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Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1357 Reviewed By: alexeib Differential Revision: D24377772 fbshipit-source-id: 51581af041d42d62166b33a35a1a4228b1a76f0c
121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import argparse
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import unittest
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import tests.utils as test_utils
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import torch
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from fairseq.sequence_scorer import SequenceScorer
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class TestSequenceScorer(unittest.TestCase):
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def test_sequence_scorer(self):
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# construct dummy dictionary
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d = test_utils.dummy_dictionary(vocab_size=2)
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self.assertEqual(d.pad(), 1)
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self.assertEqual(d.eos(), 2)
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self.assertEqual(d.unk(), 3)
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eos = d.eos()
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w1 = 4
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w2 = 5
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# construct dataloader
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data = [
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{
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"source": torch.LongTensor([w1, w2, eos]),
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"target": torch.LongTensor([w1, w2, w1, eos]),
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},
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{
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"source": torch.LongTensor([w2, eos]),
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"target": torch.LongTensor([w2, w1, eos]),
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},
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{
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"source": torch.LongTensor([w2, eos]),
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"target": torch.LongTensor([w2, eos]),
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},
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]
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data_itr = test_utils.dummy_dataloader(data)
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# specify expected output probabilities
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args = argparse.Namespace()
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unk = 0.0
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args.beam_probs = [
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# step 0:
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torch.FloatTensor(
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[
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# eos w1 w2
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[0.0, unk, 0.6, 0.4], # sentence 1
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[0.0, unk, 0.4, 0.6], # sentence 2
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[0.0, unk, 0.7, 0.3], # sentence 3
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]
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),
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# step 1:
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torch.FloatTensor(
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[
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# eos w1 w2
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[0.0, unk, 0.2, 0.7], # sentence 1
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[0.0, unk, 0.8, 0.2], # sentence 2
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[0.7, unk, 0.1, 0.2], # sentence 3
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]
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),
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# step 2:
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torch.FloatTensor(
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[
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# eos w1 w2
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[0.10, unk, 0.50, 0.4], # sentence 1
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[0.15, unk, 0.15, 0.7], # sentence 2
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[0.00, unk, 0.00, 0.0], # sentence 3
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]
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),
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# step 3:
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torch.FloatTensor(
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[
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# eos w1 w2
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[0.9, unk, 0.05, 0.05], # sentence 1
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[0.0, unk, 0.00, 0.0], # sentence 2
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[0.0, unk, 0.00, 0.0], # sentence 3
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]
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),
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]
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expected_scores = [
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[0.6, 0.7, 0.5, 0.9], # sentence 1
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[0.6, 0.8, 0.15], # sentence 2
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[0.3, 0.7], # sentence 3
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]
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task = test_utils.TestTranslationTask.setup_task(args, d, d)
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model = task.build_model(args)
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scorer = SequenceScorer(task.target_dictionary)
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for sample in data_itr:
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hypos = task.inference_step(scorer, [model], sample)
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for id, hypos_id in zip(sample["id"].tolist(), hypos):
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self.assertHypoTokens(hypos_id[0], data[id]["target"])
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self.assertHypoScore(hypos_id[0], expected_scores[id])
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def assertHypoTokens(self, hypo, tokens):
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self.assertTensorEqual(hypo["tokens"], torch.LongTensor(tokens))
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def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0):
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pos_scores = torch.FloatTensor(pos_probs).log()
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self.assertAlmostEqual(hypo["positional_scores"], pos_scores)
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self.assertEqual(pos_scores.numel(), hypo["tokens"].numel())
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score = pos_scores.sum()
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if normalized:
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score /= pos_scores.numel() ** lenpen
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self.assertLess(abs(score - hypo["score"]), 1e-6)
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def assertAlmostEqual(self, t1, t2):
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self.assertEqual(t1.size(), t2.size(), "size mismatch")
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self.assertLess((t1 - t2).abs().max(), 1e-4)
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def assertTensorEqual(self, t1, t2):
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self.assertEqual(t1.size(), t2.size(), "size mismatch")
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self.assertEqual(t1.ne(t2).long().sum(), 0)
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if __name__ == "__main__":
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unittest.main()
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