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https://github.com/huggingface/transformers.git
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* first draft * show design proposition for new generate method * up * make better readable * make first version * gpt2 tests pass * make beam search for gpt2 work * add first encoder-decoder code * delete typo * make t5 work * save indermediate * make bart work with beam search * finish beam search bart / t5 * add default kwargs * make more tests pass * fix no bad words sampler * some fixes and tests for all distribution processors * fix test * fix rag slow tests * merge to master * add nograd to generate * make all slow tests pass * speed up generate * fix edge case bug * small fix * correct typo * add type hints and docstrings * fix typos in tests * add beam search tests * add tests for beam scorer * fix test rag * finish beam search tests * move generation tests in seperate file * fix generation tests * more tests * add aggressive generation tests * fix tests * add gpt2 sample test * add more docstring * add more docs * finish doc strings * apply some more of sylvains and sams comments * fix some typos * make fix copies * apply lysandres and sylvains comments * final corrections on examples * small fix for reformer
237 lines
8.0 KiB
Python
Executable File
237 lines
8.0 KiB
Python
Executable File
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_generation_utils import GenerationTesterMixin
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from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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if is_torch_available():
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from transformers import BertGenerationConfig, BertGenerationDecoder, BertGenerationEncoder
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class BertGenerationEncoderTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=50,
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initializer_range=0.02,
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use_labels=True,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.use_labels = use_labels
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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if self.use_labels:
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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config = BertGenerationConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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is_decoder=False,
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initializer_range=self.initializer_range,
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return_dict=True,
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)
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return config, input_ids, input_mask, token_labels
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def prepare_config_and_inputs_for_decoder(self):
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(
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config,
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input_ids,
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input_mask,
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token_labels,
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) = self.prepare_config_and_inputs()
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config.is_decoder = True
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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config,
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input_ids,
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input_mask,
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token_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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def create_and_check_model(
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self,
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config,
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input_ids,
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input_mask,
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token_labels,
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**kwargs,
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):
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model = BertGenerationEncoder(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_model_as_decoder(
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self,
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config,
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input_ids,
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input_mask,
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token_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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**kwargs,
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):
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config.add_cross_attention = True
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model = BertGenerationEncoder(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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)
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result = model(
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input_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_for_causal_lm(
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self,
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config,
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input_ids,
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input_mask,
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token_labels,
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*args,
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):
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model = BertGenerationDecoder(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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input_mask,
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token_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class BertGenerationEncoderTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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all_model_classes = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
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all_generative_model_classes = (BertGenerationDecoder,) if is_torch_available() else ()
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def setUp(self):
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self.model_tester = BertGenerationEncoderTester(self)
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self.config_tester = ConfigTester(self, config_class=BertGenerationConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_as_decoder(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
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self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
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def test_model_as_decoder_with_default_input_mask(self):
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# This regression test was failing with PyTorch < 1.3
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(
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config,
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input_ids,
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input_mask,
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token_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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) = self.model_tester.prepare_config_and_inputs_for_decoder()
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input_mask = None
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self.model_tester.create_and_check_model_as_decoder(
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config,
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input_ids,
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input_mask,
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token_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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def test_for_causal_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
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self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
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self.assertIsNotNone(model)
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