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Arm Backend: Add support for expm1.default (#13274)
Decompose expm1 into other operators or use the Taylor series expansion when input values are close to 0. Signed-off-by: Agrima Khare <[email protected]>
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‎backends/arm/_passes/__init__.py‎

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from .decompose_cosine_similarity_pass import DecomposeCosineSimilarityPass # noqa
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from .decompose_div_pass import DecomposeDivPass # noqa
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from .decompose_embedding_pass import DecomposeEmbeddingPass # noqa # noqa
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from .decompose_expm1_pass import DecomposeExpm1Pass # noqa
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from .decompose_gelu_pass import DecomposeGeluPass # noqa
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from .decompose_glu_pass import DecomposeGluPass # noqa
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from .decompose_grouped_conv import DecomposeGroupedConv # noqa

‎backends/arm/_passes/arm_pass_manager.py‎

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DecomposeCosineSimilarityPass,
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DecomposeDivPass,
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DecomposeEmbeddingPass,
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DecomposeExpm1Pass,
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DecomposeGeluPass,
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DecomposeGluPass,
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DecomposeGroupedConv,
@@ -164,6 +165,7 @@ def _tosa_INT_pipeline(self, exported_program: ExportedProgram) -> GraphModule:
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return self._transform(exported_program.graph_module)
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def _tosa_FP_pipeline(self, exported_program: ExportedProgram) -> GraphModule:
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self.add_pass(DecomposeExpm1Pass())
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self.add_pass(DecomposeMaskedFill())
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self.add_pass(DecomposeRoundPass())
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self.add_pass(DecomposeAcoshPass())

‎backends/arm/_passes/decompose_expm1_pass.py‎

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# Copyright 2025 Arm Limited and/or its affiliates.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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from executorch.backends.arm._passes import ArmPass
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from executorch.exir.dialects._ops import ops as exir_ops
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edge_expm1_ops = (exir_ops.edge.aten.expm1.default,) # MI case
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def _get_expm1_decomposition(op) -> tuple:
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"""
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Returns the decomposition of the given aten.expm1 operation into
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its equivalent TOSA-supported operations
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This handles both edge dialect ops and core PyTorch ops. The decomposition strategy
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is:
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expm1(x) → where(and(ge(x, -0.35), le(x, 0.35)), {taylor_series_expansion}, (exp(x)-1))
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where {taylor_series_expansion} = x + (x^2/2) + (x^3/6) + (x^4/24)
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Returns:
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A tuple (op_pow, op_div, op_add, op_exp, op_sub, op_ge, op_where, op_le, op_and)
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corresponding to the appropriate operator overloads for the input op.
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Raises:
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RuntimeError: If the provided operator is not a supported elu variant.
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"""
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if op in edge_expm1_ops:
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return (
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exir_ops.edge.aten.pow.Tensor_Scalar,
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exir_ops.edge.aten.div.Scalar,
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exir_ops.edge.aten.add.Tensor,
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exir_ops.edge.aten.exp.default,
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exir_ops.edge.aten.sub.Scalar,
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exir_ops.edge.aten.ge.Scalar,
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exir_ops.edge.aten.where.self,
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exir_ops.edge.aten.le.Scalar,
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exir_ops.edge.aten.logical_and.default,
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)
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raise RuntimeError(f"Can't get expm1 decomposition for op {op}")
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class DecomposeExpm1Pass(ArmPass):
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"""
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A transformation pass that decomposes unsupported 'aten.expm1' operations
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into a combination of supported TOSA-equivalent operations.
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Since TOSA does not provide a native expm1 operator, this pass rewrites:
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expm1(x) → where(and(ge(x, -0.35), le(x, 0.35)), {taylor_series_expansion}, (exp(x)-1))
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where {taylor_series_expansion} = x + (x^2/2) + (x^3/6) + (x^4/24)
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Supported input ops:
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- exir_ops.edge.aten.expm1.default(x)
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These are replaced with:
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- exir_ops.edge.aten.pow.Tensor_Scalar,
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- exir_ops.edge.aten.div.Scalar,
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- exir_ops.edge.aten.add.Tensor,
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- exir_ops.edge.aten.exp.default,
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- exir_ops.edge.aten.sub.Scalar,
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- exir_ops.edge.aten.ge.Scalar,
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- exir_ops.edge.aten.where.self,
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- exir_ops.edge.aten.le.Scalar,
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- exir_ops.edge.aten.logical_and.default
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"""
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def call_operator(self, op, args, kwargs, meta):
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if op not in edge_expm1_ops:
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return super().call_operator(op, args, kwargs, meta, updated=False)
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(
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op_pow,
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op_div,
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op_add,
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op_exp,
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op_sub,
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op_ge,
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op_where,
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op_le,
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op_and,
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) = _get_expm1_decomposition(op)
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input = args[0]
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cutlo = -0.35
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cuthi = 0.35
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taylor_term_2_numerator = super().call_operator(
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op_pow, (input, 2), {}, meta, updated=False
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)
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taylor_term_3_numerator = super().call_operator(
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op_pow, (input, 3), {}, meta, updated=False
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)
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taylor_term_4_numerator = super().call_operator(
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op_pow, (input, 4), {}, meta, updated=False
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)
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taylor_term_2 = super().call_operator(
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op_div, (taylor_term_2_numerator, 2), {}, meta, updated=False
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)
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taylor_term_3 = super().call_operator(
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op_div, (taylor_term_3_numerator, 6), {}, meta, updated=False
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)
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taylor_term_4 = super().call_operator(
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op_div, (taylor_term_4_numerator, 24), {}, meta, updated=False
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)
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add_terms_1_2 = super().call_operator(
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op_add, (input, taylor_term_2), {}, meta, updated=False
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)
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add_term_3 = super().call_operator(
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op_add, (add_terms_1_2, taylor_term_3), {}, meta, updated=False
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)
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taylor_expansion = super().call_operator(
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op_add, (add_term_3, taylor_term_4), {}, meta, updated=False
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)
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decomp_exp = super().call_operator(op_exp, (input,), {}, meta, updated=False)
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decomp_sub = super().call_operator(
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op_sub, (decomp_exp, 1.0), {}, meta, updated=False
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)
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ge = super().call_operator(op_ge, (input, cutlo), {}, meta, updated=False)
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le = super().call_operator(op_le, (input, cuthi), {}, meta, updated=False)
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cond_and = super().call_operator(op_and, (ge, le), {}, meta, updated=False)
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where = super().call_operator(
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op_where, (cond_and, taylor_expansion, decomp_sub), {}, meta, updated=True
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)
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return where

