745 lines
28 KiB
Python
745 lines
28 KiB
Python
# coding=utf-8
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# Copyright 2022 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 RoCBert model."""
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import unittest
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from transformers import RoCBertConfig, is_torch_available
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from transformers.models.auto import get_values
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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, floats_tensor, 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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MODEL_FOR_PRETRAINING_MAPPING,
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RoCBertForCausalLM,
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RoCBertForMaskedLM,
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RoCBertForMultipleChoice,
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RoCBertForPreTraining,
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RoCBertForQuestionAnswering,
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RoCBertForSequenceClassification,
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RoCBertForTokenClassification,
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RoCBertModel,
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)
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class RoCBertModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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pronunciation_vocab_size=99,
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shape_vocab_size=99,
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pronunciation_embed_dim=32,
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shape_embed_dim=32,
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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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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.pronunciation_vocab_size = pronunciation_vocab_size
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self.shape_vocab_size = shape_vocab_size
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self.pronunciation_embed_dim = pronunciation_embed_dim
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self.shape_embed_dim = shape_embed_dim
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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.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_shape_ids = ids_tensor([self.batch_size, self.seq_length], self.shape_vocab_size)
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input_pronunciation_ids = ids_tensor([self.batch_size, self.seq_length], self.pronunciation_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 = self.get_config()
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return (
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config,
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input_ids,
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input_shape_ids,
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input_pronunciation_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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)
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def get_config(self):
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return RoCBertConfig(
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vocab_size=self.vocab_size,
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shape_vocab_size=self.shape_vocab_size,
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pronunciation_vocab_size=self.pronunciation_vocab_size,
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shape_embed_dim=self.shape_embed_dim,
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pronunciation_embed_dim=self.pronunciation_embed_dim,
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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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)
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def prepare_config_and_inputs_for_decoder(self):
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(
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config,
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input_ids,
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input_shape_ids,
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input_pronunciation_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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input_shape_ids,
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input_pronunciation_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,
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config,
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input_ids,
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input_shape_ids,
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input_pronunciation_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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):
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model = RoCBertModel(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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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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)
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result = model(
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input_ids,
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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_ids,
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token_type_ids=token_type_ids,
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)
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result = model(input_ids, input_shape_ids=input_shape_ids, input_pronunciation_ids=input_pronunciation_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_model_as_decoder(
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self,
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config,
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input_ids,
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input_shape_ids,
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input_pronunciation_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 = RoCBertModel(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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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_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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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_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(
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input_ids,
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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_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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def create_and_check_for_causal_lm(
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self,
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config,
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input_ids,
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input_shape_ids,
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input_pronunciation_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 = RoCBertForCausalLM(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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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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labels=token_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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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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input_shape_ids,
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input_pronunciation_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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):
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model = RoCBertForMaskedLM(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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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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labels=token_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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def create_and_check_decoder_model_past_large_inputs(
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self,
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config,
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input_ids,
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input_shape_ids,
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input_pronunciation_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.is_decoder = True
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config.add_cross_attention = True
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model = RoCBertForCausalLM(config=config)
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model.to(torch_device)
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model.eval()
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# first forward pass
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outputs = model(
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input_ids,
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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_ids,
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attention_mask=input_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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use_cache=True,
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)
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past_key_values = outputs.past_key_values
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_shape_tokens = ids_tensor((self.batch_size, 3), config.shape_vocab_size)
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next_pronunciation_tokens = ids_tensor((self.batch_size, 3), config.pronunciation_vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_input_shape_ids = torch.cat([input_shape_ids, next_shape_tokens], dim=-1)
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next_input_pronunciation_ids = torch.cat([input_pronunciation_ids, next_pronunciation_tokens], dim=-1)
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next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
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output_from_no_past = model(
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next_input_ids,
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input_shape_ids=next_input_shape_ids,
