508 lines
20 KiB
Python
508 lines
20 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 Splinter model."""
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import copy
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 SplinterConfig, SplinterForPreTraining, SplinterForQuestionAnswering, SplinterModel
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class SplinterModelTester:
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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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num_questions=3,
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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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hidden_size=32,
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question_token_id=1,
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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.num_questions = num_questions
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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.hidden_size = hidden_size
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self.question_token_id = question_token_id
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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_ids[:, 1] = self.question_token_id
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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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start_positions = None
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end_positions = None
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question_positions = None
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if self.use_labels:
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start_positions = ids_tensor([self.batch_size, self.num_questions], self.type_sequence_label_size)
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end_positions = ids_tensor([self.batch_size, self.num_questions], self.type_sequence_label_size)
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question_positions = ids_tensor([self.batch_size, self.num_questions], self.num_labels)
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config = SplinterConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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question_token_id=self.question_token_id,
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)
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return (config, input_ids, token_type_ids, input_mask, start_positions, end_positions, question_positions)
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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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token_type_ids,
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input_mask,
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start_positions,
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end_positions,
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question_positions,
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):
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model = SplinterModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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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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token_type_ids,
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input_mask,
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start_positions,
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end_positions,
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question_positions,
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):
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model = SplinterForQuestionAnswering(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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start_positions=start_positions[:, 0],
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end_positions=end_positions[:, 0],
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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_pretraining(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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start_positions,
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end_positions,
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question_positions,
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):
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model = SplinterForPreTraining(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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start_positions=start_positions,
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end_positions=end_positions,
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question_positions=question_positions,
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)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.num_questions, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.num_questions, self.seq_length))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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start_positions,
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end_positions,
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question_positions,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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}
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return config, inputs_dict
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@require_torch
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class SplinterModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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SplinterModel,
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SplinterForQuestionAnswering,
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SplinterForPreTraining,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{"feature-extraction": SplinterModel, "question-answering": SplinterForQuestionAnswering}
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if is_torch_available()
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else {}
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)
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# TODO: Fix the failed tests when this model gets more usage
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "QAPipelineTests":
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return True
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elif pipeline_test_casse_name == "FeatureExtractionPipelineTests" and tokenizer_name.endswith("Fast"):
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return True
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return False
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = copy.deepcopy(inputs_dict)
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if return_labels:
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if issubclass(model_class, SplinterForPreTraining):
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inputs_dict["start_positions"] = torch.zeros(
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self.model_tester.batch_size,
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self.model_tester.num_questions,
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["end_positions"] = torch.zeros(
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self.model_tester.batch_size,
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self.model_tester.num_questions,
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["question_positions"] = torch.zeros(
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self.model_tester.batch_size,
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self.model_tester.num_questions,
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dtype=torch.long,
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device=torch_device,
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)
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elif issubclass(model_class, SplinterForQuestionAnswering):
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inputs_dict["start_positions"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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inputs_dict["end_positions"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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return inputs_dict
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def setUp(self):
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self.model_tester = SplinterModelTester(self)
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self.config_tester = ConfigTester(self, config_class=SplinterConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_question_answering(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
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def test_for_pretraining(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
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def test_inputs_embeds(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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if not self.is_encoder_decoder:
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input_ids = inputs["input_ids"]
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del inputs["input_ids"]
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else:
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encoder_input_ids = inputs["input_ids"]
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decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
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del inputs["input_ids"]
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inputs.pop("decoder_input_ids", None)
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wte = model.get_input_embeddings()
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if not self.is_encoder_decoder:
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inputs["inputs_embeds"] = wte(input_ids)
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else:
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inputs["inputs_embeds"] = wte(encoder_input_ids)
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inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
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with torch.no_grad():
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if isinstance(model, SplinterForPreTraining):
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with self.assertRaises(TypeError):
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# question_positions must not be None.
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model(**inputs)[0]
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else:
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model(**inputs)[0]
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@slow
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def test_model_from_pretrained(self):
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model_name = "tau/splinter-base"
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model = SplinterModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# overwrite from common since `SplinterForPreTraining` could contain different number of question tokens in inputs.
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# When the batch is distributed to multiple devices, each replica could get different values for the maximal number
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# of question tokens (see `SplinterForPreTraining._prepare_question_positions()`), and the model returns different
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# shape along dimension 1 (i.e. `num_questions`) that could not be combined into a single tensor as an output.
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@require_torch_multi_gpu
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def test_multi_gpu_data_parallel_forward(self):
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from torch import nn
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# some params shouldn't be scattered by nn.DataParallel
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# so just remove them if they are present.
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blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"]
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for k in blacklist_non_batched_params:
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inputs_dict.pop(k, None)
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# move input tensors to cuda:O
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for k, v in inputs_dict.items():
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if torch.is_tensor(v):
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inputs_dict[k] = v.to(0)
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for model_class in self.all_model_classes:
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# Skip this case since it will fail sometimes, as described above.
