Deprecate TF + JAX (#38758)
* Scatter deprecation warnings around * Delete the tests * Make logging work properly!
This commit is contained in:
Matt
@@ -1,163 +0,0 @@
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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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import numpy as np
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from transformers import BertConfig, is_flax_available
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from transformers.testing_utils import require_flax, slow
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from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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if is_flax_available():
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from transformers.models.bert.modeling_flax_bert import (
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FlaxBertForMaskedLM,
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FlaxBertForMultipleChoice,
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FlaxBertForNextSentencePrediction,
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FlaxBertForPreTraining,
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FlaxBertForQuestionAnswering,
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FlaxBertForSequenceClassification,
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FlaxBertForTokenClassification,
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FlaxBertModel,
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)
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class FlaxBertModelTester:
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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_attention_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=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_choices=4,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_attention_mask = use_attention_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.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_choices = num_choices
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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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attention_mask = None
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if self.use_attention_mask:
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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config = BertConfig(
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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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)
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return config, input_ids, token_type_ids, attention_mask
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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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config, input_ids, token_type_ids, attention_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
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return config, inputs_dict
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def prepare_config_and_inputs_for_decoder(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, token_type_ids, attention_mask = config_and_inputs
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config.is_decoder = True
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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config,
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input_ids,
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attention_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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@require_flax
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class FlaxBertModelTest(FlaxModelTesterMixin, unittest.TestCase):
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test_head_masking = True
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all_model_classes = (
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(
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FlaxBertModel,
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FlaxBertForPreTraining,
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FlaxBertForMaskedLM,
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FlaxBertForMultipleChoice,
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FlaxBertForQuestionAnswering,
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FlaxBertForNextSentencePrediction,
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FlaxBertForSequenceClassification,
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FlaxBertForTokenClassification,
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FlaxBertForQuestionAnswering,
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)
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if is_flax_available()
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else ()
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)
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def setUp(self):
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self.model_tester = FlaxBertModelTester(self)
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@slow
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def test_model_from_pretrained(self):
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# Only check this for base model, not necessary for all model classes.
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# This will also help speed-up tests.
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model = FlaxBertModel.from_pretrained("google-bert/bert-base-cased")
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outputs = model(np.ones((1, 1)))
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self.assertIsNotNone(outputs)
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@@ -1,764 +0,0 @@
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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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from __future__ import annotations
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import unittest
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from transformers import BertConfig, is_tf_available
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from transformers.models.auto import get_values
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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, floats_tensor, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
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if is_tf_available():
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import tensorflow as tf
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from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
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from transformers.models.bert.modeling_tf_bert import (
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TFBertForMaskedLM,
