # noqa
 from .decompose_acosh_pass import DecomposeAcoshPass  # noqa
 from .decompose_adaptive_avg_pool2d_pass import DecomposeAdaptiveAvgPool2dPass  # noqa
+from .decompose_add_sub_alpha_pass import DecomposeAddSubAlphaPass  # noqa
 from .decompose_addmm_pass import DecomposeAddmmPass  # noqa
 from .decompose_asin_and_acos_pass import DecomposeAsinAndAcosPass  # noqa
 from .decompose_asinh_pass import DecomposeAsinhPass  # noqa

backends/arm/_passes/arm_pass_manager.py

@@ -36,6 +36,7 @@
     DecomposeAcoshPass,
     DecomposeAdaptiveAvgPool2dPass,
     DecomposeAddmmPass,
+    DecomposeAddSubAlphaPass,
     DecomposeAsinAndAcosPass,
     DecomposeAsinhPass,
     DecomposeAtanhPass,
@@ -262,6 +263,7 @@ def _tosa_FP_pipeline(self, exported_program: ExportedProgram) -> GraphModule:
         )
         self.add_pass(DecomposeNotEqualPass())
         self.add_pass(DecomposeDivPass())
+        self.add_pass(DecomposeAddSubAlphaPass())
         self.add_pass(DecomposeSoftmaxPass())
         self.add_pass(DecomposeGeluPass())
         self.add_pass(ConvertFullLikeToFullPass())
@@ -334,6 +336,7 @@ def transform_for_annotation_pipeline(self, graph_module: GraphModule):
         self.add_pass(DecomposeSignPass())
         self.add_pass(DecomposeAddmmPass())
         self.add_pass(DecomposeDivTensorModePass())
+        self.add_pass(DecomposeAddSubAlphaPass())
         self.add_pass(ReplaceScalarWithTensorArgPassTOSABI())
         self.add_pass(ScalarsToAttributePass())
         self.add_pass(DecomposeGroupNormPass())

backends/arm/_passes/decompose_add_sub_alpha_pass.py

@@ -0,0 +1,94 @@
+# 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 __future__ import annotations
+
+import numbers
+from typing import Set, Type
+
+import torch
+from executorch.backends.arm._passes import ArmPass
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass
+
+
+_ADD_OPS = (
+    exir_ops.edge.aten.add.Tensor,
+    torch.ops.aten.add.Tensor,
+)
+
+_SUB_OPS = (
+    exir_ops.edge.aten.sub.Tensor,
+    torch.ops.aten.sub.Tensor,
+)
+
+
+def _get_ops(op):
+    if op in _ADD_OPS:
+        if op is exir_ops.edge.aten.add.Tensor:
+            return (
+                exir_ops.edge.aten.mul.Tensor,
+                exir_ops.edge.aten.full.default,
+                exir_ops.edge.aten.add.Tensor,
+            )
+        return (
+            torch.ops.aten.mul.Tensor,
+            torch.ops.aten.full.default,
+            torch.ops.aten.add.Tensor,
+        )
+    if op in _SUB_OPS:
+        if op is exir_ops.edge.aten.sub.Tensor:
+            return (
+                exir_ops.edge.aten.mul.Tensor,
+                exir_ops.edge.aten.full.default,
+                exir_ops.edge.aten.sub.Tensor,
+            )
+        return (
+            torch.ops.aten.mul.Tensor,
+            torch.ops.aten.full.default,
+            torch.ops.aten.sub.Tensor,
+        )
+    raise RuntimeError(f"Unsupported operator {op}")
+
+
+def _should_decompose(alpha) -> bool:
+    if isinstance(alpha, numbers.Number):
+        return alpha != 1
+    return False
+
+
+class DecomposeAddSubAlphaPass(ArmPass):
+    """Rewrite add/sub with alpha into a mul followed by add/sub."""
+
+    _passes_required_after: Set[Type[ExportPass]] = set()
+
+    def call_operator(self, op, args, kwargs, meta, updated: bool | None = False):
+        if op not in _ADD_OPS + _SUB_OPS:
+            return super().call_operator(op, args, kwargs, meta, updated)
+
+        alpha = kwargs.get("alpha", 1)
+        if not _should_decompose(alpha):
+            return super().call_operator(op, args, kwargs, meta, updated)
+
+        mul_op, full_op, binary_op = _get_ops(op)
+        lhs, rhs = args
+
+        alpha_full = super().call_operator(
+            full_op, ((1,), float(alpha)), {}, meta, updated=True
+        )
+        scaled_rhs = super().call_operator(
+            mul_op,
+            (rhs, alpha_full),
+            {},
+            meta,
+            updated=True,
+        )
+        return super().call_operator(
+            binary_op,
+            (lhs, scaled_rhs),
+            {},
+            meta,
+            updated=True,
+        )

backends/arm/test/ops/test_add.py

@@ -78,7 +78,7 @@ def forward(self, x: torch.Tensor, y: torch.Tensor):
 
 class Add3(torch.nn.Module):
     def forward(self, x: torch.Tensor, y: torch.Tensor):
-        return x + y
+        return torch.add(x, y, alpha=1.5)
 
     test_data: list[input_t2] = {
         "3d_randn_diff_rank": lambda: (torch.randn(1, 4, 5), torch.randn(4, 1)),

backends/arm/test/ops/test_sub.py

@@ -79,6 +79,11 @@ def forward(self, x: torch.Tensor, y: torch.Tensor):
         return x - y
 
 
+class SubAlpha(torch.nn.Module):
+    def forward(self, x: torch.Tensor, y: torch.Tensor):
+        return torch.sub(x, y, alpha=5)
+
+
 class SubTan(torch.nn.Module):
 
     def forward(self, x: torch.Tensor, y: torch.Tensor):
@@ -115,6 +120,18 @@ def test_sub_tensor_tosa_FP_2(test_data: Tuple[torch.Tensor, torch.Tensor]):
     pipeline.run()
 
 
+@common.parametrize("test_data", sub_tan_test_data)
+def test_sub_tensor_tosa_FP_alpha(test_data: Tuple[torch.Tensor, torch.Tensor]):
+    """Test Two-Operand Subtraction with alpha (TOSA FP)"""
+    pipeline = TosaPipelineFP[input_t2](
+        SubAlpha(),
+        test_data(),
+        aten_op,
+        exir_op,
+    )
+    pipeline.run()
+
+
 @common.parametrize("test_data", sub_test_data)
 def test_sub_tensor_tosa_INT(test_data):
     """Test Subtraction (TOSA INT)"""
@@ -138,6 +155,15 @@ def test_sub_tensor_tosa_INT_3(test_data: Tuple[torch.Tensor, torch.Tensor]):
     pipeline.run()
 
 
+@common.parametrize("test_data", sub_tan_test_data)
+def test_sub_tensor_tosa_INT_alpha(test_data: Tuple[torch.Tensor, torch.Tensor]):
+    """Test Two-Operand Subtraction with alpha (TOSA INT)"""
+    pipeline = TosaPipelineINT[input_t2](
+        SubAlpha(), test_data(), aten_op, exir_op, qtol=0
+    )
+    pipeline.run()
+
+
 @common.parametrize("test_data", sub_test_data)
 @common.XfailIfNoCorstone300
 def test_sub_tensor_u55_INT(test_data):