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- # coding=utf-8
- # Copyright 2020 Huggingface
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
- import unittest
-
- from transformers import is_tf_available
- from transformers.testing_utils import require_tf, slow
-
- from .test_configuration_common import ConfigTester
- from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
-
-
- if is_tf_available():
- import numpy
- import tensorflow as tf
-
- from transformers import (
- TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
- TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
- TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
- BertConfig,
- DPRConfig,
- TFDPRContextEncoder,
- TFDPRQuestionEncoder,
- TFDPRReader,
- )
-
-
- class TFDPRModelTester:
- def __init__(
- self,
- parent,
- batch_size=13,
- seq_length=7,
- is_training=True,
- use_input_mask=True,
- use_token_type_ids=True,
- use_labels=True,
- vocab_size=99,
- hidden_size=32,
- num_hidden_layers=5,
- num_attention_heads=4,
- intermediate_size=37,
- hidden_act="gelu",
- hidden_dropout_prob=0.1,
- attention_probs_dropout_prob=0.1,
- max_position_embeddings=512,
- type_vocab_size=16,
- type_sequence_label_size=2,
- initializer_range=0.02,
- num_labels=3,
- num_choices=4,
- scope=None,
- projection_dim=0,
- ):
- self.parent = parent
- self.batch_size = batch_size
- self.seq_length = seq_length
- self.is_training = is_training
- self.use_input_mask = use_input_mask
- self.use_token_type_ids = use_token_type_ids
- self.use_labels = use_labels
- self.vocab_size = vocab_size
- self.hidden_size = hidden_size
- self.num_hidden_layers = num_hidden_layers
- self.num_attention_heads = num_attention_heads
- self.intermediate_size = intermediate_size
- self.hidden_act = hidden_act
- self.hidden_dropout_prob = hidden_dropout_prob
- self.attention_probs_dropout_prob = attention_probs_dropout_prob
- self.max_position_embeddings = max_position_embeddings
- self.type_vocab_size = type_vocab_size
- self.type_sequence_label_size = type_sequence_label_size
- self.initializer_range = initializer_range
- self.num_labels = num_labels
- self.num_choices = num_choices
- self.scope = scope
- self.projection_dim = projection_dim
-
- def prepare_config_and_inputs(self):
- input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
-
- input_mask = None
- if self.use_input_mask:
- input_mask = ids_tensor(
- [self.batch_size, self.seq_length], vocab_size=2
- ) # follow test_modeling_tf_ctrl.py
-
- token_type_ids = None
- if self.use_token_type_ids:
- token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
-
- sequence_labels = None
- token_labels = None
- choice_labels = None
- if self.use_labels:
- sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
- token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
- choice_labels = ids_tensor([self.batch_size], self.num_choices)
-
- config = BertConfig(
- vocab_size=self.vocab_size,
- hidden_size=self.hidden_size,
- num_hidden_layers=self.num_hidden_layers,
- num_attention_heads=self.num_attention_heads,
- intermediate_size=self.intermediate_size,
- hidden_act=self.hidden_act,
- hidden_dropout_prob=self.hidden_dropout_prob,
- attention_probs_dropout_prob=self.attention_probs_dropout_prob,
- max_position_embeddings=self.max_position_embeddings,
- type_vocab_size=self.type_vocab_size,
- is_decoder=False,
- initializer_range=self.initializer_range,
- )
- config = DPRConfig(projection_dim=self.projection_dim, **config.to_dict())
-
- return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
- def create_and_check_dpr_context_encoder(
- self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = TFDPRContextEncoder(config=config)
- result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
- result = model(input_ids, token_type_ids=token_type_ids)
- result = model(input_ids)
- self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
-
- def create_and_check_dpr_question_encoder(
- self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = TFDPRQuestionEncoder(config=config)
- result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
- result = model(input_ids, token_type_ids=token_type_ids)
- result = model(input_ids)
- self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
-
- def create_and_check_dpr_reader(
- self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = TFDPRReader(config=config)
- result = model(input_ids, attention_mask=input_mask)
-
- 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))
- self.parent.assertEqual(result.relevance_logits.shape, (self.batch_size,))
-
- 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}
- return config, inputs_dict
-
-
- @require_tf
- class TFDPRModelTest(TFModelTesterMixin, unittest.TestCase):
-
- all_model_classes = (
- (
- TFDPRContextEncoder,
- TFDPRQuestionEncoder,
- TFDPRReader,
- )
- if is_tf_available()
- else ()
- )
-
- test_resize_embeddings = False
- test_missing_keys = False
- test_pruning = False
- test_head_masking = False
- test_onnx = False
-
- def setUp(self):
- self.model_tester = TFDPRModelTester(self)
- self.config_tester = ConfigTester(self, config_class=DPRConfig, hidden_size=37)
-
- def test_config(self):
- self.config_tester.run_common_tests()
-
- def test_dpr_context_encoder_model(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_dpr_context_encoder(*config_and_inputs)
-
- def test_dpr_question_encoder_model(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_dpr_question_encoder(*config_and_inputs)
-
- def test_dpr_reader_model(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_dpr_reader(*config_and_inputs)
-
- @slow
- def test_model_from_pretrained(self):
- for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
- model = TFDPRContextEncoder.from_pretrained(model_name)
- self.assertIsNotNone(model)
-
- for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
- model = TFDPRContextEncoder.from_pretrained(model_name)
- self.assertIsNotNone(model)
-
- for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
- model = TFDPRQuestionEncoder.from_pretrained(model_name)
- self.assertIsNotNone(model)
-
- for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
- model = TFDPRReader.from_pretrained(model_name)
- self.assertIsNotNone(model)
-
-
- @require_tf
- class TFDPRModelIntegrationTest(unittest.TestCase):
- @slow
- def test_inference_no_head(self):
- model = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
-
- input_ids = tf.constant(
- [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]
- ) # [CLS] hello, is my dog cute? [SEP]
- output = model(input_ids)[0] # embedding shape = (1, 768)
- # compare the actual values for a slice.
- expected_slice = tf.constant(
- [
- [
- 0.03236253,
- 0.12753335,
- 0.16818509,
- 0.00279786,
- 0.3896933,
- 0.24264945,
- 0.2178971,
- -0.02335227,
- -0.08481959,
- -0.14324117,
- ]
- ]
- )
- self.assertTrue(numpy.allclose(output[:, :10].numpy(), expected_slice.numpy(), atol=1e-4))
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