mirror of
https://github.com/facebookresearch/fairseq.git
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1b749f4a34
Summary: Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1120 Pull Request resolved: https://github.com/pytorch/fairseq/pull/1940 Deprecate the SequenceGenerator in Fairseq with the Scripted vision. Pass all integration unit tests - Copy ScriptSequenceGenerator to SequenceGenerator: - Modified the forward_decoder to fix bug when using adaptive_softmax in `get_prob_normalize` (marked with the inline comment) - Add support for other EnsembleModels as input arg (marked with the inline comment) - Add `FBEnsembleModelWithFork` to support folk/join in ensemblemodel - Add `test_fb_ensemble_model` to test folk/join feature - Still have bugs in folk/join feature when running in the Fairseq interface (like generation and interactive). Need further investigation P128130029. cc cndn, jhcross - Modified SequenceGenerator initialization the interface - Clear up the codes: delete unused functions `get_normalized_probs` and `_decode` Reland reverted diff D20685075 Reviewed By: cndn Differential Revision: D20895977 fbshipit-source-id: 424ee318e67d5d6ffed3edb92c7fa78485ba34af
117 lines
3.9 KiB
Python
117 lines
3.9 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 unittest
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import torch
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from fairseq.data import (
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BacktranslationDataset,
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LanguagePairDataset,
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TransformEosDataset,
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)
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from fairseq.sequence_generator import SequenceGenerator
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import tests.utils as test_utils
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class TestBacktranslationDataset(unittest.TestCase):
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def setUp(self):
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self.tgt_dict, self.w1, self.w2, self.src_tokens, self.src_lengths, self.model = (
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test_utils.sequence_generator_setup()
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)
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dummy_src_samples = self.src_tokens
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self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
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self.cuda = torch.cuda.is_available()
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def _backtranslation_dataset_helper(
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self, remove_eos_from_input_src, remove_eos_from_output_src,
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):
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tgt_dataset = LanguagePairDataset(
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src=self.tgt_dataset,
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src_sizes=self.tgt_dataset.sizes,
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src_dict=self.tgt_dict,
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tgt=None,
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tgt_sizes=None,
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tgt_dict=None,
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)
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generator = SequenceGenerator(
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[self.model],
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tgt_dict=self.tgt_dict,
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max_len_a=0,
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max_len_b=200,
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beam_size=2,
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unk_penalty=0,
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)
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backtranslation_dataset = BacktranslationDataset(
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tgt_dataset=TransformEosDataset(
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dataset=tgt_dataset,
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eos=self.tgt_dict.eos(),
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# remove eos from the input src
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remove_eos_from_src=remove_eos_from_input_src,
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),
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src_dict=self.tgt_dict,
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backtranslation_fn=(
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lambda sample: generator.generate([self.model], sample)
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),
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output_collater=TransformEosDataset(
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dataset=tgt_dataset,
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eos=self.tgt_dict.eos(),
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# if we remove eos from the input src, then we need to add it
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# back to the output tgt
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append_eos_to_tgt=remove_eos_from_input_src,
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remove_eos_from_src=remove_eos_from_output_src,
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).collater,
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cuda=self.cuda,
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)
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dataloader = torch.utils.data.DataLoader(
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backtranslation_dataset,
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batch_size=2,
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collate_fn=backtranslation_dataset.collater,
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)
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backtranslation_batch_result = next(iter(dataloader))
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eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2
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# Note that we sort by src_lengths and add left padding, so actually
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# ids will look like: [1, 0]
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expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]])
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if remove_eos_from_output_src:
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expected_src = expected_src[:, :-1]
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expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
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generated_src = backtranslation_batch_result["net_input"]["src_tokens"]
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tgt_tokens = backtranslation_batch_result["target"]
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self.assertTensorEqual(expected_src, generated_src)
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self.assertTensorEqual(expected_tgt, tgt_tokens)
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def test_backtranslation_dataset_no_eos_in_output_src(self):
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self._backtranslation_dataset_helper(
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remove_eos_from_input_src=False, remove_eos_from_output_src=True,
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)
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def test_backtranslation_dataset_with_eos_in_output_src(self):
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self._backtranslation_dataset_helper(
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remove_eos_from_input_src=False, remove_eos_from_output_src=False,
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)
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def test_backtranslation_dataset_no_eos_in_input_src(self):
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self._backtranslation_dataset_helper(
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remove_eos_from_input_src=True, remove_eos_from_output_src=False,
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)
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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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