Arm Backend: Add New DecomposeSilu pass to arm_pass_manager (#9448)

* Adds DecomposeSilu pass
* Adds Tests for DecomposeSilu

Signed-off-by: Ryan O'Shea <[email protected]>

Author: ArmRyan

backends/arm/_passes/__init__.py

@@ -26,6 +26,7 @@
 from .decompose_linear_pass import DecomposeLinearPass  # noqa
 from .decompose_meandim_pass import DecomposeMeanDimPass  # noqa
 from .decompose_select import DecomposeSelectPass  # noqa
+from .decompose_silu_pass import DecomposeSiluPass  # noqa
 from .decompose_softmax_pass import DecomposeSoftmaxPass  # noqa
 from .decompose_softmax_unstable_pass import DecomposeSoftmaxUnstablePass  # noqa
 from .decompose_sqrt_pass import DecomposeSqrtPass  # noqa

backends/arm/_passes/arm_pass_manager.py

@@ -31,6 +31,7 @@
     DecomposeLinearPass,
     DecomposeMeanDimPass,
     DecomposeSelectPass,
+    DecomposeSiluPass,
     DecomposeSoftmaxPass,
     DecomposeSoftmaxUnstablePass,
     DecomposeSqrtPass,
@@ -196,6 +197,7 @@ def transform_for_annotation_pipeline(self, graph_module: GraphModule):
         self.add_pass(DecomposeDivPass())
         self.add_pass(DecomposeLeakyReLUPass())
         self.add_pass(DecomposeSqrtPass())
+        self.add_pass(DecomposeSiluPass())
 
         if isinstance(self.tosa_spec, Tosa_0_80) and self.tosa_spec.is_U55_subset:
             # Numerically stable softmax uses amax which is not supported on Ethos-U55

backends/arm/_passes/decompose_silu_pass.py

@@ -0,0 +1,34 @@
+# 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.pass_base import ExportPass
+
+aten_silu_ops = (torch.ops.aten.silu.default, torch.ops.aten.silu_.default)
+
+
+class DecomposeSiluPass(ExportPass):
+    """
+    This pass decomposes silu into a mul and a sigmoid node.
+
+    Example:
+        y = silu(a)
+    Becomes:
+        x = sigmoid(a)
+        y = mul(a,x)
+    """
+
+    def call_operator(self, op, args, kwargs, meta):
+        if op not in (aten_silu_ops):
+            return super().call_operator(op, args, kwargs, meta)
+        sigmoid_op = torch.ops.aten.sigmoid.default
+        mul_op = torch.ops.aten.mul.Tensor
+
+        original = args[0]
+        sigmoid = super().call_operator(sigmoid_op, (original,), {}, meta)
+
+        return super().call_operator(mul_op, (original, sigmoid), {}, meta)

backends/arm/quantizer/arm_quantizer.py

@@ -286,10 +286,10 @@ def _annotate_all_static_patterns(
         quantization_config: Optional[QuantizationConfig],
         filter_fn: Optional[Callable[[Node], bool]] = None,
     ) -> GraphModule:
-        """Loops over all STATIC_OPS and runs the corresponding registred annotator.
+        """Loops over all STATIC_OPS and runs the corresponding registered annotator.
         Args:
             model: The model to annotate statically.
-            quantization_config: Specifices the QuantizationSpecs for the model's
+            quantization_config: Specifies the QuantizationSpecs for the model's
                 input activations, output activations, weights and biases.
             filter_fn: An optional filter function that takes a node and returns whether the node should be annotated.
         Returns:

