368 lines
12 KiB
Python
368 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace 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 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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TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
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TFXLMForMultipleChoice,
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TFXLMForQuestionAnsweringSimple,
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TFXLMForSequenceClassification,
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TFXLMForTokenClassification,
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TFXLMModel,
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TFXLMWithLMHeadModel,
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XLMConfig,
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)
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class TFXLMModelTester:
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def __init__(
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self,
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parent,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_input_lengths = True
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self.use_token_type_ids = True
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self.use_labels = True
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self.gelu_activation = True
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self.sinusoidal_embeddings = False
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self.causal = False
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self.asm = False
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self.n_langs = 2
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self.vocab_size = 99
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self.n_special = 0
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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.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.summary_type = "last"
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self.use_proj = True
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self.scope = None
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self.bos_token_id = 0
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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 = ids_tensor([self.batch_size, self.seq_length], 2, dtype=tf.float32)
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input_lengths = None
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if self.use_input_lengths:
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input_lengths = (
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ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
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) # small variation of 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.n_langs)
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sequence_labels = None
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token_labels = None
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is_impossible_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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is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = XLMConfig(
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vocab_size=self.vocab_size,
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n_special=self.n_special,
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emb_dim=self.hidden_size,
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n_layers=self.num_hidden_layers,
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n_heads=self.num_attention_heads,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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gelu_activation=self.gelu_activation,
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sinusoidal_embeddings=self.sinusoidal_embeddings,
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asm=self.asm,
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causal=self.causal,
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n_langs=self.n_langs,
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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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summary_type=self.summary_type,
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use_proj=self.use_proj,
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bos_token_id=self.bos_token_id,
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)
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return (
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config,
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input_ids,
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token_type_ids,
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input_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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)
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def create_and_check_xlm_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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = TFXLMModel(config=config)
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inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs)
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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_xlm_lm_head(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = TFXLMWithLMHeadModel(config)
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inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
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outputs = model(inputs)
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result = outputs
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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_xlm_qa(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = TFXLMForQuestionAnsweringSimple(config)
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inputs = {"input_ids": input_ids, "lengths": input_lengths}
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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 create_and_check_xlm_sequence_classif(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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model = TFXLMForSequenceClassification(config)
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inputs = {"input_ids": input_ids, "lengths": input_lengths}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
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def create_and_check_xlm_for_token_classification(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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config.num_labels = self.num_labels
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model = TFXLMForTokenClassification(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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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_xlm_for_multiple_choice(
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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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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):
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config.num_choices = self.num_choices
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model = TFXLMForMultipleChoice(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 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_lengths,
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sequence_labels,
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token_labels,
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is_impossible_labels,
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choice_labels,
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input_mask,
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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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"langs": token_type_ids,
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"lengths": input_lengths,
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}
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return config, inputs_dict
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@require_tf
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class TFXLMModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TFXLMModel,
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TFXLMWithLMHeadModel,
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TFXLMForSequenceClassification,
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TFXLMForQuestionAnsweringSimple,
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TFXLMForTokenClassification,
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TFXLMForMultipleChoice,
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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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all_generative_model_classes = (
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(TFXLMWithLMHeadModel,) if is_tf_available() else ()
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) # TODO (PVP): Check other models whether language generation is also applicable
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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 = TFXLMModelTester(self)
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self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=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_xlm_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_xlm_model(*config_and_inputs)
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def test_xlm_lm_head(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_xlm_lm_head(*config_and_inputs)
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def test_xlm_qa(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_xlm_qa(*config_and_inputs)
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def test_xlm_sequence_classif(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_xlm_sequence_classif(*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_xlm_for_token_classification(*config_and_inputs)
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def test_for_multiple_choice(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_xlm_for_multiple_choice(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFXLMModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_tf
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class TFXLMModelLanguageGenerationTest(unittest.TestCase):
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@slow
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def test_lm_generate_xlm_mlm_en_2048(self):
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model = TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
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input_ids = tf.convert_to_tensor([[14, 447]], dtype=tf.int32) # the president
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expected_output_ids = [
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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14,
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447,
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] # the president the president the president the president the president the president the president the president the president the president
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# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
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output_ids = model.generate(input_ids, do_sample=False)
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self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
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