| # Copyright 2024 Arm Limited and/or its affiliates. |
| # All rights reserved. |
| # |
| # This source code is licensed under the BSD-style license found in the |
| # LICENSE file in the root directory of this source tree. |
| |
| import unittest |
| |
| from typing import Tuple |
| |
| import torch |
| from executorch.backends.arm.test import common |
| from executorch.backends.arm.test.tester.arm_tester import ArmTester |
| from executorch.exir import EdgeCompileConfig |
| from executorch.exir.backend.compile_spec_schema import CompileSpec |
| from parameterized import parameterized |
| |
| exampledata_t = Tuple[torch.Tensor, int | list[int], bool] |
| """(data, dim(s), keepdim)""" |
| |
| |
| class TestSum(unittest.TestCase): |
| """Tests sum which sums all elements along some specified dimensions. |
| keepdim specifies whether the dimension that is summed should |
| be squeezed or not. |
| """ |
| |
| class Sum(torch.nn.Module): |
| test_parameters: list[Tuple[exampledata_t]] = [ |
| ((torch.rand(10), 0, True),), |
| ((torch.rand(10, 10), 1, False),), |
| ((torch.rand(10, 10, 10), [-3, 1], True),), |
| ((torch.rand(2, 1, 5, 8), 1, False),), |
| ((torch.rand(1, 2, 3, 4), 3, True),), |
| ((torch.rand(1, 2, 8, 8), [2, 3, 0], True),), |
| ] |
| |
| def forward(self, x: torch.Tensor, dim: int, keepdim: bool): |
| return x.sum(dim=dim, keepdim=keepdim) |
| |
| _edge_compile_config: EdgeCompileConfig = EdgeCompileConfig( |
| _skip_dim_order=True, # TODO(T182928844): Delegate dim order op to backend. |
| ) |
| |
| def _test_sum_tosa_MI_pipeline( |
| self, module: torch.nn.Module, test_data: tuple[exampledata_t] |
| ): |
| ( |
| ArmTester( |
| module, |
| example_inputs=test_data, |
| compile_spec=common.get_tosa_compile_spec("TOSA-0.80.0+MI"), |
| ) |
| .export() |
| .check_count({"torch.ops.aten.sum.dim_IntList": 1}) |
| .check_not(["torch.ops.quantized_decomposed"]) |
| .to_edge(config=self._edge_compile_config) |
| .partition() |
| .check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) |
| .to_executorch() |
| .run_method_and_compare_outputs(inputs=test_data) |
| ) |
| |
| def _test_sum_tosa_BI_pipeline( |
| self, module: torch.nn.Module, test_data: tuple[exampledata_t] |
| ): |
| ( |
| ArmTester( |
| module, |
| example_inputs=test_data, |
| compile_spec=common.get_tosa_compile_spec("TOSA-0.80.0+BI"), |
| ) |
| .quantize() |
| .export() |
| .check_count({"torch.ops.aten.sum.dim_IntList": 1}) |
| .check(["torch.ops.quantized_decomposed"]) |
| .to_edge(config=self._edge_compile_config) |
| .partition() |
| .check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) |
| .to_executorch() |
| .run_method_and_compare_outputs(inputs=test_data, qtol=1) |
| ) |
| |
| def _test_sum_ethosu_BI_pipeline( |
| self, |
| module: torch.nn.Module, |
| test_data: tuple[exampledata_t], |
| compile_spec: CompileSpec, |
| ): |
| ( |
| ArmTester( |
| module, |
| example_inputs=test_data, |
| compile_spec=compile_spec, |
| ) |
| .quantize() |
| .export() |
| .check_count({"torch.ops.aten.sum.dim_IntList": 1}) |
| .check(["torch.ops.quantized_decomposed"]) |
| .to_edge() |
| .partition() |
| .check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) |
| .to_executorch() |
| .serialize() |
| ) |
| |
| @parameterized.expand(Sum.test_parameters) |
| def test_sum_tosa_MI(self, test_data: tuple[exampledata_t]): |
| self._test_sum_tosa_MI_pipeline(self.Sum(), test_data) |
| |
| @parameterized.expand(Sum.test_parameters) |
| def test_sum_tosa_BI(self, test_data: tuple[exampledata_t]): |
| self._test_sum_tosa_BI_pipeline(self.Sum(), test_data) |
| |
| @parameterized.expand(Sum.test_parameters) |
| def test_sum_u55_BI(self, test_data: tuple[exampledata_t]): |
| self._test_sum_ethosu_BI_pipeline( |
| self.Sum(), |
| test_data, |
| common.get_u55_compile_spec(permute_memory_to_nhwc=False), |
| ) |
| |
| @parameterized.expand(Sum.test_parameters) |
| def test_sum_u85_BI(self, test_data: tuple[exampledata_t]): |
| self._test_sum_ethosu_BI_pipeline( |
| self.Sum(), |
| test_data, |
| common.get_u85_compile_spec(permute_memory_to_nhwc=True), |
| ) |