948 lines
36 KiB
Python
948 lines
36 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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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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"""Testing suite for the PyTorch LUKE model."""
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import unittest
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from transformers import LukeConfig, 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_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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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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LukeForEntityClassification,
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LukeForEntityPairClassification,
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LukeForEntitySpanClassification,
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LukeForMaskedLM,
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LukeForMultipleChoice,
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LukeForQuestionAnswering,
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LukeForSequenceClassification,
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LukeForTokenClassification,
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LukeModel,
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LukeTokenizer,
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)
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class LukeModelTester:
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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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entity_length=3,
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mention_length=5,
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use_attention_mask=True,
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use_token_type_ids=True,
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use_entity_ids=True,
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use_entity_attention_mask=True,
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use_entity_token_type_ids=True,
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use_entity_position_ids=True,
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use_labels=True,
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vocab_size=99,
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entity_vocab_size=10,
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entity_emb_size=6,
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hidden_size=32,
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num_hidden_layers=2,
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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=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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num_entity_classification_labels=9,
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num_entity_pair_classification_labels=6,
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num_entity_span_classification_labels=4,
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use_entity_aware_attention=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.entity_length = entity_length
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self.mention_length = mention_length
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self.use_attention_mask = use_attention_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_entity_ids = use_entity_ids
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self.use_entity_attention_mask = use_entity_attention_mask
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self.use_entity_token_type_ids = use_entity_token_type_ids
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self.use_entity_position_ids = use_entity_position_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.entity_vocab_size = entity_vocab_size
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self.entity_emb_size = entity_emb_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.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.num_entity_classification_labels = num_entity_classification_labels
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self.num_entity_pair_classification_labels = num_entity_pair_classification_labels
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self.num_entity_span_classification_labels = num_entity_span_classification_labels
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self.scope = scope
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self.use_entity_aware_attention = use_entity_aware_attention
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self.encoder_seq_length = seq_length
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self.key_length = seq_length
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self.num_hidden_states_types = 2 # hidden_states and entity_hidden_states
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def prepare_config_and_inputs(self):
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# prepare words
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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attention_mask = None
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if self.use_attention_mask:
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attention_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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# prepare entities
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entity_ids = ids_tensor([self.batch_size, self.entity_length], self.entity_vocab_size)
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entity_attention_mask = None
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if self.use_entity_attention_mask:
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entity_attention_mask = random_attention_mask([self.batch_size, self.entity_length])
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entity_token_type_ids = None
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if self.use_token_type_ids:
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entity_token_type_ids = ids_tensor([self.batch_size, self.entity_length], self.type_vocab_size)
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entity_position_ids = None
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if self.use_entity_position_ids:
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entity_position_ids = ids_tensor(
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[self.batch_size, self.entity_length, self.mention_length], self.mention_length
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)
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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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entity_labels = None
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entity_classification_labels = None
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entity_pair_classification_labels = None
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entity_span_classification_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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entity_labels = ids_tensor([self.batch_size, self.entity_length], self.entity_vocab_size)
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entity_classification_labels = ids_tensor([self.batch_size], self.num_entity_classification_labels)
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entity_pair_classification_labels = ids_tensor(
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[self.batch_size], self.num_entity_pair_classification_labels
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)
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entity_span_classification_labels = ids_tensor(
