Arm backend: Add support to neg.default (#10653)

This patch adds support to aten.neg.default via TOSA NEGATE and added
test cases.

Signed-off-by: Fang-Ching <[email protected]>

Author: fumchin

backends/arm/operator_support/tosa_supported_operators.py

@@ -194,6 +194,7 @@ def is_node_supported(
             exir_ops.edge.aten.mul.Tensor,
             exir_ops.edge.aten.ne.Tensor,
             exir_ops.edge.aten.ne.Scalar,
+            exir_ops.edge.aten.neg.default,
             exir_ops.edge.aten.add.Scalar,
             exir_ops.edge.aten.sub.Scalar,
             exir_ops.edge.aten.mul.Scalar,
@@ -311,6 +312,7 @@ class CheckProperQuantization(OperatorSupportBase):
         exir_ops.edge.aten.max_pool2d_with_indices.default,
         exir_ops.edge.aten.mm.default,
         exir_ops.edge.aten.mul.Tensor,
+        exir_ops.edge.aten.neg.default,
         exir_ops.edge.aten.relu.default,
         exir_ops.edge.aten.sub.Tensor,
         exir_ops.edge.aten.upsample_bilinear2d.vec,

backends/arm/operators/__init__.py

@@ -31,6 +31,7 @@
     op_maximum,
     op_minimum,
     op_mul,
+    op_neg,
     op_permute,
     op_pow,
     op_reciprocal,

backends/arm/operators/op_neg.py

@@ -0,0 +1,78 @@
+# 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 torch.fx
+
+import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore
+from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
+    get_input_qparams,
+    get_output_qparams,
+)
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+
+from executorch.backends.arm.tosa_mapping import TosaArg
+
+
+def get_negate_zero_points(node: torch.fx.Node, dtype: ts.DType) -> tuple[int, int]:
+    """
+    Returns (input1_zp, output_zp) for TOSA NEGATE.
+    Must be zero for non-int8 types.
+    """
+    if dtype == ts.DType.INT8:
+        return (
+            get_input_qparams(node)[0].zp,
+            get_output_qparams(node)[0].zp,
+        )
+    return (0, 0)
+
+
+@register_node_visitor
+class NegVisitor(NodeVisitor):
+    target = "aten.neg.default"
+
+    supported_dtypes = {
+        ts.DType.INT8,
+        ts.DType.INT16,
+        ts.DType.INT32,
+        ts.DType.FP16,
+        ts.DType.BF16,
+        ts.DType.FP32,
+    }
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+    ) -> None:
+
+        if inputs[0].dtype not in self.supported_dtypes:
+            raise ValueError(f"Unsupported dtype for NEGATE: {inputs[0].dtype}")
+
+        if inputs[0].dtype != output.dtype:
+            raise ValueError(
+                "All inputs and output need same dtype."
+                f"Got {inputs[0].dtype=}, {output.dtype=}"
+            )
+        input_zp, output_zp = get_negate_zero_points(node, inputs[0].dtype)
+
+        attr = ts.TosaSerializerAttribute()
+        attr.NegateAttribute(input1_zp=input_zp, output_zp=output_zp)
+        tosa_graph.addOperator(
+            ts.TosaOp.Op().NEGATE,
+            [inputs[0].name],
+            [output.name],
+            attributes=attr,
+        )

backends/arm/quantizer/quantization_annotator.py

@@ -375,6 +375,9 @@ def any_or_hardtanh_min_zero(n: Node):
             )
         ]
         quant_properties.quant_output = _QuantProperty(0, shared_qspec)  # type: ignore[arg-type]
+    elif node.target in (torch.ops.aten.neg.default,):
+        quant_properties.quant_inputs = [_QuantProperty(0, input_act_qspec)]
+        quant_properties.quant_output = _QuantProperty(0, input_act_qspec)
     elif node.target in _one_to_one:
         quant_properties.quant_inputs = [_QuantProperty(0, input_act_qspec)]
         quant_properties.quant_output = _QuantProperty(0, output_act_qspec)

backends/arm/test/ops/test_neg.py

@@ -0,0 +1,66 @@
+# 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 Dict, Tuple
+
+import torch
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.test_pipeline import (
+    EthosU55PipelineBI,
+    EthosU85PipelineBI,
+    TosaPipelineBI,
+    TosaPipelineMI,
+)
+
+input_t1 = Tuple[torch.Tensor]
+
+
+class Neg(torch.nn.Module):
+
+    aten_op = "torch.ops.aten.neg.default"
+    exir_op = "executorch_exir_dialects_edge__ops_aten_neg_default"
+
+    test_data: Dict[str, input_t1] = {
+        "rank_1_ramp": (torch.arange(-16, 16, 0.2),),
+        "rank_2_rand_uniform": (torch.rand(10, 10) - 0.5,),
+        "rank_3_all_ones": (torch.ones(10, 10, 10),),
+        "rank_4_all_zeros": (torch.zeros(1, 10, 10, 10),),
+        "rank_4_randn_pos": (torch.randn(1, 4, 4, 4) + 10,),
+        "rank_4_randn_neg": (torch.randn(1, 4, 4, 4) - 10,),
+    }
+
+    def forward(self, x: torch.Tensor):
+        return torch.neg(x)
+
+
+@common.parametrize("test_data", Neg.test_data)
+def test_neg_tosa_MI(test_data: input_t1):
+    pipeline = TosaPipelineMI[input_t1](Neg(), test_data, Neg.aten_op, Neg.exir_op)
+    pipeline.run()
+
+
+@common.parametrize("test_data", Neg.test_data)
+def test_neg_tosa_BI(test_data: input_t1):
+    pipeline = TosaPipelineBI[input_t1](Neg(), test_data, Neg.aten_op, Neg.exir_op)
+    pipeline.run()
+
+
+@common.parametrize("test_data", Neg.test_data)
+@common.XfailIfNoCorstone300
+def test_neg_u55_BI(test_data: input_t1):
+    pipeline = EthosU55PipelineBI[input_t1](
+        Neg(), test_data, Neg.aten_op, Neg.exir_op, run_on_fvp=True
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Neg.test_data)
+@common.XfailIfNoCorstone320
+def test_neg_u85_BI(test_data: input_t1):
+    pipeline = EthosU85PipelineBI[input_t1](
+        Neg(), test_data, Neg.aten_op, Neg.exir_op, run_on_fvp=True
+    )
+    pipeline.run()