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TFBertForMultipleChoice,
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TFBertForNextSentencePrediction,
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TFBertForPreTraining,
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TFBertForQuestionAnswering,
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TFBertForSequenceClassification,
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TFBertForTokenClassification,
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TFBertLMHeadModel,
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TFBertModel,
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)
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class TFBertModelTester:
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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=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 = 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 = 2
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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 = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = BertConfig(
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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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)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def prepare_config_and_inputs_for_decoder(self):
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = self.prepare_config_and_inputs()
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config.is_decoder = True
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertModel(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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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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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_causal_lm_base_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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config.is_decoder = True
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model = TFBertModel(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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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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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_model_as_decoder(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = TFBertModel(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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"encoder_hidden_states": encoder_hidden_states,
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"encoder_attention_mask": encoder_attention_mask,
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}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
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# Also check the case where encoder outputs are not passed
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_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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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_causal_lm_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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config.is_decoder = True
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model = TFBertLMHeadModel(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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|
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def create_and_check_causal_lm_model_as_decoder(
|
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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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sequence_labels,
|
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token_labels,
|
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choice_labels,
|
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encoder_hidden_states,
|
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encoder_attention_mask,
|
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):
|
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config.add_cross_attention = True
|
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|
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model = TFBertLMHeadModel(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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"encoder_hidden_states": encoder_hidden_states,
|
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"encoder_attention_mask": encoder_attention_mask,
|
||||
}
|
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result = model(inputs)
|
||||
|
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inputs = [input_ids, input_mask]
|
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result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states)
|
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|
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prediction_scores = result["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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|
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def create_and_check_causal_lm_model_past(
|
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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,
|
||||
sequence_labels,
|
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token_labels,
|
||||
choice_labels,
|
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):
|
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config.is_decoder = True
|
||||
|
||||
model = TFBertLMHeadModel(config=config)
|
||||
|
||||
# first forward pass
|
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outputs = model(input_ids, use_cache=True)
|
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outputs_use_cache_conf = model(input_ids)
|
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outputs_no_past = model(input_ids, use_cache=False)
|
||||
|
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self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
|
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self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
|
||||
|
||||
past_key_values = outputs.past_key_values
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
||||
|
||||
# append to next input_ids and attn_mask
|
||||
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
|
||||
|
||||
output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0]
|
||||
output_from_past = model(
|
||||
next_tokens, past_key_values=past_key_values, output_hidden_states=True
|
||||
).hidden_states[0]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
|
||||
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
|
||||
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
|
||||
|
||||
# test that outputs are equal for slice
|
||||
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
|
||||
|
||||