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[self.batch_size, self.entity_length], self.num_entity_span_classification_labels
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)
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config = self.get_config()
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return (
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config,
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input_ids,
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attention_mask,
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token_type_ids,
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entity_ids,
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entity_attention_mask,
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entity_token_type_ids,
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entity_position_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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entity_labels,
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entity_classification_labels,
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entity_pair_classification_labels,
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entity_span_classification_labels,
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)
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def get_config(self):
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return LukeConfig(
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vocab_size=self.vocab_size,
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entity_vocab_size=self.entity_vocab_size,
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entity_emb_size=self.entity_emb_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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is_decoder=False,
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initializer_range=self.initializer_range,
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use_entity_aware_attention=self.use_entity_aware_attention,
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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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attention_mask,
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token_type_ids,
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entity_ids,
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entity_attention_mask,
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entity_token_type_ids,
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entity_position_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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entity_labels,
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entity_classification_labels,
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entity_pair_classification_labels,
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entity_span_classification_labels,
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):
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model = LukeModel(config=config)
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model.to(torch_device)
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model.eval()
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# test with words + entities
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result = model(
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input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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entity_ids=entity_ids,
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entity_attention_mask=entity_attention_mask,
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entity_token_type_ids=entity_token_type_ids,
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entity_position_ids=entity_position_ids,
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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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self.parent.assertEqual(
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result.entity_last_hidden_state.shape, (self.batch_size, self.entity_length, self.hidden_size)
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)
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# test with words only
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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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def create_and_check_for_masked_lm(
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self,
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config,
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input_ids,
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attention_mask,
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token_type_ids,
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entity_ids,
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entity_attention_mask,
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entity_token_type_ids,
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entity_position_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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entity_labels,
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entity_classification_labels,
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entity_pair_classification_labels,
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entity_span_classification_labels,
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):
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config.num_labels = self.num_entity_classification_labels
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model = LukeForMaskedLM(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=attention_mask,
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token_type_ids=token_type_ids,
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entity_ids=entity_ids,
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entity_attention_mask=entity_attention_mask,
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entity_token_type_ids=entity_token_type_ids,
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entity_position_ids=entity_position_ids,
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labels=token_labels,
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entity_labels=entity_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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if entity_ids is not None:
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self.parent.assertEqual(
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result.entity_logits.shape, (self.batch_size, self.entity_length, self.entity_vocab_size)
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)
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else:
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self.parent.assertIsNone(result.entity_logits)
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def create_and_check_for_entity_classification(
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self,
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config,
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input_ids,
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attention_mask,
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token_type_ids,
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entity_ids,
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entity_attention_mask,
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entity_token_type_ids,
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entity_position_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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entity_labels,
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entity_classification_labels,
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entity_pair_classification_labels,
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entity_span_classification_labels,
