Add TFDPR (#8203)
* Create modeling_tf_dpr.py * Add TFDPR * Add back TFPegasus, TFMarian, TFMBart, TFBlenderBot last commit accidentally deleted these 4 lines, so I recover them back * Add TFDPR * Add TFDPR * clean up some comments, add TF input-style doc string * Add TFDPR * Make return_dict=False as default * Fix return_dict bug (in .from_pretrained) * Add get_input_embeddings() * Create test_modeling_tf_dpr.py The current version is already passed all 27 tests! Please see the test run at : https://colab.research.google.com/drive/1czS_m9zy5k-iSJbzA_DP1k1xAAC_sdkf?usp=sharing * fix quality * delete init weights * run fix copies * fix repo consis * del config_class, load_tf_weights They shoud be 'pytorch only' * add config_class back after removing it, test failed ... so totally only removing "use_tf_weights = None" on Lysandre suggestion * newline after .. note:: * import tf, np (Necessary for ModelIntegrationTest) * slow_test from_pretrained with from_pt=True At the moment we don't have TF weights (since we don't have official official TF model) Previously, I did not run slow test, so I missed this bug * Add simple TFDPRModelIntegrationTest Note that this is just a test that TF and Pytorch gives approx. the same output. However, I could not test with the official DPR repo's output yet * upload correct tf model * remove position_ids as missing keys Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: patrickvonplaten <patrick@huggingface.co>
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Ratthachat (Jung)
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GitHub
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a38d1c7c31
commit
026a2ff225
@@ -24,6 +24,8 @@ from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention
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if is_torch_available():
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import torch
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from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
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from transformers.modeling_dpr import (
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DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
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@@ -227,3 +229,36 @@ class DPRModelTest(ModelTesterMixin, unittest.TestCase):
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for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = DPRReader.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_torch
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class DPRModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_no_head(self):
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model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base", return_dict=False)
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model.to(torch_device)
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input_ids = torch.tensor(
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[[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]], dtype=torch.long, device=torch_device
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) # [CLS] hello, is my dog cute? [SEP]
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output = model(input_ids)[0] # embedding shape = (1, 768)
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# compare the actual values for a slice.
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expected_slice = torch.tensor(
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[
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[
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0.03236253,
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0.12753335,
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0.16818509,
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0.00279786,
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0.3896933,
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0.24264945,
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0.2178971,
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-0.02335227,
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-0.08481959,
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-0.14324117,
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]
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],
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dtype=torch.float,
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device=torch_device,
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)
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self.assertTrue(torch.allclose(output[:, :10], expected_slice, atol=1e-4))
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260
tests/test_modeling_tf_dpr.py
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260
tests/test_modeling_tf_dpr.py
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@@ -0,0 +1,260 @@
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# coding=utf-8
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# Copyright 2020 Huggingface
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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 numpy
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import tensorflow as tf
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from transformers import (
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TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
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TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
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TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
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BertConfig,
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DPRConfig,
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TFDPRContextEncoder,
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TFDPRQuestionEncoder,
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TFDPRReader,
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)
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class TFDPRModelTester:
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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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projection_dim=0,
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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_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.projection_dim = projection_dim
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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(
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[self.batch_size, self.seq_length], vocab_size=2
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) # follow test_modeling_tf_ctrl.py
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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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is_decoder=False,
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initializer_range=self.initializer_range,
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# MODIFY
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return_dict=False,
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)
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config = DPRConfig(projection_dim=self.projection_dim, **config.to_dict())
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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_dpr_context_encoder(
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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 = TFDPRContextEncoder(config=config)
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids, return_dict=True) # MODIFY
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
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def create_and_check_dpr_question_encoder(
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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 = TFDPRQuestionEncoder(config=config)
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids, return_dict=True) # MODIFY
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
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def create_and_check_dpr_reader(
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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 = TFDPRReader(config=config)
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result = model(input_ids, attention_mask=input_mask, return_dict=True) # MODIFY
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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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self.parent.assertEqual(result.relevance_logits.shape, (self.batch_size,))
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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}
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return config, inputs_dict
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@require_tf
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class TFDPRModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TFDPRContextEncoder,
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TFDPRQuestionEncoder,
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TFDPRReader,
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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_resize_embeddings = False
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test_missing_keys = False
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test_pruning = False
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test_head_masking = False
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def setUp(self):
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self.model_tester = TFDPRModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DPRConfig, 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_dpr_context_encoder_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_dpr_context_encoder(*config_and_inputs)
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def test_dpr_question_encoder_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_dpr_question_encoder(*config_and_inputs)
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def test_dpr_reader_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_dpr_reader(*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_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFDPRContextEncoder.from_pretrained(model_name, from_pt=True)
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self.assertIsNotNone(model)
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for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFDPRContextEncoder.from_pretrained(model_name, from_pt=True)
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self.assertIsNotNone(model)
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for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFDPRQuestionEncoder.from_pretrained(model_name, from_pt=True)
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self.assertIsNotNone(model)
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for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFDPRReader.from_pretrained(model_name, from_pt=True)
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self.assertIsNotNone(model)
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@require_tf
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class TFDPRModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_no_head(self):
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model = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base", return_dict=False)
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input_ids = tf.constant(
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[[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]
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) # [CLS] hello, is my dog cute? [SEP]
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output = model(input_ids)[0] # embedding shape = (1, 768)
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# compare the actual values for a slice.
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expected_slice = tf.constant(
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[
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[
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0.03236253,
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0.12753335,
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0.16818509,
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0.00279786,
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0.3896933,
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0.24264945,
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0.2178971,
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-0.02335227,
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-0.08481959,
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-0.14324117,
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]
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]
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)
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self.assertTrue(numpy.allclose(output[:, :10].numpy(), expected_slice.numpy(), atol=1e-4))
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