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transformers/tests/models/funnel/test_modeling_funnel.py

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# 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 unittest
from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
)
class FunnelModelTester:
"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester"""
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,
block_sizes=[1, 1, 2],
num_decoder_layers=1,
d_model=32,
n_head=4,
d_head=8,
d_inner=37,
hidden_act="gelu_new",
hidden_dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
max_position_embeddings=512,
type_vocab_size=3,
initializer_std=0.02, # Set to a smaller value, so we can keep the small error threshold (1e-5) in the test
num_labels=3,
num_choices=4,
scope=None,
base=False,
):
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.block_sizes = block_sizes
self.num_decoder_layers = num_decoder_layers
self.d_model = d_model
self.n_head = n_head
self.d_head = d_head
self.d_inner = d_inner
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = 2
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.initializer_std = initializer_std
# Used in the tests to check the size of the first attention layer
self.num_attention_heads = n_head
# Used in the tests to check the size of the first hidden state
self.hidden_size = self.d_model
# Used in the tests to check the number of output hidden states/attentions
self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
self.expected_num_hidden_layers = self.num_hidden_layers + 2
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)
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)
fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1)
config = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
)
def get_config(self):
return FunnelConfig(
vocab_size=self.vocab_size,
block_sizes=self.block_sizes,
num_decoder_layers=self.num_decoder_layers,
d_model=self.d_model,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_inner,
hidden_act=self.hidden_act,
hidden_dropout=self.hidden_dropout,
attention_dropout=self.attention_dropout,
activation_dropout=self.activation_dropout,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_std=self.initializer_std,
)
def create_and_check_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelModel(config=config)
model.to(torch_device)
model.eval()
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.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
def create_and_check_base_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelBaseModel(config=config)
model.to(torch_device)
model.eval()
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.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
def create_and_check_for_pretraining(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_masked_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
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,
fake_token_labels,
):
config.num_choices = self.num_choices
model = FunnelForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
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,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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,
fake_token_labels,
):
model = FunnelForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
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,
fake_token_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_torch
class FunnelModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(
FunnelModel,
FunnelForMaskedLM,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": (FunnelBaseModel, FunnelModel),
"fill-mask": FunnelForMaskedLM,
"question-answering": FunnelForQuestionAnswering,
"text-classification": FunnelForSequenceClassification,
"token-classification": FunnelForTokenClassification,
"zero-shot": FunnelForSequenceClassification,
}
if is_torch_available()
else {}
)
# 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(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = FunnelModelTester(self)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*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_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_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_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)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
class FunnelBaseModelTest(ModelTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
)
def setUp(self):
self.model_tester = FunnelModelTester(self, base=True)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_base_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*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_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)
# overwrite from test_modeling_common
def test_training(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
if model_class.__name__ == "FunnelBaseModel":
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
@require_sentencepiece
@require_tokenizers
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(2344.8352)
expected_output_mean = torch.tensor(0.8052)
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(2343.8425)
expected_output_mean = torch.tensor(0.8049)
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(235.7246)
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))
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