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if model_class == SplinterForPreTraining:
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continue
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model = model_class(config=config)
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model.to(0)
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model.eval()
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# Wrap model in nn.DataParallel
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model = nn.DataParallel(model)
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with torch.no_grad():
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_ = model(**self._prepare_for_class(inputs_dict, model_class))
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@require_torch
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class SplinterModelIntegrationTest(unittest.TestCase):
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@slow
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def test_splinter_question_answering(self):
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model = SplinterForQuestionAnswering.from_pretrained("tau/splinter-base-qass")
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# Input: "[CLS] Brad was born in [QUESTION] . He returned to the United Kingdom later . [SEP]"
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# Output should be the span "the United Kingdom"
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input_ids = torch.tensor(
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[[101, 7796, 1108, 1255, 1107, 104, 119, 1124, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102]]
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)
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output = model(input_ids)
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expected_shape = torch.Size((1, 16))
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self.assertEqual(output.start_logits.shape, expected_shape)
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self.assertEqual(output.end_logits.shape, expected_shape)
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self.assertEqual(torch.argmax(output.start_logits), 10)
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self.assertEqual(torch.argmax(output.end_logits), 12)
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@slow
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def test_splinter_pretraining(self):
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model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
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# Input: "[CLS] [QUESTION] was born in [QUESTION] . Brad returned to the United Kingdom later . [SEP]"
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# Output should be the spans "Brad" and "the United Kingdom"
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input_ids = torch.tensor(
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[[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102]]
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)
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question_positions = torch.tensor([[1, 5]], dtype=torch.long)
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output = model(input_ids, question_positions=question_positions)
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expected_shape = torch.Size((1, 2, 16))
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self.assertEqual(output.start_logits.shape, expected_shape)
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self.assertEqual(output.end_logits.shape, expected_shape)
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self.assertEqual(torch.argmax(output.start_logits[0, 0]), 7)
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self.assertEqual(torch.argmax(output.end_logits[0, 0]), 7)
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self.assertEqual(torch.argmax(output.start_logits[0, 1]), 10)
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self.assertEqual(torch.argmax(output.end_logits[0, 1]), 12)
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@slow
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def test_splinter_pretraining_loss_requires_question_positions(self):
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model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
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# Input: "[CLS] [QUESTION] was born in [QUESTION] . Brad returned to the United Kingdom later . [SEP]"
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# Output should be the spans "Brad" and "the United Kingdom"
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input_ids = torch.tensor(
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[[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102]]
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)
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start_positions = torch.tensor([[7, 10]], dtype=torch.long)
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end_positions = torch.tensor([7, 12], dtype=torch.long)
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with self.assertRaises(TypeError):
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model(
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input_ids,
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start_positions=start_positions,
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end_positions=end_positions,
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)
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@slow
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def test_splinter_pretraining_loss(self):
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model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
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# Input: "[CLS] [QUESTION] was born in [QUESTION] . Brad returned to the United Kingdom later . [SEP]"
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# Output should be the spans "Brad" and "the United Kingdom"
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input_ids = torch.tensor(
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[
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[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102],
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[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102],
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]
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)
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start_positions = torch.tensor([[7, 10], [7, 10]], dtype=torch.long)
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end_positions = torch.tensor([[7, 12], [7, 12]], dtype=torch.long)
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question_positions = torch.tensor([[1, 5], [1, 5]], dtype=torch.long)
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output = model(
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input_ids,
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start_positions=start_positions,
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end_positions=end_positions,
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question_positions=question_positions,
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)
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self.assertAlmostEqual(output.loss.item(), 0.0024, 4)
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@slow
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def test_splinter_pretraining_loss_with_padding(self):
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model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
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# Input: "[CLS] [QUESTION] was born in [QUESTION] . Brad returned to the United Kingdom later . [SEP]"
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# Output should be the spans "Brad" and "the United Kingdom"
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input_ids = torch.tensor(
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[
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[101, 104, 1108, 1255, 1107, 104, 119, 7796, 1608, 1106, 1103, 1244, 2325, 1224, 119, 102],
|
|
]
|
|
)
|
|
start_positions = torch.tensor([[7, 10]], dtype=torch.long)
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|
end_positions = torch.tensor([7, 12], dtype=torch.long)
|
|
question_positions = torch.tensor([[1, 5]], dtype=torch.long)
|
|
start_positions_with_padding = torch.tensor([[7, 10, 0]], dtype=torch.long)
|
|
end_positions_with_padding = torch.tensor([7, 12, 0], dtype=torch.long)
|
|
question_positions_with_padding = torch.tensor([[1, 5, 0]], dtype=torch.long)
|
|
output = model(
|
|
input_ids,
|
|
start_positions=start_positions,
|
|
end_positions=end_positions,
|
|
question_positions=question_positions,
|
|
)
|
|
output_with_padding = model(
|
|
input_ids,
|
|
start_positions=start_positions_with_padding,
|
|
end_positions=end_positions_with_padding,
|
|
question_positions=question_positions_with_padding,
|
|
)
|
|
|
|
self.assertAlmostEqual(output.loss.item(), output_with_padding.loss.item(), 4)
|
|
|
|
# Note that the original code uses 0 to denote padded question tokens
|
|
# and their start and end positions. As the pad_token_id of the model's
|
|
# config is used for the losse's ignore_index in SplinterForPreTraining,
|
|
# we add this test to ensure anybody making changes to the default
|
|
# value of the config, will be aware of the implication.
|
|
self.assertEqual(model.config.pad_token_id, 0)
|
|
|
|
@slow
|
|
def test_splinter_pretraining_prepare_question_positions(self):
|
|
model = SplinterForPreTraining.from_pretrained("tau/splinter-base-qass")
|
|
|
|
input_ids = torch.tensor(
|
|
[
|
|
[101, 104, 1, 2, 104, 3, 4, 102],
|
|
[101, 1, 104, 2, 104, 3, 104, 102],
|
|
[101, 1, 2, 104, 104, 3, 4, 102],
|
|
[101, 1, 2, 3, 4, 5, 104, 102],
|
|
]
|
|
)
|
|
question_positions = torch.tensor([[1, 4, 0], [2, 4, 6], [3, 4, 0], [6, 0, 0]], dtype=torch.long)
|
|
output_without_positions = model(input_ids)
|
|
output_with_positions = model(input_ids, question_positions=question_positions)
|
|
self.assertTrue((output_without_positions.start_logits == output_with_positions.start_logits).all())
|
|
self.assertTrue((output_without_positions.end_logits == output_with_positions.end_logits).all())
|