pytorch/test/functorch/test_rearrange.py

# Owner(s): ["module: functorch"]

"""Adapted from https://github.com/arogozhnikov/einops/blob/230ac1526c1f42c9e1f7373912c7f8047496df11/tests/test_ops.py.

MIT License

Copyright (c) 2018 Alex Rogozhnikov

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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"""

from typing import List, Tuple

import numpy as np
import torch
from functorch.einops import rearrange
from torch.testing._internal.common_utils import TestCase, run_tests

identity_patterns: List[str] = [
    '...->...',
    'a b c d e-> a b c d e',
    'a b c d e ...-> ... a b c d e',
    'a b c d e ...-> a ... b c d e',
    '... a b c d e -> ... a b c d e',
    'a ... e-> a ... e',
    'a ... -> a ... ',
    'a ... c d e -> a (...) c d e',
]

equivalent_rearrange_patterns: List[Tuple[str, str]] = [
    ('a b c d e -> (a b) c d e', 'a b ... -> (a b) ... '),
    ('a b c d e -> a b (c d) e', '... c d e -> ... (c d) e'),
    ('a b c d e -> a b c d e', '... -> ... '),
    ('a b c d e -> (a b c d e)', '... ->  (...)'),
    ('a b c d e -> b (c d e) a', 'a b ... -> b (...) a'),
    ('a b c d e -> b (a c d) e', 'a b ... e -> b (a ...) e'),
]


class TestRearrange(TestCase):
    def test_collapsed_ellipsis_errors_out(self) -> None:
        x = torch.zeros([1, 1, 1, 1, 1])
        rearrange(x, 'a b c d ... ->  a b c ... d')
        with self.assertRaises(ValueError):
            rearrange(x, 'a b c d (...) ->  a b c ... d')

        rearrange(x, '... ->  (...)')
        with self.assertRaises(ValueError):
            rearrange(x, '(...) -> (...)')

    def test_ellipsis_ops(self) -> None:
        x = torch.arange(2 * 3 * 4 * 5 * 6).reshape([2, 3, 4, 5, 6])
        for pattern in identity_patterns:
            torch.testing.assert_close(rearrange(x, pattern), x, msg=pattern)

        for pattern1, pattern2 in equivalent_rearrange_patterns:
            torch.testing.assert_close(rearrange(x, pattern1), rearrange(x, pattern2), msg=f"{pattern1} vs {pattern2}")

    def test_rearrange_consistency(self) -> None:
        shape = [1, 2, 3, 5, 7, 11]
        x = torch.arange(int(np.prod(shape, dtype=int))).reshape(shape)
        for pattern in [
            'a b c d e f -> a b c d e f',
            'b a c d e f -> a b d e f c',
            'a b c d e f -> f e d c b a',
            'a b c d e f -> (f e) d (c b a)',
            'a b c d e f -> (f e d c b a)',
        ]:
            result = rearrange(x, pattern)
            self.assertEqual(len(np.setdiff1d(x, result)), 0)
            self.assertIs(result.dtype, x.dtype)

        result = rearrange(x, 'a b c d e f -> a (b) (c d e) f')
        torch.testing.assert_close(x.flatten(), result.flatten())

        result = rearrange(x, 'a aa aa1 a1a1 aaaa a11 -> a aa aa1 a1a1 aaaa a11')
        torch.testing.assert_close(x, result)

        result1 = rearrange(x, 'a b c d e f -> f e d c b a')
        result2 = rearrange(x, 'f e d c b a -> a b c d e f')
        torch.testing.assert_close(result1, result2)

        result = rearrange(rearrange(x, 'a b c d e f -> (f d) c (e b) a'), '(f d) c (e b) a -> a b c d e f', b=2, d=5)
        torch.testing.assert_close(x, result)

        sizes = dict(zip('abcdef', shape))
        temp = rearrange(x, 'a b c d e f -> (f d) c (e b) a', **sizes)
        result = rearrange(temp, '(f d) c (e b) a -> a b c d e f', **sizes)
        torch.testing.assert_close(x, result)

        x2 = torch.arange(2 * 3 * 4).reshape([2, 3, 4])
        result = rearrange(x2, 'a b c -> b c a')
        self.assertEqual(x2[1, 2, 3], result[2, 3, 1])
        self.assertEqual(x2[0, 1, 2], result[1, 2, 0])

    def test_rearrange_permutations(self) -> None:
        # tests random permutation of axes against two independent numpy ways
        for n_axes in range(1, 10):
            input = torch.arange(2 ** n_axes).reshape([2] * n_axes)
            permutation = np.random.permutation(n_axes)
            left_expression = ' '.join('i' + str(axis) for axis in range(n_axes))
            right_expression = ' '.join('i' + str(axis) for axis in permutation)
            expression = left_expression + ' -> ' + right_expression
            result = rearrange(input, expression)

            for pick in np.random.randint(0, 2, [10, n_axes]):
                self.assertEqual(input[tuple(pick)], result[tuple(pick[permutation])])

        for n_axes in range(1, 10):
            input = torch.arange(2 ** n_axes).reshape([2] * n_axes)
            permutation = np.random.permutation(n_axes)
            left_expression = ' '.join('i' + str(axis) for axis in range(n_axes)[::-1])
            right_expression = ' '.join('i' + str(axis) for axis in permutation[::-1])
            expression = left_expression + ' -> ' + right_expression
            result = rearrange(input, expression)
            self.assertEqual(result.shape, input.shape)
            expected_result = torch.zeros_like(input)
            for original_axis, result_axis in enumerate(permutation):
                expected_result |= ((input >> original_axis) & 1) << result_axis

            torch.testing.assert_close(result, expected_result)

    def test_concatenations_and_stacking(self) -> None:
        for n_arrays in [1, 2, 5]:
            shapes: List[List[int]] = [[], [1], [1, 1], [2, 3, 5, 7], [1] * 6]
            for shape in shapes:
                arrays1 = [torch.arange(i, i + np.prod(shape, dtype=int)).reshape(shape) for i in range(n_arrays)]
                result0 = torch.stack(arrays1)
                result1 = rearrange(arrays1, '...->...')
                torch.testing.assert_close(result0, result1)

    def test_unsqueeze(self) -> None:
        x = torch.randn((2, 3, 4, 5))
        actual = rearrange(x, 'b h w c -> b 1 h w 1 c')
        expected = x.unsqueeze(1).unsqueeze(-2)
        torch.testing.assert_close(actual, expected)

    def test_squeeze(self) -> None:
        x = torch.randn((2, 1, 3, 4, 1, 5))
        actual = rearrange(x, 'b 1 h w 1 c -> b h w c')
        expected = x.squeeze()
        torch.testing.assert_close(actual, expected)

    def test_0_dim_tensor(self) -> None:
        x = expected = torch.tensor(1)
        actual = rearrange(x, '->')
        torch.testing.assert_close(actual, expected)

        actual = rearrange(x, '... -> ...')
        torch.testing.assert_close(actual, expected)

    def test_dimension_mismatch_no_ellipsis(self) -> None:
        x = torch.randn((1, 2, 3))
        with self.assertRaises(ValueError):
            rearrange(x, "a b -> b a")

        with self.assertRaises(ValueError):
            rearrange(x, "a b c d -> c d b a")

    def test_dimension_mismatch_with_ellipsis(self) -> None:
        x = torch.tensor(1)
        with self.assertRaises(ValueError):
            rearrange(x, "a ... -> ... a")


if __name__ == '__main__':
    run_tests()