Arm backend: Add decomposition of div.Tensor_mode

backends/arm/_passes/__init__.py

@@ -37,6 +37,7 @@
 from .decompose_cosine_similarity_pass import DecomposeCosineSimilarityPass  # noqa
 from .decompose_cumsum_pass import DecomposeCumsumPass  # noqa
 from .decompose_div_pass import DecomposeDivPass  # noqa
+from .decompose_div_tensor_mode import DecomposeDivTensorModePass  # noqa
 from .decompose_elu_pass import DecomposeEluPass  # noqa
 from .decompose_embedding_pass import DecomposeEmbeddingPass  # noqa  # noqa
 from .decompose_expm1_pass import DecomposeExpm1Pass  # noqa

backends/arm/_passes/arm_pass_manager.py

@@ -42,6 +42,7 @@
     DecomposeCosineSimilarityPass,
     DecomposeCumsumPass,
     DecomposeDivPass,
+    DecomposeDivTensorModePass,
     DecomposeEluPass,
     DecomposeEmbeddingPass,
     DecomposeExpm1Pass,
@@ -211,6 +212,7 @@ def _tosa_FP_pipeline(self, exported_program: ExportedProgram) -> GraphModule:
             DecomposeMeanDimPass(exported_program.graph_module, self.tosa_spec)
         )
         self.add_pass(DecomposeNotEqualPass())
+        self.add_pass(DecomposeDivTensorModePass())
         self.add_pass(DecomposeDivPass())
         self.add_pass(DecomposeSoftmaxPass())
         self.add_pass(DecomposeGeluPass())
@@ -289,6 +291,7 @@ def transform_for_annotation_pipeline(self, graph_module: GraphModule):
         self.add_pass(DecomposeNotEqualPass())
         self.add_pass(DecomposeCosineSimilarityPass())
         self.add_pass(DecomposeGluPass())
+        self.add_pass(DecomposeDivTensorModePass())
         self.add_pass(DecomposeDivPass())
         self.add_pass(DecomposeLeakyReLUPass())
         self.add_pass(DecomposeLinearVectorNormPass())

backends/arm/_passes/decompose_div_tensor_mode.py

@@ -0,0 +1,84 @@
+# 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
+
+import torch
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass
+
+edge_div_mode_ops = (exir_ops.edge.aten.div.Tensor_mode,)
+aten_div_mode_ops = (torch.ops.aten.div.Tensor_mode,)
+
+edge_unary = {
+    "div": exir_ops.edge.aten.div.Tensor,
+    "floor": exir_ops.edge.aten.floor.default,
+    "ceil": exir_ops.edge.aten.ceil.default,
+    "full": exir_ops.edge.aten.full.default,
+    "lt": exir_ops.edge.aten.lt.Tensor,
+    "where": exir_ops.edge.aten.where.self,
+}
+
+aten_unary = {
+    "div": torch.ops.aten.div.Tensor,
+    "floor": torch.ops.aten.floor.default,
+    "ceil": torch.ops.aten.ceil.default,
+    "full": torch.ops.aten.full.default,
+    "lt": torch.ops.aten.lt.Tensor,
+    "where": torch.ops.aten.where.self,
+}
+
+
+def _get_opset(op):
+    if op in edge_div_mode_ops:
+        return edge_unary
+    if op in aten_div_mode_ops:
+        return aten_unary
+    raise RuntimeError(f"div.Tensor_mode not supported for op {op}")
+
+
+class DecomposeDivTensorModePass(ExportPass):
+    """
+    Rewrites aten.div.Tensor_mode into
+
+    rounding_mode=None  -> div(a, b)
+    rounding_mode='floor' -> floor(div(a, b))
+    rounding_mode='trunc' -> where(div(a,b) < 0, ceil(div(a,b)), floor(div(a,b)))
+    """
+
+    def call_operator(self, op, args, kwargs, meta):
+        if op not in (edge_div_mode_ops + aten_div_mode_ops):
+            return super().call_operator(op, args, kwargs, meta)
+
+        opset = _get_opset(op)
+
+        a, b = args[0], args[1]
+        rounding_mode = kwargs.get("rounding_mode", None)
+        if rounding_mode is None and len(args) > 2:
+            rounding_mode = args[2]
+
+        q = super().call_operator(opset["div"], (a, b), {}, meta)
+
+        if rounding_mode is None:
+            return q
+
+        if rounding_mode == "floor":
+            return super().call_operator(opset["floor"], (q,), {}, meta)
+
+        if rounding_mode == "trunc":
+            zero = super().call_operator(
+                opset["full"],
+                args=((1,) * len(meta["val"].size()), 0.0),
+                kwargs={"dtype": torch.float32},
+                meta=meta,
+            )
+            lt0 = self.call_operator(opset["lt"], (q, zero), {}, meta)
+            ceilq = self.call_operator(opset["ceil"], (q,), {}, meta)
+            floorq = self.call_operator(opset["floor"], (q,), {}, meta)
+            return self.call_operator(opset["where"], (lt0, ceilq, floorq), {}, meta)
+
+        raise RuntimeError(
+            f"Unsupported rounding_mode for div.Tensor_mode: {rounding_mode!r}"
+        )

