executorch/backends/xnnpack/test/ops/test_elu.py at 54170190a253c9ebcfdafa73be4fb054d871f88d · mcr229/executorch · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
# Copyright (c) Meta Platforms, Inc. and 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

import torch
from executorch.backends.xnnpack.test.tester import Tester


class TestElu(unittest.TestCase):
    def setUp(self):
        torch._dynamo.reset()

    class ELU(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.elu = torch.nn.ELU(alpha=0.5)

        def forward(self, x):
            return self.elu(x)

    class ELUFunctional(torch.nn.Module):
        def forward(self, x):
            return torch.nn.functional.elu(x, alpha=1.2)

    def _test_elu(self, inputs):
        (
            Tester(self.ELU(), inputs)
            .export()
            .check_count({"torch.ops.aten.elu.default": 1})
            .to_edge_transform_and_lower()
            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
            .check_not(
                [
                    "executorch_exir_dialects_edge__ops_aten_elu_default",
                ]
            )
            .to_executorch()
            .serialize()
            .run_method_and_compare_outputs()
        )

    @unittest.skip("PyTorch Pin Update Required")
    def _test_fp16_elu(self):
        inputs = (torch.randn(1, 3, 3).to(torch.float16),)
        self._test_elu(inputs)

    @unittest.skip("PyTorch Pin Update Required")
    def _test_fp32_elu(self):
        inputs = (torch.randn(1, 3, 3),)
        self._test_elu(inputs)

    @unittest.skip("Update Quantizer to quantize Elu")
    def _test_qs8_elu(self):
        inputs = (torch.randn(1, 3, 4, 4),)
        (
            Tester(self.ELU(), inputs)
            .quantize()
            .export()
            .check_count({"torch.ops.aten.elu.default": 1})
            .check(["torch.ops.quantized_decomposed"])
            .to_edge_transform_and_lower()
            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
            .check_not(
                [
                    "executorch_exir_dialects_edge__ops_aten_elu_default",
                    "torch.ops.quantized_decomposed",
                ]
            )
            .to_executorch()
            .serialize()
            .run_method_and_compare_outputs()
        )

    @unittest.skip("Update Quantizer to quantize Elu")
    def _test_qs8_elu_functional(self):
        inputs = (torch.randn(1, 3, 4, 4),)
        (
            Tester(self.ELU(), inputs)
            .quantize()
            .export()
            .check_count({"torch.ops.aten.elu.default": 1})
            .check(["torch.ops.quantized_decomposed"])
            .to_edge_transform_and_lower()
            .check_count({"torch.ops.higher_order.executorch_call_delegate": 1})
            .check_not(
                [
                    "executorch_exir_dialects_edge__ops_aten_elu_default",
                    "torch.ops.quantized_decomposed",
                ]
            )
            .to_executorch()
            .serialize()
            .run_method_and_compare_outputs()
        )