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):
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config.num_labels = self.num_entity_classification_labels
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model = LukeForEntityClassification(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=attention_mask,
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token_type_ids=token_type_ids,
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entity_ids=entity_ids,
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entity_attention_mask=entity_attention_mask,
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entity_token_type_ids=entity_token_type_ids,
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entity_position_ids=entity_position_ids,
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labels=entity_classification_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_entity_classification_labels))
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def create_and_check_for_entity_pair_classification(
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self,
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config,
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input_ids,
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attention_mask,
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token_type_ids,
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entity_ids,
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entity_attention_mask,
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entity_token_type_ids,
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entity_position_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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entity_labels,
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entity_classification_labels,
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entity_pair_classification_labels,
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entity_span_classification_labels,
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):
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config.num_labels = self.num_entity_pair_classification_labels
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model = LukeForEntityClassification(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=attention_mask,
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token_type_ids=token_type_ids,
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entity_ids=entity_ids,
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entity_attention_mask=entity_attention_mask,
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entity_token_type_ids=entity_token_type_ids,
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entity_position_ids=entity_position_ids,
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labels=entity_pair_classification_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_entity_pair_classification_labels))
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def create_and_check_for_entity_span_classification(
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self,
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config,
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input_ids,
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attention_mask,
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token_type_ids,
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entity_ids,
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entity_attention_mask,
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entity_token_type_ids,
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entity_position_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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entity_labels,
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entity_classification_labels,
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entity_pair_classification_labels,
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entity_span_classification_labels,
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):
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config.num_labels = self.num_entity_span_classification_labels
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model = LukeForEntitySpanClassification(config)
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model.to(torch_device)
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model.eval()
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entity_start_positions = ids_tensor([self.batch_size, self.entity_length], self.seq_length)
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entity_end_positions = ids_tensor([self.batch_size, self.entity_length], self.seq_length)
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result = model(
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input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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entity_ids=entity_ids,
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entity_attention_mask=entity_attention_mask,
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entity_token_type_ids=entity_token_type_ids,
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entity_position_ids=entity_position_ids,
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entity_start_positions=entity_start_positions,
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entity_end_positions=entity_end_positions,
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labels=entity_span_classification_labels,
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)
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self.parent.assertEqual(
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result.logits.shape, (self.batch_size, self.entity_length, self.num_entity_span_classification_labels)
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)
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def create_and_check_for_question_answering(
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self,
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config,
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input_ids,
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attention_mask,
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token_type_ids,
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entity_ids,
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entity_attention_mask,
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entity_token_type_ids,
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entity_position_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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entity_labels,
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entity_classification_labels,
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entity_pair_classification_labels,
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entity_span_classification_labels,
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):
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model = LukeForQuestionAnswering(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=attention_mask,
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token_type_ids=token_type_ids,
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entity_ids=entity_ids,
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entity_attention_mask=entity_attention_mask,
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entity_token_type_ids=entity_token_type_ids,
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entity_position_ids=entity_position_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 create_and_check_for_sequence_classification(
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self,
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|
config,
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input_ids,