backends/arm/operator_support/tosa_supported_operators.py

@@ -176,6 +176,7 @@ def is_node_supported(
             exir_ops.edge.aten.hardtanh.default,
             exir_ops.edge.aten.hardswish.default,
             exir_ops.edge.aten.div.Tensor,
+            exir_ops.edge.aten.div.Tensor_mode,
             exir_ops.edge.aten.eq.Tensor,
             exir_ops.edge.aten.eq.Scalar,
             exir_ops.edge.aten.erf.default,

backends/arm/test/ops/test_div_tensor_mode.py

@@ -0,0 +1,150 @@
+# 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 Tuple
+
+import pytest
+import torch
+
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.test_pipeline import (
+    EthosU55PipelineINT,
+    EthosU85PipelineINT,
+    TosaPipelineFP,
+    TosaPipelineINT,
+    VgfPipeline,
+)
+
+input_tt = Tuple[torch.Tensor, torch.Tensor]
+
+
+def make_float_div_inputs(B: int = 4, T: int = 64) -> input_tt:
+    x = torch.randn(B, T)
+    # guard against zero in denominator
+    y = torch.randn(B, T).abs() + 1e-3
+    return x, y
+
+
+class DivTensorModeFloat(torch.nn.Module):
+    """
+    torch.div(x, y, rounding_mode=mode) with
+    mode from {None, "floor", "trunc"}.
+    """
+
+    aten_ops = ["aten.div.Tensor_mode"]
+    aten_ops_int = ["aten.mul.Tensor", "aten.reciprocal.default"]
+
+    def __init__(self, mode=None):
+        super().__init__()
+        assert mode in (None, "floor", "trunc")
+        self.mode = mode
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        return torch.div(x, y, rounding_mode=self.mode)
+
+
+@pytest.mark.parametrize("mode", [None, "floor", "trunc"])
+def test_div_tensor_mode_tosa_FP(mode):
+
+    model = DivTensorModeFloat(mode)
+    inputs = make_float_div_inputs()
+
+    pipeline = TosaPipelineFP[input_tt](
+        model,
+        inputs,
+        aten_op=model.aten_ops,
+        exir_op=[],
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.pop_stage("check_count.exir")
+    pipeline.run()
+
+
+@pytest.mark.parametrize("mode", [None, "floor", "trunc"])
+def test_div_tensor_mode_tosa_INT(mode):
+
+    model = DivTensorModeFloat(mode)
+    inputs = make_float_div_inputs()
+
+    pipeline = TosaPipelineINT[input_tt](
+        model,
+        inputs,
+        aten_op=model.aten_ops_int,
+        exir_op=[],
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.pop_stage("check_count.exir")
+    pipeline.run()
+
+
+@pytest.mark.parametrize("mode", [None, "floor"])
+def test_div_tensor_mode_u55_INT(mode):
+
+    model = DivTensorModeFloat(mode)
+    inputs = make_float_div_inputs()
+
+    pipeline = EthosU55PipelineINT[input_tt](
+        model,
+        inputs,
+        aten_ops=model.aten_ops_int,
+        exir_ops=[],
+        use_to_edge_transform_and_lower=True,
+        run_on_fvp=True,
+    )
+    pipeline.run()
+
+
+@common.XfailIfNoCorstone320
+@pytest.mark.parametrize("mode", [None, "floor", "trunc"])
+def test_div_tensor_mode_u85_INT(mode):
+
+    model = DivTensorModeFloat(mode)
+    inputs = make_float_div_inputs()
+
+    pipeline = EthosU85PipelineINT[input_tt](
+        model,
+        inputs,
+        aten_ops=model.aten_ops_int,
+        exir_ops=[],
+        use_to_edge_transform_and_lower=True,
+        run_on_fvp=True,
+    )
+    pipeline.run()
+
+
+@common.SkipIfNoModelConverter
+@pytest.mark.parametrize("mode", [None, "floor", "trunc"])
+def test_div_tensor_mode_vgf_INT(mode):
+
+    model = DivTensorModeFloat(mode)
+    inputs = make_float_div_inputs()
+
+    pipeline = VgfPipeline[input_tt](
+        model,
+        inputs,
+        aten_op=model.aten_ops_int,
+        exir_op=[],
+        tosa_version="TOSA-1.0+INT",
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.pop_stage("check_count.exir")
+    pipeline.run()
+
+
+@common.SkipIfNoModelConverter
+@pytest.mark.parametrize("mode", [None, "floor", "trunc"])
+def test_div_tensor_mode_vgf_FP(mode):
+
+    model = DivTensorModeFloat(mode)
+    inputs = make_float_div_inputs()
+
+    pipeline = VgfPipeline[input_tt](
+        model,
+        inputs,
+        aten_op=model.aten_ops,
+        exir_op=[],
+        tosa_version="TOSA-1.0+FP",
+        use_to_edge_transform_and_lower=True,
+    )
+    pipeline.run()