‎backends/arm/_passes/insert_table_ops.py‎

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exir_ops.edge.aten.ceil.default: torch.ceil,
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exir_ops.edge.aten.erf.default: torch.erf,
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exir_ops.edge.aten.exp.default: torch.exp,
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exir_ops.edge.aten.expm1.default: torch.expm1,
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exir_ops.edge.aten.floor.default: torch.floor,
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exir_ops.edge.aten.log.default: torch.log,
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exir_ops.edge.aten.reciprocal.default: torch.reciprocal,

‎backends/arm/operator_support/tosa_supported_operators.py‎

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exir_ops.edge.aten.eq.Scalar,
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exir_ops.edge.aten.erf.default,
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exir_ops.edge.aten.exp.default,
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exir_ops.edge.aten.expm1.default,
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exir_ops.edge.aten.log.default,
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exir_ops.edge.aten.linear.default,
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exir_ops.edge.aten.split_with_sizes_copy.default,

‎backends/arm/quantizer/quantization_annotator.py‎

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torch.ops.aten.ceil.default,
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torch.ops.aten.erf.default,
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torch.ops.aten.exp.default,
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torch.ops.aten.expm1.default,
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torch.ops.aten.floor.default,
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torch.ops.aten.log.default,
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torch.ops.aten.reciprocal.default,

‎backends/arm/scripts/parse_test_names.py‎

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CUSTOM_EDGE_OPS = [
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"linspace.default",
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"eye.default",
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"expm1.default",
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"vector_norm.default",
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"hardsigmoid.default",
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"hardswish.default",

‎backends/arm/test/ops/test_expm1.py‎

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# Copyright 2025 Arm Limited and/or its affiliates.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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from typing import Tuple
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import torch
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from executorch.backends.arm.test import common
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from executorch.backends.arm.test.tester.test_pipeline import (
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EthosU55PipelineINT,
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EthosU85PipelineINT,
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TosaPipelineFP,
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TosaPipelineINT,
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VgfPipeline,
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)
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aten_op = "torch.ops.aten.expm1.default"
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exir_op = "executorch_exir_dialects_edge__ops_aten_expm1_default"
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input_t1 = Tuple[torch.Tensor]
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test_data_suite = {
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"zeroes": torch.zeros(1, 10, 10, 10),
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"ones": torch.ones(10, 2, 3),
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"rand": torch.rand(10, 10) - 0.5,
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"near_zero": torch.randn(100) * 0.01,
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"taylor_small": torch.empty(5).uniform_(
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-0.35, 0.35
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), # test cases for taylor series expansion
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"randn_large_pos": torch.randn(10) + 10,
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"randn_large_neg": torch.randn(10) - 10,
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"ramp": torch.arange(-16, 16, 0.2),
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}
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class Expm1(torch.nn.Module):
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def forward(self, x: torch.Tensor):
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return torch.expm1(x)
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@common.parametrize("test_data", test_data_suite)
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def test_expm1_tosa_FP(test_data: Tuple):
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pipeline = TosaPipelineFP[input_t1](
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Expm1(),
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(test_data,),
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aten_op=aten_op,
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exir_op=exir_op,
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)
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pipeline.run()
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@common.parametrize("test_data", test_data_suite)
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def test_expm1_tosa_INT(test_data: Tuple):
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pipeline = TosaPipelineINT[input_t1](
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Expm1(),
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(test_data,),
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aten_op=aten_op,
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exir_op=exir_op,
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)
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pipeline.run()
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@common.XfailIfNoCorstone300
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@common.parametrize("test_data", test_data_suite)
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def test_expm1_u55_INT(test_data: Tuple):
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pipeline = EthosU55PipelineINT[input_t1](
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Expm1(),
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(test_data,),
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aten_ops=aten_op,
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exir_ops=exir_op,
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)
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pipeline.run()
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@common.XfailIfNoCorstone320
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@common.parametrize("test_data", test_data_suite)
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def test_expm1_u85_INT(test_data: Tuple):
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pipeline = EthosU85PipelineINT[input_t1](
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Expm1(),
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(test_data,),
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aten_ops=aten_op,
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exir_ops=exir_op,
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)
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pipeline.run()
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@common.parametrize("test_data", test_data_suite)
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@common.SkipIfNoModelConverter
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def test_expm1_vgf_FP(test_data: Tuple):
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pipeline = VgfPipeline[input_t1](
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Expm1(),
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(test_data,),
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aten_op,
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exir_op,
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tosa_version="TOSA-1.0+FP",
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)
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pipeline.run()
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@common.parametrize("test_data", test_data_suite)
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@common.SkipIfNoModelConverter
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def test_expm1_vgf_INT(test_data: Tuple):
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pipeline = VgfPipeline[input_t1](
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Expm1(),
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(test_data,),
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aten_op,
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exir_op,
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tosa_version="TOSA-1.0+INT",
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)
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pipeline.run()

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