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|
attention_mask,
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token_type_ids,
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entity_ids,
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|
entity_attention_mask,
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|
entity_token_type_ids,
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entity_position_ids,
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|
sequence_labels,
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|
token_labels,
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|
choice_labels,
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|
entity_labels,
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entity_classification_labels,
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entity_pair_classification_labels,
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entity_span_classification_labels,
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):
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config.num_labels = self.num_labels
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model = LukeForSequenceClassification(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=attention_mask,
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token_type_ids=token_type_ids,
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entity_ids=entity_ids,
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entity_attention_mask=entity_attention_mask,
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entity_token_type_ids=entity_token_type_ids,
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entity_position_ids=entity_position_ids,
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labels=sequence_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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|
def create_and_check_for_token_classification(
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|
self,
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|
config,
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|
input_ids,
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|
attention_mask,
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token_type_ids,
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entity_ids,
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entity_attention_mask,
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entity_token_type_ids,
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entity_position_ids,
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sequence_labels,
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token_labels,
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choice_labels,
|
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entity_labels,
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entity_classification_labels,
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entity_pair_classification_labels,
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entity_span_classification_labels,
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):
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config.num_labels = self.num_labels
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model = LukeForTokenClassification(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=attention_mask,
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token_type_ids=token_type_ids,
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entity_ids=entity_ids,
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entity_attention_mask=entity_attention_mask,
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entity_token_type_ids=entity_token_type_ids,
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entity_position_ids=entity_position_ids,
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|
labels=token_labels,
|
|
)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
|
|
|
|
def create_and_check_for_multiple_choice(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
token_type_ids,
|
|
entity_ids,
|
|
entity_attention_mask,
|
|
entity_token_type_ids,
|
|
entity_position_ids,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
entity_labels,
|
|
entity_classification_labels,
|
|
entity_pair_classification_labels,
|
|
entity_span_classification_labels,
|
|
):
|
|
config.num_choices = self.num_choices
|
|
model = LukeForMultipleChoice(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
|
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
|
multiple_choice_attention_mask = attention_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
|
multiple_choice_entity_ids = entity_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
|
multiple_choice_entity_token_type_ids = (
|
|
entity_token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
|
)
|
|
multiple_choice_entity_attention_mask = (
|
|
entity_attention_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
|
|
)
|
|
multiple_choice_entity_position_ids = (
|
|
entity_position_ids.unsqueeze(1).expand(-1, self.num_choices, -1, -1).contiguous()
|
|
)
|
|
result = model(
|
|
multiple_choice_inputs_ids,
|
|
attention_mask=multiple_choice_attention_mask,
|
|
token_type_ids=multiple_choice_token_type_ids,
|
|
entity_ids=multiple_choice_entity_ids,
|
|
entity_attention_mask=multiple_choice_entity_attention_mask,
|
|
entity_token_type_ids=multiple_choice_entity_token_type_ids,
|
|
entity_position_ids=multiple_choice_entity_position_ids,
|
|
labels=choice_labels,
|
|
)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
(
|
|
config,
|
|
input_ids,
|
|
attention_mask,
|
|
token_type_ids,
|
|
entity_ids,
|
|
entity_attention_mask,
|
|
entity_token_type_ids,
|
|
entity_position_ids,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
entity_labels,
|
|
entity_classification_labels,
|
|
entity_pair_classification_labels,
|
|
entity_span_classification_labels,
|
|
) = config_and_inputs
|
|
inputs_dict = {
|
|
"input_ids": input_ids,
|
|
"token_type_ids": token_type_ids,
|
|
"attention_mask": attention_mask,
|
|
"entity_ids": entity_ids,
|
|
"entity_token_type_ids": entity_token_type_ids,
|
|
"entity_attention_mask": entity_attention_mask,
|
|
"entity_position_ids": entity_position_ids,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class LukeModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(
|
|
LukeModel,
|
|
LukeForMaskedLM,
|
|
LukeForEntityClassification,
|
|
LukeForEntityPairClassification,
|
|
LukeForEntitySpanClassification,
|
|
LukeForQuestionAnswering,
|
|
LukeForSequenceClassification,
|
|
LukeForTokenClassification,
|
|
LukeForMultipleChoice,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": LukeModel,
|
|
"fill-mask": LukeForMaskedLM,
|
|
"question-answering": LukeForQuestionAnswering,
|
|
"text-classification": LukeForSequenceClassification,
|
|
"token-classification": LukeForTokenClassification,
|
|
"zero-shot": LukeForSequenceClassification,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
test_pruning = False
|
|
test_torchscript = False
|
|
test_resize_embeddings = True
|
|
test_head_masking = True
|
|
|
|
# TODO: Fix the failed tests
|
|
def is_pipeline_test_to_skip(
|
|
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
|
|
):
|
|
if pipeline_test_casse_name in ["QAPipelineTests", "ZeroShotClassificationPipelineTests"]:
|
|
return True
|
|
|
|
return False
|
|
|
|
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
|
|
entity_inputs_dict = {k: v for k, v in inputs_dict.items() if k.startswith("entity")}
|
|
inputs_dict = {k: v for k, v in inputs_dict.items() if not k.startswith("entity")}
|
|
|
|
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
|
|
if model_class == LukeForMultipleChoice:
|
|
entity_inputs_dict = {
|
|
k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
|
|
if v.ndim == 2
|
|
else v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1, -1).contiguous()
|
|
for k, v in entity_inputs_dict.items()
