Funnel transformer (#6908)
* Initial model * Fix upsampling * Add special cls token id and test * Formatting * Test and fist FunnelTokenizerFast * Common tests * Fix the check_repo script and document Funnel * Doc fixes * Add all models * Write doc * Fix test * Initial model * Fix upsampling * Add special cls token id and test * Formatting * Test and fist FunnelTokenizerFast * Common tests * Fix the check_repo script and document Funnel * Doc fixes * Add all models * Write doc * Fix test * Fix copyright * Forgot some layers can be repeated * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/modeling_funnel.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Address review comments * Update src/transformers/modeling_funnel.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Address review comments * Update src/transformers/modeling_funnel.py Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * Slow integration test * Make small integration test * Formatting * Add checkpoint and separate classification head * Formatting * Expand list, fix link and add in pretrained models * Styling * Add the model in all summaries * Typo fixes Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
This commit is contained in:
Sylvain Gugger
@@ -539,7 +539,10 @@ class ModelTesterMixin:
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs[-1]
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self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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if hasattr(self.model_tester, "encoder_seq_length"):
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seq_length = self.model_tester.encoder_seq_length
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if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
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454
tests/test_modeling_funnel.py
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454
tests/test_modeling_funnel.py
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@@ -0,0 +1,454 @@
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# coding=utf-8
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# Copyright 2020 HuggingFace Inc. team.
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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 FunnelTokenizer, is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, ids_tensor
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if is_torch_available():
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import torch
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from transformers import (
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FunnelBaseModel,
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FunnelConfig,
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FunnelForMaskedLM,
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FunnelForMultipleChoice,
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FunnelForPreTraining,
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FunnelForQuestionAnswering,
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FunnelForSequenceClassification,
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FunnelForTokenClassification,
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FunnelModel,
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)
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class FunnelModelTester:
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"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester """
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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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block_sizes=[1, 1, 2],
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num_decoder_layers=1,
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d_model=32,
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n_head=4,
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d_head=8,
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d_inner=37,
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hidden_act="gelu_new",
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hidden_dropout=0.1,
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attention_dropout=0.1,
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activation_dropout=0.0,
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max_position_embeddings=512,
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type_vocab_size=3,
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num_labels=3,
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num_choices=4,
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scope=None,
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base=False,
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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.block_sizes = block_sizes
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self.num_decoder_layers = num_decoder_layers
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self.d_model = d_model
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self.n_head = n_head
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self.d_head = d_head
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self.d_inner = d_inner
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self.hidden_act = hidden_act
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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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 = 2
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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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# Used in the tests to check the size of the first attention layer
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self.num_attention_heads = n_head
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# Used in the tests to check the size of the first hidden state
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self.hidden_size = self.d_model
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# Used in the tests to check the number of output hidden states/attentions
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self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
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# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
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# the last hidden state of the first block (which is the first hidden state of the decoder).
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if not base:
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self.expected_num_hidden_layers = self.num_hidden_layers + 2
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1)
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config = FunnelConfig(
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vocab_size=self.vocab_size,
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block_sizes=self.block_sizes,
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num_decoder_layers=self.num_decoder_layers,
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d_model=self.d_model,
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n_head=self.n_head,
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d_head=self.d_head,
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d_inner=self.d_inner,
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hidden_act=self.hidden_act,
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hidden_dropout=self.hidden_dropout,
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attention_dropout=self.attention_dropout,
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activation_dropout=self.activation_dropout,
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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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return_dict=True,
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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_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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fake_token_labels,
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)
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def create_and_check_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_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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fake_token_labels,
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):
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model = FunnelModel(config=config)
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model.to(torch_device)
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model.eval()
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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)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
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model.config.truncate_seq = False
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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.d_model))
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model.config.separate_cls = False
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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.d_model))
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def create_and_check_base_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_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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fake_token_labels,
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):
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model = FunnelBaseModel(config=config)
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model.to(torch_device)
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model.eval()
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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)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
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model.config.truncate_seq = False
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
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model.config.separate_cls = False
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
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def create_and_check_for_pretraining(
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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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fake_token_labels,
