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- # coding=utf-8
- # Copyright 2020 The HuggingFace Team. All rights reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
-
- import unittest
-
- from transformers import is_torch_available
- from transformers.testing_utils import require_torch, slow, torch_device
-
- from .test_configuration_common import ConfigTester
- from .test_generation_utils import GenerationTesterMixin
- from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
-
-
- if is_torch_available():
- import torch
-
- from transformers import BertGenerationConfig, BertGenerationDecoder, BertGenerationEncoder
-
-
- class BertGenerationEncoderTester:
- def __init__(
- self,
- parent,
- batch_size=13,
- seq_length=7,
- is_training=True,
- use_input_mask=True,
- vocab_size=99,
- hidden_size=32,
- num_hidden_layers=5,
- num_attention_heads=4,
- intermediate_size=37,
- hidden_act="gelu",
- hidden_dropout_prob=0.1,
- attention_probs_dropout_prob=0.1,
- max_position_embeddings=50,
- initializer_range=0.02,
- use_labels=True,
- scope=None,
- ):
- self.parent = parent
- self.batch_size = batch_size
- self.seq_length = seq_length
- self.is_training = is_training
- self.use_input_mask = use_input_mask
- self.vocab_size = vocab_size
- self.hidden_size = hidden_size
- self.num_hidden_layers = num_hidden_layers
- self.num_attention_heads = num_attention_heads
- self.intermediate_size = intermediate_size
- self.hidden_act = hidden_act
- self.hidden_dropout_prob = hidden_dropout_prob
- self.attention_probs_dropout_prob = attention_probs_dropout_prob
- self.max_position_embeddings = max_position_embeddings
- self.initializer_range = initializer_range
- self.use_labels = use_labels
- self.scope = scope
-
- def prepare_config_and_inputs(self):
- input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
-
- input_mask = None
- if self.use_input_mask:
- input_mask = random_attention_mask([self.batch_size, self.seq_length])
-
- if self.use_labels:
- token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
-
- config = BertGenerationConfig(
- vocab_size=self.vocab_size,
- hidden_size=self.hidden_size,
- num_hidden_layers=self.num_hidden_layers,
- num_attention_heads=self.num_attention_heads,
- intermediate_size=self.intermediate_size,
- hidden_act=self.hidden_act,
- hidden_dropout_prob=self.hidden_dropout_prob,
- attention_probs_dropout_prob=self.attention_probs_dropout_prob,
- max_position_embeddings=self.max_position_embeddings,
- is_decoder=False,
- initializer_range=self.initializer_range,
- )
-
- return config, input_ids, input_mask, token_labels
-
- def prepare_config_and_inputs_for_decoder(self):
- (
- config,
- input_ids,
- input_mask,
- token_labels,
- ) = self.prepare_config_and_inputs()
-
- config.is_decoder = True
- encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
- encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
-
- return (
- config,
- input_ids,
- input_mask,
- token_labels,
- encoder_hidden_states,
- encoder_attention_mask,
- )
-
- def create_and_check_model(
- self,
- config,
- input_ids,
- input_mask,
- token_labels,
- **kwargs,
- ):
- model = BertGenerationEncoder(config=config)
- model.to(torch_device)
- model.eval()
- result = model(input_ids, attention_mask=input_mask)
- result = model(input_ids)
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
-
- def create_and_check_model_as_decoder(
- self,
- config,
- input_ids,
- input_mask,
- token_labels,
- encoder_hidden_states,
- encoder_attention_mask,
- **kwargs,
- ):
- config.add_cross_attention = True
- model = BertGenerationEncoder(config=config)
- model.to(torch_device)
- model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_attention_mask,
- )
- result = model(
- input_ids,
- attention_mask=input_mask,
- encoder_hidden_states=encoder_hidden_states,
- )
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
-
- def create_and_check_decoder_model_past_large_inputs(
- self,
- config,
- input_ids,
- input_mask,
- token_labels,
- encoder_hidden_states,
- encoder_attention_mask,
- **kwargs,
- ):
- config.is_decoder = True
- config.add_cross_attention = True
- model = BertGenerationDecoder(config=config).to(torch_device).eval()
-
- # first forward pass
- outputs = model(
- input_ids,
- attention_mask=input_mask,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_attention_mask,
- use_cache=True,
- )
- past_key_values = outputs.past_key_values
-
- # create hypothetical multiple next token and extent to next_input_ids
- next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
- next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
-
- # append to next input_ids and
- next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
- next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
-
- output_from_no_past = model(
- next_input_ids,
- attention_mask=next_attention_mask,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_attention_mask,
- output_hidden_states=True,
- )["hidden_states"][0]
- output_from_past = model(
- next_tokens,
- attention_mask=next_attention_mask,
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=encoder_attention_mask,
- past_key_values=past_key_values,
- output_hidden_states=True,
- )["hidden_states"][0]
-
- # select random slice
- random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
- output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
- output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
-
- self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
-
- # test that outputs are equal for slice
- self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
-
- def create_and_check_for_causal_lm(
- self,
- config,
- input_ids,
- input_mask,
- token_labels,
- *args,
- ):
- model = BertGenerationDecoder(config)
- model.to(torch_device)
- model.eval()
- result = model(input_ids, attention_mask=input_mask, labels=token_labels)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
-
- def prepare_config_and_inputs_for_common(self):
- config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs()
- inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
- return config, inputs_dict
-
-
- @require_torch
- class BertGenerationEncoderTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
-
- all_model_classes = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
- all_generative_model_classes = (BertGenerationDecoder,) if is_torch_available() else ()
-
- def setUp(self):
- self.model_tester = BertGenerationEncoderTester(self)
- self.config_tester = ConfigTester(self, config_class=BertGenerationConfig, hidden_size=37)
-
- def test_config(self):
- self.config_tester.run_common_tests()
-
- def test_model(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_model(*config_and_inputs)
-
- def test_model_as_bert(self):
- config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
- config.model_type = "bert"
- self.model_tester.create_and_check_model(config, input_ids, input_mask, token_labels)
-
- def test_model_as_decoder(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
- self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
-
- def test_decoder_model_past_with_large_inputs(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
- self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
-
- def test_model_as_decoder_with_default_input_mask(self):
- # This regression test was failing with PyTorch < 1.3
- (
- config,
- input_ids,
- input_mask,
- token_labels,
- encoder_hidden_states,
- encoder_attention_mask,
- ) = self.model_tester.prepare_config_and_inputs_for_decoder()
-
- input_mask = None
-
- self.model_tester.create_and_check_model_as_decoder(
- config,
- input_ids,
- input_mask,
- token_labels,
- encoder_hidden_states,
- encoder_attention_mask,
- )
-
- def test_for_causal_lm(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
- self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
-
- @slow
- def test_model_from_pretrained(self):
- model = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
- self.assertIsNotNone(model)
-
-
- @require_torch
- class BertGenerationEncoderIntegrationTest(unittest.TestCase):
- @slow
- def test_inference_no_head_absolute_embedding(self):
- model = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
- input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
- output = model(input_ids)[0]
- expected_shape = torch.Size([1, 8, 1024])
- self.assertEqual(output.shape, expected_shape)
- expected_slice = torch.tensor(
- [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]
- )
- self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
-
-
- @require_torch
- class BertGenerationDecoderIntegrationTest(unittest.TestCase):
- @slow
- def test_inference_no_head_absolute_embedding(self):
- model = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
- input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
- output = model(input_ids)[0]
- expected_shape = torch.Size([1, 8, 50358])
- self.assertEqual(output.shape, expected_shape)
- expected_slice = torch.tensor(
- [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]
- )
- self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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