402 lines
15 KiB
Python
402 lines
15 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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import unittest
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from transformers import RoFormerConfig, is_tf_available
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from transformers.testing_utils import require_tf, slow
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from .test_configuration_common import ConfigTester
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from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
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if is_tf_available():
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import tensorflow as tf
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from transformers import (
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TFRoFormerForCausalLM,
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TFRoFormerForMaskedLM,
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TFRoFormerForMultipleChoice,
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TFRoFormerForQuestionAnswering,
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TFRoFormerForSequenceClassification,
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TFRoFormerForTokenClassification,
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TFRoFormerModel,
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)
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from transformers.models.roformer.modeling_tf_roformer import (
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TFRoFormerSelfAttention,
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TFRoFormerSinusoidalPositionalEmbedding,
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)
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class TFRoFormerModelTester:
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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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hidden_size=32,
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num_hidden_layers=5,
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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 = 13
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self.seq_length = 7
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self.is_training = True
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self.use_input_mask = True
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self.use_token_type_ids = True
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self.use_labels = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.num_hidden_layers = 5
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self.num_attention_heads = 4
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self.intermediate_size = 37
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 3
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self.num_choices = 4
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self.scope = None
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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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 = RoFormerConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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return_dict=True,
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)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFRoFormerModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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inputs = [input_ids, input_mask]
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result = model(inputs)
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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_lm_head(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.is_decoder = True
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model = TFRoFormerForCausalLM(config=config)
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inputs = {
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"input_ids": input_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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prediction_scores = model(inputs)["logits"]
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self.parent.assertListEqual(
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list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
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)
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def create_and_check_for_masked_lm(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFRoFormerForMaskedLM(config=config)
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inputs = {
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"input_ids": input_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(inputs)
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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_sequence_classification(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = TFRoFormerForSequenceClassification(config=config)
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inputs = {
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"input_ids": input_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(inputs)
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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_multiple_choice(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_choices = self.num_choices
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model = TFRoFormerForMultipleChoice(config=config)
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multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
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multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
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multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
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inputs = {
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"input_ids": multiple_choice_inputs_ids,
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"attention_mask": multiple_choice_input_mask,
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"token_type_ids": multiple_choice_token_type_ids,
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}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def create_and_check_for_token_classification(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = TFRoFormerForTokenClassification(config=config)
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inputs = {
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"input_ids": input_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(inputs)
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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_question_answering(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFRoFormerForQuestionAnswering(config=config)
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inputs = {
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"input_ids": input_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(inputs)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_tf
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class TFRoFormerModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TFRoFormerModel,
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TFRoFormerForCausalLM,
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TFRoFormerForMaskedLM,
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TFRoFormerForQuestionAnswering,
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TFRoFormerForSequenceClassification,
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TFRoFormerForTokenClassification,
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TFRoFormerForMultipleChoice,
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)
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if is_tf_available()
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else ()
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)
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test_head_masking = False
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test_onnx = False
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def setUp(self):
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self.model_tester = TFRoFormerModelTester(self)
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self.config_tester = ConfigTester(self, config_class=RoFormerConfig, 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_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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def test_for_causal_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_lm_head(*config_and_inputs)
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def test_for_multiple_choice(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
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def test_for_question_answering(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
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def test_for_sequence_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base")
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self.assertIsNotNone(model)
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@require_tf
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class TFRoFormerModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_masked_lm(self):
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model = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
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input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
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output = model(input_ids)[0]
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# TODO Replace vocab size
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vocab_size = 50000
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expected_shape = [1, 6, vocab_size]
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self.assertEqual(output.shape, expected_shape)
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print(output[:, :3, :3])
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# TODO Replace values below with what was printed above.
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expected_slice = tf.constant(
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[
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[
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[-0.12053341, -1.0264901, 0.29221946],
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[-1.5133783, 0.197433, 0.15190607],
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[-5.0135403, -3.900256, -0.84038764],
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]
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]
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)
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tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
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@require_tf
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class TFRoFormerSinusoidalPositionalEmbeddingTest(unittest.TestCase):
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tolerance = 1e-4
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def test_basic(self):
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input_ids = tf.constant([[4, 10]])
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emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6)
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emb = emb1(input_ids.shape)
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desired_weights = tf.constant(
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[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]
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)
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tf.debugging.assert_near(emb, desired_weights, atol=self.tolerance)
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def test_positional_emb_weights_against_roformer(self):
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desired_weights = tf.constant(
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[
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[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
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[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
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[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
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]
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)
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emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512)
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emb1([2, 16, 512])
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weights = emb1.weight[:3, :5]
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tf.debugging.assert_near(weights, desired_weights, atol=self.tolerance)
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@require_tf
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class TFRoFormerSelfAttentionRotaryPositionEmbeddingTest(unittest.TestCase):
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tolerance = 1e-4
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def test_apply_rotary_position_embeddings(self):
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# 2,12,16,64
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query_layer = tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
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key_layer = -tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
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embed_positions = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=64)
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sinusoidal_pos = embed_positions([2, 16, 768])[None, None, :, :]
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query_layer, key_layer = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
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sinusoidal_pos, query_layer, key_layer
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)
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desired_query_layer = tf.constant(
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[
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[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
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[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
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[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
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[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
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[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
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[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
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]
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)
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desired_key_layer = tf.constant(
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[
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[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
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[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
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[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
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[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
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[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
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[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
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]
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)
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tf.debugging.assert_near(query_layer[0, 0, :6, :8], desired_query_layer, atol=self.tolerance)
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tf.debugging.assert_near(key_layer[0, 0, :6, :8], desired_key_layer, atol=self.tolerance)
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