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AdrianLundellAdrianLundell
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Arm backend: Add constant ops unittests.
Adds unittests for eye, ones and zeros. These ops are supported by the ComputeConstantOpsAOT pass, add unittests for coverage. This exposes an edge case bug similar as for the clone, where a partion with one single constant op results in an empty network. Signed-off-by: Adrian Lundell <[email protected]> Change-Id: Ieabf8bc7b28ad887f0238c1e889332dc663cb7d6
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‎backends/arm/test/ops/test_eye.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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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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EthosU55PipelineBI,
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EthosU85PipelineBI,
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OpNotSupportedPipeline,
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TosaPipelineBI,
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TosaPipelineMI,
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)
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input_t = tuple[torch.Tensor]
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test_data_t = tuple[int, torch.dtype]
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class EyeAdd(torch.nn.Module):
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aten_op: str = "torch.ops.aten.eye.default"
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def __init__(self, n: int, dtype: torch.dtype):
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super().__init__()
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self.args = (n,)
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self.dtype = dtype
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return torch.eye(*self.args, dtype=self.dtype) + x
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test_data: dict[str, test_data_t] = {
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"10x3x3": (lambda: (torch.randn(10, 3, 3),), (3, torch.float32)),
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"10x1": (lambda: (torch.randn(10, 1),), (10, torch.float32)),
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"int32_int32": (
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lambda: (torch.randint(0, 10, [10], dtype=torch.int32),),
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(10, torch.int32),
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),
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}
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test_data_not_delegated: dict[str, test_data_t] = {
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"fp32_int64": (lambda: (torch.randn(10),), (10, torch.int64)),
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"fp32_int32": (lambda: (torch.randn(10),), (10, torch.int32)),
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"int32_int64": (
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lambda: (torch.randint(0, 10, [10], dtype=torch.int32),),
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(10, torch.int64),
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),
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}
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@common.parametrize("test_data", EyeAdd.test_data)
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def test_eye_tosa_MI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = TosaPipelineMI[input_t](
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EyeAdd(*init_data),
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input_data(),
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EyeAdd.aten_op,
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)
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pipeline.run()
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@common.parametrize("test_data", EyeAdd.test_data)
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def test_eye_tosa_BI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = TosaPipelineBI[input_t](
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EyeAdd(*init_data),
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input_data(),
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EyeAdd.aten_op,
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)
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pipeline.pop_stage("check.quant_nodes")
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pipeline.run()
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@common.parametrize("test_data", EyeAdd.test_data)
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def test_eye_u55_BI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = EthosU55PipelineBI[input_t](
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EyeAdd(*init_data),
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input_data(),
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EyeAdd.aten_op,
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use_to_edge_transform_and_lower=True,
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)
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pipeline.pop_stage("check.quant_nodes")
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pipeline.run()
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@common.parametrize("test_data", EyeAdd.test_data)
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def test_eye_u85_BI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = EthosU85PipelineBI[input_t](
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EyeAdd(*init_data),
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input_data(),
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EyeAdd.aten_op,
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use_to_edge_transform_and_lower=True,
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).dump_artifact("to_edge_transform_and_lower")
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pipeline.pop_stage("check.quant_nodes")
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pipeline.run()
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@common.parametrize(
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"test_data",
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EyeAdd.test_data_not_delegated,
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xfails={
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"fp32_int32": "MLETORCG-716: Do not delegate empty networks to vela",
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"fp32_int64": "MLETORCG-716: Do not delegate empty networks to vela",
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"int32_int64": "MLETORCG-716: Do not delegate empty networks to vela",
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},
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)
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def test_eye_tosa_BI_not_delegated(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = OpNotSupportedPipeline[input_t](
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EyeAdd(*init_data), input_data(), non_delegated_ops={}, quantize=True
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)
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pipeline.run()

