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SaoirseARMErik-Lundell
SaoirseARM
authored and
Erik-Lundell
committed
Add missing unit tests for operators
* hardtanh * permute Change-Id: Ia1802bdc37d365af382835b3c14174d841892927
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‎backends/arm/test/ops/test_hardtanh.py

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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# Copyright 2024 Arm Limited and/or its affiliates.
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# All rights reserved.
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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 unittest
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from typing import Tuple
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import torch
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from executorch.backends.arm.quantizer.arm_quantizer import (
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ArmQuantizer,
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get_symmetric_quantization_config,
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)
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from executorch.backends.arm.test import common
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from executorch.backends.arm.test.tester.arm_tester import ArmTester
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from executorch.backends.xnnpack.test.tester.tester import Quantize
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from parameterized import parameterized
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test_data_suite = [
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# (test_name, test_data)
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("zeros", torch.zeros(1, 10, 10, 10)),
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("ones", torch.ones(10, 10, 10)),
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("rand", torch.rand(10, 10) - 0.5),
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("randn_pos", torch.randn(10) + 10),
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("randn_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 TestHardTanh(unittest.TestCase):
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"""Tests HardTanh Operator."""
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class HardTanh(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.hardTanh = torch.nn.Hardtanh()
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def forward(self, x):
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return self.hardTanh(x)
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def _test_hardtanh_tosa_MI_pipeline(
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self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
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):
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(
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ArmTester(
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module,
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example_inputs=test_data,
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compile_spec=common.get_tosa_compile_spec(),
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)
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.export()
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.check(["torch.ops.aten.hardtanh.default"])
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.check_not(["torch.ops.quantized_decomposed"])
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.to_edge()
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.partition()
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.check_not(["executorch_exir_dialects_edge__ops_aten_hardtanh_default"])
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.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
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.to_executorch()
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.run_method_and_compare_outputs(inputs=test_data)
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)
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def _test_hardtanh_tosa_BI_pipeline(
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self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
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):
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quantizer = ArmQuantizer().set_io(get_symmetric_quantization_config())
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(
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ArmTester(
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module,
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example_inputs=test_data,
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compile_spec=common.get_tosa_compile_spec(),
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)
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.quantize(Quantize(quantizer, get_symmetric_quantization_config()))
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.export()
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.check_count({"torch.ops.aten.hardtanh.default": 1})
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.check(["torch.ops.quantized_decomposed"])
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.to_edge()
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.partition()
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.check_not(["executorch_exir_dialects_edge__ops_aten_hardtanh_default"])
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.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
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.to_executorch()
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.run_method_and_compare_outputs(inputs=test_data)
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)
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def _test_hardtanh_tosa_u55_BI_pipeline(
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self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
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):
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quantizer = ArmQuantizer().set_io(get_symmetric_quantization_config())
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(
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ArmTester(
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module,
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example_inputs=test_data,
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compile_spec=common.get_u55_compile_spec(),
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)
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.quantize(Quantize(quantizer, get_symmetric_quantization_config()))
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.export()
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.check_count({"torch.ops.aten.hardtanh.default": 1})
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.check(["torch.ops.quantized_decomposed"])
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.to_edge()
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.partition()
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.check_not(["executorch_exir_dialects_edge__ops_aten_hardtanh_default"])
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.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
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.to_executorch()
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)
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@parameterized.expand(test_data_suite)
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def test_hardtanh_tosa_MI(
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self,
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test_name: str,
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test_data: torch.Tensor,
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):
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self._test_hardtanh_tosa_MI_pipeline(self.HardTanh(), (test_data,))
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@parameterized.expand(test_data_suite)
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def test_hardtanh_tosa_BI(self, test_name: str, test_data: torch.Tensor):
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self._test_hardtanh_tosa_BI_pipeline(self.HardTanh(), (test_data,))
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@parameterized.expand(test_data_suite)
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def test_hardtanh_tosa_u55_BI(self, test_name: str, test_data: torch.Tensor):
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self._test_hardtanh_tosa_u55_BI_pipeline(self.HardTanh(), (test_data,))