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):
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config.num_labels = self.num_labels
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model = FunnelForPreTraining(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
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def create_and_check_for_masked_lm(
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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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fake_token_labels,
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):
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model = FunnelForMaskedLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_for_sequence_classification(
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self,
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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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fake_token_labels,
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):
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config.num_labels = self.num_labels
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model = FunnelForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_for_multiple_choice(
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self,
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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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fake_token_labels,
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):
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config.num_choices = self.num_choices
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model = FunnelForMultipleChoice(config=config)
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model.to(torch_device)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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result = model(
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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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labels=choice_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def create_and_check_for_token_classification(
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self,
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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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fake_token_labels,
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):
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config.num_labels = self.num_labels
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model = FunnelForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_for_question_answering(
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self,
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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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fake_token_labels,
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):
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model = FunnelForQuestionAnswering(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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fake_token_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class FunnelModelTest(ModelTesterMixin, unittest.TestCase):
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test_head_masking = False
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test_pruning = False
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all_model_classes = (
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(
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FunnelModel,
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FunnelForMaskedLM,
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FunnelForPreTraining,
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FunnelForQuestionAnswering,
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FunnelForTokenClassification,
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)
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if is_torch_available()
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else ()
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)
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def setUp(self):
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self.model_tester = FunnelModelTester(self)
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self.config_tester = ConfigTester(self, config_class=FunnelConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_pretraining(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
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def test_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
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def test_for_question_answering(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
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|
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@require_torch
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class FunnelBaseModelTest(ModelTesterMixin, unittest.TestCase):
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test_head_masking = False
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test_pruning = False
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all_model_classes = (
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(FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
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)
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def setUp(self):
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self.model_tester = FunnelModelTester(self, base=True)
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self.config_tester = ConfigTester(self, config_class=FunnelConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_base_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_base_model(*config_and_inputs)
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def test_for_sequence_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
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|
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def test_for_multiple_choice(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
|
||||
|
||||
|
||||
@require_torch
|
||||
class FunnelModelIntegrationTest(unittest.TestCase):
|
||||
def test_inference_tiny_model(self):
|
||||
batch_size = 13
|
||||
sequence_length = 7
|
||||
input_ids = torch.arange(0, batch_size * sequence_length).long().reshape(batch_size, sequence_length)
|
||||
lengths = [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
|
||||
token_type_ids = torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])
|
||||
|
||||
model = FunnelModel.from_pretrained("sgugger/funnel-random-tiny")
|
||||
output = model(input_ids, token_type_ids=token_type_ids)[0].abs()
|
||||
|
||||
expected_output_sum = torch.tensor(2344.9023)
|
||||
expected_output_mean = torch.tensor(0.8053)
|
||||
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
|
||||
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
|
||||
|
||||
attention_mask = torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]])
|
||||
output = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)[0].abs()
|
||||
|
||||
expected_output_sum = torch.tensor(2363.2178)
|
||||
expected_output_mean = torch.tensor(0.8115)
|
||||
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
|
||||
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
|
||||
|
||||
@slow
|
||||
def test_inference_model(self):
|
||||
tokenizer = FunnelTokenizer.from_pretrained("huggingface/funnel-small")
|
||||
model = FunnelModel.from_pretrained("huggingface/funnel-small")
|
||||
inputs = tokenizer("Hello! I am the Funnel Transformer model.", return_tensors="pt")
|
||||
output = model(**inputs)[0]
|
||||
|
||||
expected_output_sum = torch.tensor(235.7827)
|
||||
expected_output_mean = torch.tensor(0.0256)
|
||||
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
|
||||
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
|
||||
78
tests/test_tokenization_funnel.py
Normal file
78
tests/test_tokenization_funnel.py
Normal file
@@ -0,0 +1,78 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2020 HuggingFace Inc. team.
|
||||
#
|
||||
# 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 os
|
||||
import unittest
|
||||
|
||||
from transformers.tokenization_funnel import VOCAB_FILES_NAMES, FunnelTokenizer, FunnelTokenizerFast
|
||||
|
||||
from .test_tokenization_common import TokenizerTesterMixin
|
||||
|
||||
|
||||
class FunnelTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
||||
|
||||
tokenizer_class = FunnelTokenizer
|
||||
test_rust_tokenizer = True
|
||||
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
vocab_tokens = [
|
||||
"<unk>",
|
||||
"<cls>",
|
||||
"<sep>",
|
||||
"want",
|
||||
"##want",
|
||||
"##ed",
|
||||
"wa",
|
||||
"un",
|
||||
"runn",
|
||||
"##ing",
|
||||
",",
|
||||
"low",
|
||||
"lowest",
|
||||
]
|
||||
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
|
||||
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
|
||||
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
|
||||
|
||||
def get_tokenizer(self, **kwargs):
|
||||
return FunnelTokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def get_rust_tokenizer(self, **kwargs):
|
||||
return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def get_input_output_texts(self, tokenizer):
|
||||
input_text = "UNwant\u00E9d,running"
|
||||
output_text = "unwanted, running"
|
||||
return input_text, output_text
|
||||
|
||||
def test_full_tokenizer(self):
|
||||
tokenizer = self.tokenizer_class(self.vocab_file)
|
||||
|
||||
tokens = tokenizer.tokenize("UNwant\u00E9d,running")
|
||||
self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
|
||||
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
|
||||
|
||||
def test_token_type_ids(self):
|
||||
tokenizers = self.get_tokenizers(do_lower_case=False)
|
||||
for tokenizer in tokenizers:
|
||||
inputs = tokenizer("UNwant\u00E9d,running")
|
||||
sentence_len = len(inputs["input_ids"]) - 1
|
||||
self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len)
|
||||
|
||||
inputs = tokenizer("UNwant\u00E9d,running", "UNwant\u00E9d,running")
|
||||
self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len + [1] * sentence_len)
|
||||
Reference in New Issue
Block a user