def create_and_check_causal_lm_model_past_with_attn_mask(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
):
|
||||
config.is_decoder = True
|
||||
|
||||
model = TFBertLMHeadModel(config=config)
|
||||
|
||||
# create attention mask
|
||||
half_seq_length = self.seq_length // 2
|
||||
attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32)
|
||||
attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32)
|
||||
attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
|
||||
|
||||
# first forward pass
|
||||
outputs = model(input_ids, attention_mask=attn_mask, use_cache=True)
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
|
||||
|
||||
past_key_values = outputs.past_key_values
|
||||
|
||||
# change a random masked slice from input_ids
|
||||
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1
|
||||
random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size)
|
||||
vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
|
||||
condition = tf.transpose(
|
||||
tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size))
|
||||
)
|
||||
input_ids = tf.where(condition, random_other_next_tokens, input_ids)
|
||||
|
||||
# append to next input_ids and
|
||||
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
|
||||
attn_mask = tf.concat(
|
||||
[attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
|
||||
axis=1,
|
||||
)
|
||||
|
||||
output_from_no_past = model(
|
||||
next_input_ids,
|
||||
attention_mask=attn_mask,
|
||||
output_hidden_states=True,
|
||||
).hidden_states[0]
|
||||
output_from_past = model(
|
||||
next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True
|
||||
).hidden_states[0]
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
|
||||
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx]
|
||||
output_from_past_slice = output_from_past[:, 0, random_slice_idx]
|
||||
|
||||
# test that outputs are equal for slice
|
||||
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6)
|
||||
|
||||
def create_and_check_causal_lm_model_past_large_inputs(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
):
|
||||
config.is_decoder = True
|
||||
|
||||
model = TFBertLMHeadModel(config=config)
|
||||
|
||||
input_ids = input_ids[:1, :]
|
||||
input_mask = input_mask[:1, :]
|
||||
self.batch_size = 1
|
||||
|
||||
# first forward pass
|
||||
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
|
||||
past_key_values = outputs.past_key_values
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
|
||||
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
|
||||
|
||||
# append to next input_ids and
|
||||
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
|
||||
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
|
||||
|
||||
output_from_no_past = model(
|
||||
next_input_ids,
|
||||
attention_mask=next_attention_mask,
|
||||
output_hidden_states=True,
|
||||
).hidden_states[0]
|
||||
output_from_past = model(
|
||||
next_tokens,
|
||||
attention_mask=next_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
output_hidden_states=True,
|
||||
).hidden_states[0]
|
||||
|
||||
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
|
||||
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
|
||||
output_from_past_slice = output_from_past[:, :, random_slice_idx]
|
||||
|
||||
# test that outputs are equal for slice
|
||||
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
|
||||
|
||||
def create_and_check_decoder_model_past_large_inputs(
|
||||
self,
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
):
|
||||
config.add_cross_attention = True
|
||||
|
||||
model = TFBertLMHeadModel(config=config)
|
||||
|
||||
input_ids = input_ids[:1, :]
|
||||
input_mask = input_mask[:1, :]
|
||||
encoder_hidden_states = encoder_hidden_states[:1, :, :]
|
||||
encoder_attention_mask = encoder_attention_mask[:1, :]
|
||||
self.batch_size = 1
|
||||
|
||||
# first forward pass
|
||||
outputs = model(
|
||||
input_ids,
|
||||
attention_mask=input_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
use_cache=True,
|
||||
)
|
||||
past_key_values = outputs.past_key_values
|
||||
|
||||
# create hypothetical next token and extent to next_input_ids
|
||||
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
|
||||
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
|
||||
|
||||
# append to next input_ids and
|
||||
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
|
||||
next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1)
|
||||
|
||||
output_from_no_past = model(
|
||||
next_input_ids,
|
||||
attention_mask=next_attention_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
output_hidden_states=True,
|
||||
).hidden_states[0]
|
||||
output_from_past = model(
|
||||
next_tokens,
|
||||
attention_mask=next_attention_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
output_hidden_states=True,
|
||||
).hidden_states[0]
|
||||
|
||||
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
|
||||
|
||||
# select random slice
|
||||
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
|
||||
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
|
||||
output_from_past_slice = output_from_past[:, :, random_slice_idx]
|
||||
|
||||
# test that outputs are equal for slice
|
||||
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
|
||||
|
||||
def create_and_check_for_masked_lm(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = TFBertForMaskedLM(config=config)
|
||||
inputs = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": input_mask,
|
||||
"token_type_ids": token_type_ids,
|
||||
}
|
||||
result = model(inputs)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
||||
|
||||
def create_and_check_for_next_sequence_prediction(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = TFBertForNextSentencePrediction(config=config)
|
||||
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
|
||||
result = model(inputs)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
|
||||
|
||||
def create_and_check_for_pretraining(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = TFBertForPreTraining(config=config)
|
||||
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
|
||||
result = model(inputs)
|
||||
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
|
||||
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
|
||||
|
||||
def create_and_check_for_sequence_classification(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
config.num_labels = self.num_labels
|
||||
model = TFBertForSequenceClassification(config=config)
|
||||
inputs = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": input_mask,
|
||||
"token_type_ids": token_type_ids,
|
||||
}
|
||||
|
||||
result = model(inputs)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
|
||||
|
||||
def create_and_check_for_multiple_choice(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
config.num_choices = self.num_choices
|
||||
model = TFBertForMultipleChoice(config=config)
|
||||
multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
|
||||
multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
|
||||
multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
|
||||
inputs = {
|
||||
"input_ids": multiple_choice_inputs_ids,
|
||||
"attention_mask": multiple_choice_input_mask,
|
||||
"token_type_ids": multiple_choice_token_type_ids,
|
||||
}
|
||||
result = model(inputs)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
|
||||
|
||||
def create_and_check_for_token_classification(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
config.num_labels = self.num_labels
|
||||
model = TFBertForTokenClassification(config=config)
|
||||
inputs = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": input_mask,
|
||||
"token_type_ids": token_type_ids,
|
||||
}
|
||||
result = model(inputs)
|
||||
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
|
||||
|
||||
def create_and_check_for_question_answering(
|
||||
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
|
||||
):
|
||||
model = TFBertForQuestionAnswering(config=config)
|
||||
inputs = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": input_mask,
|
||||
"token_type_ids": token_type_ids,
|
||||
}
|
||||
|
||||
result = model(inputs)
|
||||
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
|
||||
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
token_type_ids,
|
||||
input_mask,
|
||||
sequence_labels,
|
||||
token_labels,
|
||||
choice_labels,
|
||||
) = config_and_inputs
|
||||
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFBertModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (
|
||||
(
|
||||
TFBertModel,
|
||||
TFBertForMaskedLM,
|
||||
TFBertLMHeadModel,
|
||||
TFBertForNextSentencePrediction,
|
||||
TFBertForPreTraining,
|
||||
TFBertForQuestionAnswering,
|
||||
TFBertForSequenceClassification,
|
||||
TFBertForTokenClassification,
|
||||
TFBertForMultipleChoice,
|
||||
)
|
||||
if is_tf_available()
|
||||
else ()
|
||||
)
|
||||
pipeline_model_mapping = (
|
||||
{
|
||||
"feature-extraction": TFBertModel,
|
||||
"fill-mask": TFBertForMaskedLM,
|
||||
"question-answering": TFBertForQuestionAnswering,
|
||||
"text-classification": TFBertForSequenceClassification,
|
||||
"text-generation": TFBertLMHeadModel,
|
||||
"token-classification": TFBertForTokenClassification,
|
||||
"zero-shot": TFBertForSequenceClassification,
|
||||
}
|
||||
if is_tf_available()
|
||||
else {}
|
||||
)
|
||||
test_head_masking = False
|
||||
test_onnx = True
|
||||
onnx_min_opset = 10
|
||||
|
||||
# special case for ForPreTraining model
|
||||
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
|
||||
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
|
||||
|
||||
if return_labels:
|
||||
if model_class in get_values(TF_MODEL_FOR_PRETRAINING_MAPPING):
|
||||
inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
|
||||
|
||||
return inputs_dict
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = TFBertModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_model(self):
|
||||
"""Test the base model"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_causal_lm_base_model(self):
|
||||
"""Test the base model of the causal LM model
|
||||
|
||||
is_decoder=True, no cross_attention, no encoder outputs
|
||||
"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs)
|
||||
|
||||
def test_model_as_decoder(self):
|
||||
"""Test the base model as a decoder (of an encoder-decoder architecture)
|
||||
|
||||
is_decoder=True + cross_attention + pass encoder outputs
|
||||
"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
||||
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
|
||||
|
||||
def test_for_masked_lm(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
|
||||
|
||||
def test_for_causal_lm(self):
|
||||
"""Test the causal LM model"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_causal_lm_model(*config_and_inputs)
|
||||
|
||||
def test_causal_lm_model_as_decoder(self):
|
||||
"""Test the causal LM model as a decoder"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
||||
self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs)
|
||||
|
||||
def test_causal_lm_model_past(self):
|
||||
"""Test causal LM model with `past_key_values`"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs)
|
||||
|
||||
def test_causal_lm_model_past_with_attn_mask(self):
|
||||
"""Test the causal LM model with `past_key_values` and `attention_mask`"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)
|
||||
|
||||
def test_causal_lm_model_past_with_large_inputs(self):
|
||||
"""Test the causal LM model with `past_key_values` and a longer decoder sequence length"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs)
|
||||
|
||||
def test_decoder_model_past_with_large_inputs(self):
|
||||
"""Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention"""
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
|
||||
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
|
||||
|
||||
def test_for_multiple_choice(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
|
||||
|
||||
def test_for_next_sequence_prediction(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs)
|
||||
|
||||
def test_for_pretraining(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
|
||||
|
||||
def test_for_question_answering(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
|
||||
|
||||
def test_for_sequence_classification(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
|
||||
|
||||
def test_for_token_classification(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
|
||||
|
||||
def test_model_from_pretrained(self):
|
||||
model = TFBertModel.from_pretrained("jplu/tiny-tf-bert-random")
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
def test_custom_load_tf_weights(self):
|
||||
model, output_loading_info = TFBertForTokenClassification.from_pretrained(
|
||||
"jplu/tiny-tf-bert-random", output_loading_info=True
|
||||
)
|
||||
self.assertEqual(sorted(output_loading_info["unexpected_keys"]), [])
|
||||
for layer in output_loading_info["missing_keys"]:
|
||||
self.assertTrue(layer.split("_")[0] in ["dropout", "classifier"])
|
||||
|
||||
# TODO (Joao): fix me
|
||||
@unittest.skip("Onnx compliance broke with TF 2.10")
|
||||
def test_onnx_compliancy(self):
|
||||
pass
|
||||
|
||||
|
||||
@require_tf
|
||||
class TFBertModelIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_inference_masked_lm(self):
|
||||
model = TFBertForPreTraining.from_pretrained("lysandre/tiny-bert-random")
|
||||
input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
|
||||
output = model(input_ids)[0]
|
||||
|
||||
expected_shape = [1, 6, 32000]
|
||||
self.assertEqual(output.shape, expected_shape)
|
||||
|
||||
print(output[:, :3, :3])
|
||||
|
||||
expected_slice = tf.constant(
|
||||
[
|
||||
[
|
||||
[-0.05243197, -0.04498899, 0.05512108],
|
||||
[-0.07444685, -0.01064632, 0.04352357],
|
||||
[-0.05020351, 0.05530146, 0.00700043],
|
||||
]
|
||||
]
|
||||
)
|
||||
tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
|
||||
Reference in New Issue
Block a user