backends/arm/test/ops/test_silu.py

@@ -0,0 +1,113 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+# 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 Optional, Tuple
+
+import torch
+from executorch.backends.arm.test import common
+from executorch.backends.arm.test.tester.test_pipeline import (
+    EthosU55PipelineBI,
+    EthosU85PipelineBI,
+    TosaPipelineBI,
+    TosaPipelineMI,
+)
+
+
+input_t = Tuple[torch.Tensor]
+
+
+class Silu(torch.nn.Module):
+    def forward(
+        self,
+        _input: torch.Tensor,
+        _inplace: Optional[bool] = False,
+    ):
+        return torch.nn.SiLU(inplace=_inplace)(_input)
+
+    test_data: list[input_t] = {
+        "op_silu_rank1_ones": (torch.ones(5),),
+        "op_silu_rank1_negative_ones": (torch.ones(5) * (-1),),
+        "op_silu_rank1_rand": (torch.rand(5) * 5,),
+        "op_silu_rank4_ones": (torch.ones(1, 10, 25, 20),),
+        "op_silu_rank4_negative_ones": ((-1) * torch.ones(1, 10, 25, 20),),
+        "op_silu_rank4_large_rand": (200 * torch.rand(1, 10, 25, 20),),
+        "op_silu_rank4_negative_large_rand": ((-200) * torch.rand(1, 10, 25, 20),),
+        "op_silu_rank4_large_randn": (200 * torch.randn(1, 10, 25, 20) + 1,),
+    }
+
+    aten_op_MI = "torch.ops.aten.silu.default"
+    aten_op_inplace_MI = "torch.ops.aten.silu_.default"
+    aten_op_BI = ["torch.ops.aten.sigmoid.default", "torch.ops.aten.mul.Tensor"]
+
+
+@common.parametrize("test_data", Silu.test_data)
+def test_silu_tosa_MI(test_data: input_t):
+    silu_data = (test_data[0], False)
+    pipeline = TosaPipelineMI[input_t](Silu(), silu_data, Silu.aten_op_MI)
+    pipeline.run()
+
+
+@common.parametrize("test_data", Silu.test_data)
+def test_silu_tosa_MI_inplace(test_data: input_t):
+    silu_data = (test_data[0], True)
+    pipeline = TosaPipelineMI[input_t](Silu(), silu_data, Silu.aten_op_inplace_MI)
+    pipeline.run()
+
+
+@common.parametrize("test_data", Silu.test_data)
+def test_silu_tosa_BI(test_data: input_t):
+    silu_data = (test_data[0], False)
+    pipeline = TosaPipelineBI[input_t](Silu(), silu_data, Silu.aten_op_BI)
+    pipeline.run()
+
+
+@common.parametrize("test_data", Silu.test_data)
+def test_silu_tosa_BI_inplace(test_data: input_t):
+    silu_data = (test_data[0], True)
+    pipeline = TosaPipelineBI[input_t](Silu(), silu_data, Silu.aten_op_BI)
+    pipeline.run()
+
+
+@common.parametrize("test_data", Silu.test_data)
+@common.XfailIfNoCorstone300
+def test_silu_u55_BI(test_data: input_t):
+    silu_data = (test_data[0], False)
+    pipeline = EthosU55PipelineBI[input_t](
+        Silu(), silu_data, Silu.aten_op_BI, run_on_fvp=True
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Silu.test_data)
+@common.XfailIfNoCorstone300
+def test_silu_u55_BI_inplace(test_data: input_t):
+    silu_data = (test_data[0], True)
+    pipeline = EthosU55PipelineBI[input_t](
+        Silu(), silu_data, Silu.aten_op_BI, run_on_fvp=True
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Silu.test_data)
+@common.XfailIfNoCorstone320
+def test_silu_u85_BI(test_data: input_t):
+    silu_data = (test_data[0], False)
+    pipeline = EthosU85PipelineBI[input_t](
+        Silu(), silu_data, Silu.aten_op_BI, run_on_fvp=True
+    )
+    pipeline.run()
+
+
+@common.parametrize("test_data", Silu.test_data)
+@common.XfailIfNoCorstone320
+def test_silu_u85_BI_inplace(test_data: input_t):
+    silu_data = (test_data[0], True)
+    pipeline = EthosU85PipelineBI[input_t](
+        Silu(), silu_data, Silu.aten_op_BI, run_on_fvp=True
+    )
+    pipeline.run()