451 lines
17 KiB
Python
451 lines
17 KiB
Python
# 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_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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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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import torch
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from transformers import (
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RobertaConfig,
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RobertaForCausalLM,
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RobertaForMaskedLM,
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RobertaForMultipleChoice,
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RobertaForQuestionAnswering,
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RobertaForSequenceClassification,
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RobertaForTokenClassification,
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RobertaModel,
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)
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from transformers.modeling_roberta import (
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ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
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RobertaEmbeddings,
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create_position_ids_from_input_ids,
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)
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class RobertaModelTester:
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def __init__(
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self,
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parent,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_input_mask = True
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self.use_token_type_ids = True
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self.use_labels = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.num_hidden_layers = 5
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self.num_attention_heads = 4
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self.intermediate_size = 37
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 3
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self.num_choices = 4
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self.scope = None
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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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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = RobertaConfig(
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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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type_vocab_size=self.type_vocab_size,
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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, token_type_ids, input_mask, sequence_labels, token_labels, choice_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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_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, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = RobertaModel(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, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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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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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, 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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = RobertaModel(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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token_type_ids=token_type_ids,
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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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token_type_ids=token_type_ids,
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encoder_hidden_states=encoder_hidden_states,
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)
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_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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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, 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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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model = RobertaForCausalLM(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, token_type_ids=token_type_ids, 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 create_and_check_for_masked_lm(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = RobertaForMaskedLM(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, token_type_ids=token_type_ids, 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 create_and_check_for_token_classification(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = RobertaForTokenClassification(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, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_for_multiple_choice(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_choices = self.num_choices
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model = RobertaForMultipleChoice(config=config)
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model.to(torch_device)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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result = model(
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multiple_choice_inputs_ids,
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attention_mask=multiple_choice_input_mask,
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token_type_ids=multiple_choice_token_type_ids,
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labels=choice_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def create_and_check_for_question_answering(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = RobertaForQuestionAnswering(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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token_type_ids=token_type_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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RobertaForCausalLM,
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RobertaForMaskedLM,
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RobertaModel,
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RobertaForSequenceClassification,
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RobertaForTokenClassification,
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RobertaForMultipleChoice,
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RobertaForQuestionAnswering,
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)
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if is_torch_available()
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else ()
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)
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def setUp(self):
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self.model_tester = RobertaModelTester(self)
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self.config_tester = ConfigTester(self, config_class=RobertaConfig, 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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_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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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_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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def test_for_masked_lm(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_for_masked_lm(*config_and_inputs)
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def test_for_token_classification(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_for_token_classification(*config_and_inputs)
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def test_for_multiple_choice(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_for_multiple_choice(*config_and_inputs)
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def test_for_question_answering(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_for_question_answering(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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for model_name in ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = RobertaModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_create_position_ids_respects_padding_index(self):
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"""Ensure that the default position ids only assign a sequential . This is a regression
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test for https://github.com/huggingface/transformers/issues/1761
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The position ids should be masked with the embedding object's padding index. Therefore, the
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first available non-padding position index is RobertaEmbeddings.padding_idx + 1
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"""
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config = self.model_tester.prepare_config_and_inputs()[0]
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model = RobertaEmbeddings(config=config)
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input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
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expected_positions = torch.as_tensor(
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[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
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)
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position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
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self.assertEqual(position_ids.shape, expected_positions.shape)
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self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
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def test_create_position_ids_from_inputs_embeds(self):
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"""Ensure that the default position ids only assign a sequential . This is a regression
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test for https://github.com/huggingface/transformers/issues/1761
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The position ids should be masked with the embedding object's padding index. Therefore, the
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first available non-padding position index is RobertaEmbeddings.padding_idx + 1
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"""
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config = self.model_tester.prepare_config_and_inputs()[0]
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embeddings = RobertaEmbeddings(config=config)
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inputs_embeds = torch.Tensor(2, 4, 30)
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expected_single_positions = [
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0 + embeddings.padding_idx + 1,
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1 + embeddings.padding_idx + 1,
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2 + embeddings.padding_idx + 1,
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3 + embeddings.padding_idx + 1,
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]
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expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
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position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
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self.assertEqual(position_ids.shape, expected_positions.shape)
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self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
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@require_sentencepiece
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@require_tokenizers
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class RobertaModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_masked_lm(self):
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model = RobertaForMaskedLM.from_pretrained("roberta-base")
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input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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expected_shape = torch.Size((1, 11, 50265))
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self.assertEqual(output.shape, expected_shape)
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# compare the actual values for a slice.
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expected_slice = torch.tensor(
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[[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
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)
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# roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
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# roberta.eval()
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# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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@slow
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def test_inference_no_head(self):
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model = RobertaModel.from_pretrained("roberta-base")
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input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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# compare the actual values for a slice.
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expected_slice = torch.tensor(
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[[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]]
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)
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# roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
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# roberta.eval()
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# expected_slice = roberta.extract_features(input_ids)[:, :3, :3].detach()
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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@slow
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def test_inference_classification_head(self):
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model = RobertaForSequenceClassification.from_pretrained("roberta-large-mnli")
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input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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expected_shape = torch.Size((1, 3))
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self.assertEqual(output.shape, expected_shape)
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expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]])
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# roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
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# roberta.eval()
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# expected_tensor = roberta.predict("mnli", input_ids, return_logits=True).detach()
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self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))
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