File size: 4,867 Bytes
ee21b96
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import unittest

import numpy as np
from fairseq.data.data_utils_fast import batch_by_size_fn
from fairseq.data.data_utils_fast import batch_by_size_vec


class TestBatchBySize(unittest.TestCase):
    @classmethod
    def batch_by_size_baseline(
        cls,
        indices,
        num_tokens_vec,
        max_tokens,
        max_sentences,
        bsz_mult,
    ):
        """Simple, reliable and slow implementation of batch by size """
        batches = []
        start = 0
        while start < len(indices):
            for end in range(start + 1, len(indices) + 1):
                max_val = max(num_tokens_vec[pos] for pos in range(start, end))
                sent_count = end - start
                num_tokens = max_val * sent_count
                overflow = num_tokens > max_tokens > 0 or sent_count > max_sentences > 0
                terminate = overflow or end == len(indices)
                if overflow:
                    sent_count -= 1
                if terminate:
                    if sent_count > bsz_mult:
                        sent_count = sent_count - sent_count % bsz_mult
                    batches.append(indices[start : start + sent_count])
                    start = start + sent_count
                    break
        return batches

    @classmethod
    def _get_error_message(
        cls, max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results
    ):
        return f"""Reference batch_by_size implementation should produce
                    same output as the baseline method.
                Params:
                max_sentences={max_sentences},
                max_tokens={max_tokens},
                bsz_mult={bsz_mult},
                num_tokens_vec={num_tokens_vec},
                expected_batches={validation},
                returned_batches={results}"""

    def _compare_results(
        self,
        indices_len,
        batch_by_size_impl,
        max_sentences,
        max_tokens,
        bsz_mult,
        num_tokens_vec,
    ):
        indices = np.array(list(range(indices_len)))
        validation = self.batch_by_size_baseline(
            indices,
            num_tokens_vec,
            max_tokens=max_tokens,
            max_sentences=max_sentences,
            bsz_mult=bsz_mult,
        )
        results = batch_by_size_impl(
            indices,
            num_tokens_vec,
            max_tokens=max_tokens,
            max_sentences=max_sentences,
            bsz_mult=bsz_mult,
        )
        error_msg = self._get_error_message(
            max_sentences, max_tokens, bsz_mult, num_tokens_vec, validation, results
        )
        self.assertEqual(len(validation), len(results), error_msg)
        for first, second in zip(validation, results):
            self.assertTrue(np.array_equal(first, second), error_msg)

    def _run_compare_with_baseline_sweep(self, batch_by_size_impl):
        """Compare reference batch_by_size implementation with batch_by_size_baseline
        across a dense grid of hyperparam values"""
        MAX_MAX_TOKENS = 10
        NUM_TOKENS_VECS_COUNT = 5
        for indices_len in [10, 11]:  # try odd and even len of indices
            for max_sentences in range(0, indices_len + 2):
                for max_tokens in range(0, MAX_MAX_TOKENS):
                    for bsz_mult in range(1, max(MAX_MAX_TOKENS, indices_len) + 2):
                        for _ in range(NUM_TOKENS_VECS_COUNT):
                            num_tokens_vec = np.random.randint(
                                0, max_tokens + 1, size=indices_len
                            )
                            self._compare_results(
                                indices_len,
                                batch_by_size_impl,
                                max_sentences,
                                max_tokens,
                                bsz_mult,
                                num_tokens_vec,
                            )


class TestBatchBySizeVec(TestBatchBySize):
    def test_compare_with_baseline(self):
        self._run_compare_with_baseline_sweep(batch_by_size_vec)


class TestBatchBySizeFn(TestBatchBySize):
    def test_compare_with_baseline(self):
        def batch_by_size_fn_wrapper(
            indices,
            num_tokens_vec,
            max_tokens,
            max_sentences,
            bsz_mult,
        ):
            def num_tokens_fn(idx):
                return num_tokens_vec[idx]

            return batch_by_size_fn(
                indices, num_tokens_fn, max_tokens, max_sentences, bsz_mult
            )

        self._run_compare_with_baseline_sweep(batch_by_size_fn_wrapper)


if __name__ == "__main__":
    unittest.main()