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input_pronunciation_ids=next_input_pronunciation_ids,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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output_hidden_states=True,
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)["hidden_states"][0]
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output_from_past = model(
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next_tokens,
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input_shape_ids=next_shape_tokens,
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input_pronunciation_ids=next_pronunciation_tokens,
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attention_mask=next_attention_mask,
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encoder_hidden_states=encoder_hidden_states,
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encoder_attention_mask=encoder_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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)["hidden_states"][0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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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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input_shape_ids,
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input_pronunciation_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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):
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model = RoCBertForQuestionAnswering(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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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_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 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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input_shape_ids,
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input_pronunciation_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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):
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config.num_labels = self.num_labels
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model = RoCBertForSequenceClassification(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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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_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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input_shape_ids,
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input_pronunciation_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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):
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config.num_labels = self.num_labels
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model = RoCBertForTokenClassification(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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input_shape_ids=input_shape_ids,
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input_pronunciation_ids=input_pronunciation_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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labels=token_labels,
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)
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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,
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config,
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input_ids,
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input_shape_ids,
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input_pronunciation_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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):
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config.num_choices = self.num_choices
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model = RoCBertForMultipleChoice(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_inputs_shape_ids = input_shape_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_inputs_pronunciation_ids = (
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input_pronunciation_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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)
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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()
|
|
result = model(
|
|
multiple_choice_inputs_ids,
|
|
input_shape_ids=multiple_choice_inputs_shape_ids,
|
|
input_pronunciation_ids=multiple_choice_inputs_pronunciation_ids,
|
|
attention_mask=multiple_choice_input_mask,
|
|
token_type_ids=multiple_choice_token_type_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,
|
|
input_shape_ids,
|
|
input_pronunciation_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
) = config_and_inputs
|
|
inputs_dict = {
|
|
"input_ids": input_ids,
|
|
"input_shape_ids": input_shape_ids,
|
|
"input_pronunciation_ids": input_pronunciation_ids,
|
|
"token_type_ids": token_type_ids,
|
|
"attention_mask": input_mask,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
def create_and_check_for_pretraining(
|
|
self,
|
|
config,
|
|
input_ids,
|
|
input_shape_ids,
|
|
input_pronunciation_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
):
|
|
model = RoCBertForPreTraining(config=config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
result = model(
|
|
input_ids,
|
|
input_shape_ids,
|
|
input_pronunciation_ids,
|
|
attention_mask=input_mask,
|
|
token_type_ids=token_type_ids,
|
|
attack_input_ids=input_ids,
|
|
attack_input_shape_ids=input_shape_ids,
|
|
attack_input_pronunciation_ids=input_pronunciation_ids,
|
|
attack_attention_mask=input_mask,
|
|
attack_token_type_ids=token_type_ids,
|
|
labels_input_ids=token_labels,
|
|
labels_input_shape_ids=input_shape_ids,
|
|
labels_input_pronunciation_ids=input_pronunciation_ids,
|
|
labels_attention_mask=input_mask,
|
|
labels_token_type_ids=token_type_ids,
|
|
)
|
|
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
|
|
|
|
|
@require_torch
|
|
class RoCBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(
|
|
RoCBertModel,
|
|
RoCBertForMaskedLM,
|
|
RoCBertForCausalLM,
|
|
RoCBertForMultipleChoice,
|
|
RoCBertForQuestionAnswering,
|
|
RoCBertForSequenceClassification,
|
|
RoCBertForTokenClassification,
|
|
RoCBertForPreTraining,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
all_generative_model_classes = (RoCBertForCausalLM,) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": RoCBertModel,
|
|
"fill-mask": RoCBertForMaskedLM,
|
|
"question-answering": RoCBertForQuestionAnswering,
|
|
"text-classification": RoCBertForSequenceClassification,
|
|
"text-generation": RoCBertForCausalLM,
|
|
"token-classification": RoCBertForTokenClassification,
|
|
"zero-shot": RoCBertForSequenceClassification,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
|
|
# TODO: Fix the failed tests when this model gets more usage
|
|
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 [
|
|
"FillMaskPipelineTests",
|
|
"FeatureExtractionPipelineTests",
|
|
"TextClassificationPipelineTests",
|
|
"TokenClassificationPipelineTests",
|
|
]:
|
|
# Get error: IndexError: index out of range in self.
|
|
# `word_shape_file` and `word_pronunciation_file` should be shrunk during tiny model creation,
|
|
# otherwise `IndexError` could occur in some embedding layers. Skip for now until this model has
|
|
# more usage.
|
|
return True
|
|
|
|
return False
|
|
|
|
# special case for ForPreTraining model
|
|
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
|
|
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
|
|
|
|
if return_labels:
|
|
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
|
|
inputs_dict["labels_input_ids"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
|
|
)
|
|
inputs_dict["labels_input_shape_ids"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
|
|
)
|
|
inputs_dict["labels_input_pronunciation_ids"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
|
|
)
|
|
inputs_dict["attack_input_ids"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
|
|
)
|
|
inputs_dict["attack_input_shape_ids"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
|
|
)
|
|
inputs_dict["attack_input_pronunciation_ids"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
|
|
)
|
|
return inputs_dict
|
|
|
|
def setUp(self):
|
|
self.model_tester = RoCBertModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=RoCBertConfig, 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_various_embeddings(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
for type in ["absolute", "relative_key", "relative_key_query"]:
|
|
config_and_inputs[0].position_embedding_type = type
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
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_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_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_decoder_model_past_with_large_inputs_relative_pos_emb(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
|
config_and_inputs[0].position_embedding_type = "relative_key"
|
|
self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_pretraining(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
|
|
|
|
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_model_as_decoder_with_default_input_mask(self):
|
|
# This regression test was failing with PyTorch < 1.3
|
|
(
|
|
config,
|
|
input_ids,
|
|
input_shape_ids,
|
|
input_pronunciation_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_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_shape_ids,
|
|
input_pronunciation_ids,
|
|
token_type_ids,
|
|
input_mask,
|
|
sequence_labels,
|
|
token_labels,
|
|
choice_labels,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "weiweishi/roc-bert-base-zh"
|
|
model = RoCBertModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class RoCBertModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_masked_lm(self):
|
|
model = RoCBertForMaskedLM.from_pretrained("weiweishi/roc-bert-base-zh")
|
|
|
|
# input_text: ['[CLS]', 'b', 'a', '里', '系', '[MASK]', '国', '的', '首', '都', '[SEP]'] is the adversarial text
|
|
# of ['[CLS]', '巴', '黎', '是', '[MASK]', '国', '的', '首', '都', '[SEP]'], means
|
|
# "Paris is the [MASK] of France" in English
|
|
input_ids = torch.tensor([[101, 144, 143, 7027, 5143, 103, 1744, 4638, 7674, 6963, 102]])
|
|
input_shape_ids = torch.tensor([[2, 20324, 23690, 8740, 706, 1, 10900, 23343, 20205, 5850, 2]])
|
|
input_pronunciation_ids = torch.tensor([[2, 718, 397, 52, 61, 1, 168, 273, 180, 243, 2]])
|
|
|
|
output = model(input_ids, input_shape_ids, input_pronunciation_ids)
|
|
output_ids = torch.argmax(output.logits, dim=2)
|
|
|
|
# convert to tokens is: ['[CLS]', '巴', '*', '黎', '是', '法', '国', '的', '首', '都', '[SEP]']
|
|
expected_output = torch.tensor([[101, 2349, 115, 7944, 3221, 3791, 1744, 4638, 7674, 6963, 102]])
|
|
|
|
assert torch.allclose(output_ids, expected_output)
|