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|
| import logging |
| import unittest |
|
|
| from fairseq.dataclass.utils import convert_namespace_to_omegaconf |
| from fairseq.models.transformer import TransformerModel |
| from tests.test_sequence_generator import get_dummy_task_and_parser |
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|
|
|
| class TestInferenceDropout(unittest.TestCase): |
| def setUp(self): |
| self.task, self.parser = get_dummy_task_and_parser() |
| TransformerModel.add_args(self.parser) |
| self.args = self.parser.parse_args([]) |
| self.args.encoder_layers = 2 |
| self.args.decoder_layers = 1 |
| logging.disable(logging.CRITICAL) |
|
|
| def tearDown(self): |
| logging.disable(logging.NOTSET) |
|
|
| def test_sets_inference_dropout_to_true(self): |
| self.args.retain_dropout = True |
| self.transformer_model = TransformerModel.build_model(self.args, self.task) |
| cfg = convert_namespace_to_omegaconf(self.args) |
| self.transformer_model.prepare_for_inference_(cfg) |
| assert self.transformer_model.encoder.dropout_module.apply_during_inference |
| assert self.transformer_model.decoder.dropout_module.apply_during_inference |
| for layer in self.transformer_model.encoder.layers: |
| assert layer.dropout_module.apply_during_inference |
|
|
| def test_inference_dropout_false_by_default(self): |
| self.transformer_model = TransformerModel.build_model(self.args, self.task) |
| cfg = convert_namespace_to_omegaconf(self.args) |
| self.transformer_model.prepare_for_inference_(cfg) |
| assert not self.transformer_model.encoder.dropout_module.apply_during_inference |
| assert not self.transformer_model.decoder.dropout_module.apply_during_inference |
| for layer in self.transformer_model.encoder.layers: |
| assert not layer.dropout_module.apply_during_inference |
| for layer in self.transformer_model.decoder.layers: |
| assert not layer.dropout_module.apply_during_inference |
|
|
| def test_applies_training_mode(self): |
| self.transformer_model = TransformerModel.build_model(self.args, self.task) |
| assert self.transformer_model.encoder.dropout_module.training |
| for layer in self.transformer_model.encoder.layers: |
| assert layer.dropout_module.training |
|
|
| self.transformer_model.eval() |
| assert not self.transformer_model.decoder.dropout_module.training |
| for layer in self.transformer_model.encoder.layers: |
| assert not layer.dropout_module.training |
|
|
| def test_retain_modules(self): |
| self.args.retain_dropout = True |
| self.args.retain_dropout_modules = [ |
| "TransformerEncoder", |
| "TransformerEncoderLayer", |
| ] |
| self.transformer_model = TransformerModel.build_model(self.args, self.task) |
| cfg = convert_namespace_to_omegaconf(self.args) |
| self.transformer_model.prepare_for_inference_(cfg) |
| assert self.transformer_model.encoder.dropout_module.apply_during_inference |
| assert not self.transformer_model.decoder.dropout_module.apply_during_inference |
| for layer in self.transformer_model.decoder.layers: |
| assert not layer.dropout_module.apply_during_inference |
|
|