‎backends/arm/test/ops/test_permute.py

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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# Copyright 2024 Arm Limited and/or its affiliates.
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# All rights reserved.
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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 unittest
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from typing import Tuple
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import torch
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from executorch.backends.arm.quantizer.arm_quantizer import (
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ArmQuantizer,
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get_symmetric_quantization_config,
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)
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from executorch.backends.arm.test import common
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from executorch.backends.arm.test.tester.arm_tester import ArmTester
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from executorch.backends.xnnpack.test.tester.tester import Quantize
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from parameterized import parameterized
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from torchvision.ops import Permute
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test_data_suite = [
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# (test_name,test_data,dims)
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("zeros", torch.zeros(10, 10, 10, 10), [1, 0, 3, 2]),
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("ones", torch.ones(10, 10, 10, 10), [3, 1, 0, 2]),
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("rand", torch.rand(10, 10, 10, 10) - 0.5, [0, 2, 3, 1]),
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("randn_pos", torch.randn(10, 10, 10) + 10, [2, 0, 1]),
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("randn_neg", torch.randn(10, 10, 10) - 10, [1, 2, 0]),
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("ramp", torch.arange(-16, 16, 0.2), [0]),
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]
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class TestPermute(unittest.TestCase):
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"""Tests Permute Operator."""
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class Permute(torch.nn.Module):
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def __init__(self, dims: list[int]):
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super().__init__()
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self.permute = Permute(dims=dims)
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def forward(self, x):
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return self.permute(x)
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def _test_permute_tosa_MI_pipeline(
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self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
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):
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(
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ArmTester(
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module,
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example_inputs=test_data,
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compile_spec=common.get_tosa_compile_spec(),
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)
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.export()
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.check(["torch.ops.aten.permute.default"])
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.check_not(["torch.ops.quantized_decomposed"])
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.to_edge()
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.partition()
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.check_not(["executorch_exir_dialects_edge__ops_aten_permute_default"])
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.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
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.to_executorch()
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.run_method_and_compare_outputs(inputs=test_data)
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)
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def _test_permute_tosa_BI_pipeline(
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self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
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):
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quantizer = ArmQuantizer().set_io(get_symmetric_quantization_config())
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(
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ArmTester(
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module,
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example_inputs=test_data,
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compile_spec=common.get_tosa_compile_spec(),
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)
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.quantize(Quantize(quantizer, get_symmetric_quantization_config()))
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.export()
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.check_count({"torch.ops.aten.permute.default": 1})
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.check(["torch.ops.quantized_decomposed"])
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.to_edge()
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.partition()
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.check_not(["executorch_exir_dialects_edge__ops_aten_permute_default"])
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.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
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.to_executorch()
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.run_method_and_compare_outputs(inputs=test_data)
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)
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def _test_permute_tosa_u55_BI_pipeline(
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self, module: torch.nn.Module, test_data: Tuple[torch.tensor]
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):
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quantizer = ArmQuantizer().set_io(get_symmetric_quantization_config())
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(
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ArmTester(
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module,
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example_inputs=test_data,
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compile_spec=common.get_u55_compile_spec(),
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)
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.quantize(Quantize(quantizer, get_symmetric_quantization_config()))
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.export()
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.check_count({"torch.ops.aten.permute.default": 1})
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.check(["torch.ops.quantized_decomposed"])
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.to_edge()
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.partition()
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.check_not(["executorch_exir_dialects_edge__ops_aten_permute_default"])
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.check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
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.to_executorch()
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)
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@parameterized.expand(test_data_suite)
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def test_permute_tosa_MI(
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self, test_name: str, test_data: torch.Tensor, dims: list[int]
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):
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self._test_permute_tosa_MI_pipeline(self.Permute(dims=dims), (test_data,))
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@parameterized.expand(test_data_suite)
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def test_permute_tosa_BI(
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self, test_name: str, test_data: torch.Tensor, dims: list[int]
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):
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self._test_permute_tosa_BI_pipeline(self.Permute(dims=dims), (test_data,))
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# Expected to fail as Permute is not supported by the NPU
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@parameterized.expand(test_data_suite)
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@unittest.expectedFailure
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def test_permute_tosa_u55_BI(
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self, test_name: str, test_data: torch.Tensor, dims: list[int]
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):
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self._test_permute_tosa_u55_BI_pipeline(self.Permute(dims=dims), (test_data,))

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