|
|
}
|
|
inputs_dict.update(entity_inputs_dict)
|
|
|
|
if model_class == LukeForEntitySpanClassification:
|
|
inputs_dict["entity_start_positions"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.entity_length), dtype=torch.long, device=torch_device
|
|
)
|
|
inputs_dict["entity_end_positions"] = torch.ones(
|
|
(self.model_tester.batch_size, self.model_tester.entity_length), dtype=torch.long, device=torch_device
|
|
)
|
|
|
|
if return_labels:
|
|
if model_class in (
|
|
LukeForEntityClassification,
|
|
LukeForEntityPairClassification,
|
|
LukeForSequenceClassification,
|
|
LukeForMultipleChoice,
|
|
):
|
|
inputs_dict["labels"] = torch.zeros(
|
|
self.model_tester.batch_size, dtype=torch.long, device=torch_device
|
|
)
|
|
elif model_class == LukeForEntitySpanClassification:
|
|
inputs_dict["labels"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.entity_length),
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
)
|
|
elif model_class == LukeForTokenClassification:
|
|
inputs_dict["labels"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length),
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
)
|
|
elif model_class == LukeForMaskedLM:
|
|
inputs_dict["labels"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length),
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
)
|
|
inputs_dict["entity_labels"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.entity_length),
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
)
|
|
|
|
return inputs_dict
|
|
|
|
def setUp(self):
|
|
self.model_tester = LukeModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=LukeConfig, 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)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "studio-ousia/luke-base"
|
|
model = LukeModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
def test_for_masked_lm(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
|
|
|
|
def test_for_masked_lm_with_word_only(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
config_and_inputs = (*config_and_inputs[:4], *((None,) * len(config_and_inputs[4:])))
|
|
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
|
|
|
|
def test_for_question_answering(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
|
|
|
|
def test_for_sequence_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
|
|
|
|
def test_for_token_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
|
|
|
|
def test_for_multiple_choice(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
|
|
|
|
def test_for_entity_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_entity_classification(*config_and_inputs)
|
|
|
|
def test_for_entity_pair_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_entity_pair_classification(*config_and_inputs)
|
|
|
|
def test_for_entity_span_classification(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_entity_span_classification(*config_and_inputs)
|
|
|
|
def test_attention_outputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
seq_length = self.model_tester.seq_length
|
|
entity_length = self.model_tester.entity_length
|
|
key_length = seq_length + entity_length
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = False
|
|
config.return_dict = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
attentions = outputs.attentions
|
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
|
|
|
# check that output_attentions also work using config
|
|
del inputs_dict["output_attentions"]
|
|
config.output_attentions = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
attentions = outputs.attentions
|
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
|
|
|
self.assertListEqual(
|
|
list(attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, seq_length + entity_length, key_length],
|
|
)
|
|
out_len = len(outputs)
|
|
|
|
# Check attention is always last and order is fine
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
added_hidden_states = self.model_tester.num_hidden_states_types
|
|
self.assertEqual(out_len + added_hidden_states, len(outputs))
|
|
|
|
self_attentions = outputs.attentions
|
|
|
|
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, seq_length + entity_length, key_length],
|
|
)
|
|
|
|
def test_entity_hidden_states_output(self):
|
|
def check_hidden_states_output(inputs_dict, config, model_class):
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
entity_hidden_states = outputs.entity_hidden_states
|
|
|
|
expected_num_layers = getattr(
|
|
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
|
)
|
|
self.assertEqual(len(entity_hidden_states), expected_num_layers)
|
|
|
|
entity_length = self.model_tester.entity_length
|
|
|
|
self.assertListEqual(
|
|
list(entity_hidden_states[0].shape[-2:]),
|
|
[entity_length, self.model_tester.hidden_size],
|
|
)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
# check that output_hidden_states also work using config
|
|
del inputs_dict["output_hidden_states"]
|
|
config.output_hidden_states = True
|
|
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
def test_retain_grad_entity_hidden_states(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.output_hidden_states = True
|
|
config.output_attentions = True
|
|
|
|
# no need to test all models as different heads yield the same functionality
|
|
model_class = self.all_model_classes[0]
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
outputs = model(**inputs)
|
|
|
|
output = outputs[0]
|
|
|
|
entity_hidden_states = outputs.entity_hidden_states[0]
|
|
entity_hidden_states.retain_grad()
|
|
|
|
output.flatten()[0].backward(retain_graph=True)
|
|
|
|
self.assertIsNotNone(entity_hidden_states.grad)
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
|
|
@require_torch
|
|
class LukeModelIntegrationTests(unittest.TestCase):
|
|
@slow
|
|
def test_inference_base_model(self):
|
|
model = LukeModel.from_pretrained("studio-ousia/luke-base").eval()
|
|
model.to(torch_device)
|
|
|
|
tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base", task="entity_classification")
|
|
text = (
|
|
"Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped"
|
|
" the new world number one avoid a humiliating second- round exit at Wimbledon ."
|
|
)
|
|
span = (39, 42)
|
|
encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt")
|
|
|
|
# move all values to device
|
|
for key, value in encoding.items():
|
|
encoding[key] = encoding[key].to(torch_device)
|
|
|
|
outputs = model(**encoding)
|
|
|
|
# Verify word hidden states
|
|
expected_shape = torch.Size((1, 42, 768))
|
|
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]
|
|
).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
# Verify entity hidden states
|
|
expected_shape = torch.Size((1, 1, 768))
|
|
self.assertEqual(outputs.entity_last_hidden_state.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([[0.1457, 0.1044, 0.0174]]).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_large_model(self):
|
|
model = LukeModel.from_pretrained("studio-ousia/luke-large").eval()
|
|
model.to(torch_device)
|
|
|
|
tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-large", task="entity_classification")
|
|
text = (
|
|
"Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped"
|
|
" the new world number one avoid a humiliating second- round exit at Wimbledon ."
|
|
)
|
|
span = (39, 42)
|
|
encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt")
|
|
|
|
# move all values to device
|
|
for key, value in encoding.items():
|
|
encoding[key] = encoding[key].to(torch_device)
|
|
|
|
outputs = model(**encoding)
|
|
|
|
# Verify word hidden states
|
|
expected_shape = torch.Size((1, 42, 1024))
|
|
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]
|
|
).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
# Verify entity hidden states
|
|
expected_shape = torch.Size((1, 1, 1024))
|
|
self.assertEqual(outputs.entity_last_hidden_state.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([[0.0466, -0.0106, -0.0179]]).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
|