‎backends/arm/test/ops/test_ones.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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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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EthosU55PipelineBI,
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EthosU85PipelineBI,
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OpNotSupportedPipeline,
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TosaPipelineBI,
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TosaPipelineMI,
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)
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input_t = tuple[torch.Tensor]
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test_data_t = tuple[int, torch.dtype]
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class OnesAdd(torch.nn.Module):
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aten_op: str = "torch.ops.aten.ones.default"
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def __init__(self, n: int, dtype: torch.dtype):
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super().__init__()
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self.args = (n,)
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self.dtype = dtype
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return torch.ones(*self.args, dtype=self.dtype) + x
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test_data: dict[str, test_data_t] = {
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"10x3x3": (lambda: (torch.randn(10, 3, 3),), (3, torch.float32)),
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"10x1": (lambda: (torch.randn(10, 1),), (10, torch.float32)),
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"int32_int32": (
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lambda: (torch.randint(0, 10, [10], dtype=torch.int32),),
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(10, torch.int32),
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),
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}
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test_data_not_delegated: dict[str, test_data_t] = {
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"fp32_int64": (lambda: (torch.randn(10),), (10, torch.int64)),
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"fp32_int32": (lambda: (torch.randn(10),), (10, torch.int32)),
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"int32_int64": (
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lambda: (torch.randint(0, 10, [10], dtype=torch.int32),),
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(10, torch.int64),
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),
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}
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@common.parametrize("test_data", OnesAdd.test_data)
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def test_ones_tosa_MI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = TosaPipelineMI[input_t](
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OnesAdd(*init_data),
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input_data(),
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OnesAdd.aten_op,
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)
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pipeline.run()
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@common.parametrize("test_data", OnesAdd.test_data)
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def test_ones_tosa_BI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = TosaPipelineBI[input_t](
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OnesAdd(*init_data),
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input_data(),
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OnesAdd.aten_op,
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)
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pipeline.pop_stage("check.quant_nodes")
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pipeline.run()
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@common.parametrize("test_data", OnesAdd.test_data)
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def test_ones_u55_BI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = EthosU55PipelineBI[input_t](
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OnesAdd(*init_data),
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input_data(),
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OnesAdd.aten_op,
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use_to_edge_transform_and_lower=True,
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)
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pipeline.pop_stage("check.quant_nodes")
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pipeline.run()
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@common.parametrize("test_data", OnesAdd.test_data)
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def test_ones_u85_BI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = EthosU85PipelineBI[input_t](
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OnesAdd(*init_data),
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input_data(),
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OnesAdd.aten_op,
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use_to_edge_transform_and_lower=True,
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).dump_artifact("to_edge_transform_and_lower")
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pipeline.pop_stage("check.quant_nodes")
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pipeline.run()
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@common.parametrize(
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"test_data",
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OnesAdd.test_data_not_delegated,
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xfails={
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"fp32_int32": "MLETORCG-716: Do not delegate empty networks to vela",
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"fp32_int64": "MLETORCG-716: Do not delegate empty networks to vela",
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"int32_int64": "MLETORCG-716: Do not delegate empty networks to vela",
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},
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)
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def test_ones_tosa_BI_not_delegated(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = OpNotSupportedPipeline[input_t](
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OnesAdd(*init_data), input_data(), non_delegated_ops={}, quantize=True
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)
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pipeline.run()

‎backends/arm/test/ops/test_zeros.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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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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EthosU55PipelineBI,
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EthosU85PipelineBI,
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OpNotSupportedPipeline,
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TosaPipelineBI,
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TosaPipelineMI,
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)
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input_t = tuple[torch.Tensor]
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test_data_t = tuple[int, torch.dtype]
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class ZerosAdd(torch.nn.Module):
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aten_op: str = "torch.ops.aten.zeros.default"
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def __init__(self, n: int, dtype: torch.dtype):
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super().__init__()
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self.args = (n,)
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self.dtype = dtype
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return torch.zeros(*self.args, dtype=self.dtype) + x
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test_data: dict[str, test_data_t] = {
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"10x3x3": (lambda: (torch.randn(10, 3, 3),), (3, torch.float32)),
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"10x1": (lambda: (torch.randn(10, 1),), (10, torch.float32)),
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"int32_int32": (
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lambda: (torch.randint(0, 10, [10], dtype=torch.int32),),
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(10, torch.int32),
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),
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}
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test_data_not_delegated: dict[str, test_data_t] = {
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"fp32_int64": (lambda: (torch.randn(10),), (10, torch.int64)),
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"fp32_int32": (lambda: (torch.randn(10),), (10, torch.int32)),
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"int32_int64": (
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lambda: (torch.randint(0, 10, [10], dtype=torch.int32),),
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(10, torch.int64),
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),
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}
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@common.parametrize("test_data", ZerosAdd.test_data)
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def test_zeros_tosa_MI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = TosaPipelineMI[input_t](
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ZerosAdd(*init_data),
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input_data(),
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ZerosAdd.aten_op,
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)
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pipeline.run()
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@common.parametrize("test_data", ZerosAdd.test_data)
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def test_zeros_tosa_BI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = TosaPipelineBI[input_t](
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ZerosAdd(*init_data),
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input_data(),
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ZerosAdd.aten_op,
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)
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pipeline.pop_stage("check.quant_nodes")
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pipeline.run()
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@common.parametrize("test_data", ZerosAdd.test_data)
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def test_zeros_u55_BI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = EthosU55PipelineBI[input_t](
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ZerosAdd(*init_data),
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input_data(),
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ZerosAdd.aten_op,
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use_to_edge_transform_and_lower=True,
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)
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pipeline.pop_stage("check.quant_nodes")
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pipeline.run()
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@common.parametrize("test_data", ZerosAdd.test_data)
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def test_zeros_u85_BI(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = EthosU85PipelineBI[input_t](
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ZerosAdd(*init_data),
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input_data(),
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ZerosAdd.aten_op,
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use_to_edge_transform_and_lower=True,
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).dump_artifact("to_edge_transform_and_lower")
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pipeline.pop_stage("check.quant_nodes")
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pipeline.run()
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@common.parametrize(
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"test_data",
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ZerosAdd.test_data_not_delegated,
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xfails={
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"fp32_int32": "MLETORCG-716: Do not delegate empty networks to vela",
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"fp32_int64": "MLETORCG-716: Do not delegate empty networks to vela",
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"int32_int64": "MLETORCG-716: Do not delegate empty networks to vela",
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},
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)
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def test_zeros_tosa_BI_not_delegated(test_data: test_data_t):
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input_data, init_data = test_data
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pipeline = OpNotSupportedPipeline[input_t](
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ZerosAdd(*init_data), input_data(), non_delegated_ops={}, quantize=True
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)
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pipeline.run()

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