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[WIP] Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, MultipleC… (#5614)

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* Test TF Flaubert + Add {XLM, Flaubert}{TokenClassification, MultipleChoice} models and tests

* AutoModels


Tiny tweaks

* Style

* Final changes before merge

* Re-order for simpler review

* Final fixes

* Addressing @sgugger's comments

* Test MultipleChoice
This commit is contained in:
Lysandre Debut
2020-07-29 14:26:26 -04:00
committed by GitHub
parent 8a8ae27617
commit 3f94170a10
12 changed files with 652 additions and 21 deletions
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3
src/transformers/__init__.py
View File

@@ -278,6 +278,7 @@ if is_torch_available():
XLMForTokenClassification,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForMultipleChoice,
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
)
from .modeling_bart import (
@@ -356,6 +357,8 @@ if is_torch_available():
FlaubertForTokenClassification,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
)

5
src/transformers/modeling_auto.py
View File

@@ -98,6 +98,7 @@ from .modeling_electra import (
)
from .modeling_encoder_decoder import EncoderDecoderModel
from .modeling_flaubert import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
@@ -142,6 +143,7 @@ from .modeling_roberta import (
from .modeling_t5 import T5ForConditionalGeneration, T5Model
from .modeling_transfo_xl import TransfoXLLMHeadModel, TransfoXLModel
from .modeling_xlm import (
XLMForMultipleChoice,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
@@ -338,6 +340,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
(XLNetConfig, XLNetForTokenClassification),
(AlbertConfig, AlbertForTokenClassification),
(ElectraConfig, ElectraForTokenClassification),
(FlaubertConfig, FlaubertForTokenClassification),
]
)
@@ -353,6 +356,8 @@ MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
(MobileBertConfig, MobileBertForMultipleChoice),
(XLNetConfig, XLNetForMultipleChoice),
(AlbertConfig, AlbertForMultipleChoice),
(XLMConfig, XLMForMultipleChoice),
(FlaubertConfig, FlaubertForMultipleChoice),
]
)

20
src/transformers/modeling_flaubert.py
View File

@@ -25,6 +25,7 @@ from .configuration_flaubert import FlaubertConfig
from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
from .modeling_outputs import BaseModelOutput
from .modeling_xlm import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
@@ -382,3 +383,22 @@ class FlaubertForQuestionAnswering(XLMForQuestionAnswering):
super().__init__(config)
self.transformer = FlaubertModel(config)
self.init_weights()
@add_start_docstrings(
"""Flaubert Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
FLAUBERT_START_DOCSTRING,
)
class FlaubertForMultipleChoice(XLMForMultipleChoice):
"""
This class overrides :class:`~transformers.XLMForMultipleChoice`. Please check the
superclass for the appropriate documentation alongside usage examples.
"""
config_class = FlaubertConfig
def __init__(self, config):
super().__init__(config)
self.transformer = FlaubertModel(config)
self.init_weights()

36
src/transformers/modeling_tf_flaubert.py
View File

@@ -22,7 +22,7 @@ import tensorflow as tf
from .configuration_flaubert import FlaubertConfig
from .file_utils import add_start_docstrings
from .modeling_tf_utils import keras_serializable, shape_list
from .modeling_tf_utils import cast_bool_to_primitive, keras_serializable, shape_list
from .modeling_tf_xlm import (
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
@@ -30,6 +30,7 @@ from .modeling_tf_xlm import (
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPredLayer,
TFXLMWithLMHeadModel,
get_masks,
)
@@ -123,6 +124,8 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
super().__init__(config, *inputs, **kwargs)
self.layerdrop = getattr(config, "layerdrop", 0.0)
self.pre_norm = getattr(config, "pre_norm", False)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
def call(
self,
@@ -135,9 +138,9 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
cache=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
training=False,
output_attentions=False,
output_hidden_states=False,
):
# removed: src_enc=None, src_len=None
if isinstance(inputs, (tuple, list)):
@@ -150,7 +153,9 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
cache = inputs[6] if len(inputs) > 6 else cache
head_mask = inputs[7] if len(inputs) > 7 else head_mask
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
assert len(inputs) <= 9, "Too many inputs."
output_attentions = inputs[9] if len(inputs) > 9 else output_attentions
output_hidden_states = inputs[10] if len(inputs) > 10 else output_hidden_states
assert len(inputs) <= 11, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask)
@@ -161,10 +166,15 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
cache = inputs.get("cache", cache)
head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
assert len(inputs) <= 9, "Too many inputs."
output_attentions = inputs.get("output_attentions", output_attentions)
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
assert len(inputs) <= 11, "Too many inputs."
else:
input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
@@ -257,9 +267,12 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
# self attention
if not self.pre_norm:
attn_outputs = self.attentions[i]([tensor, attn_mask, None, cache, head_mask[i]], training=training)
attn_outputs = self.attentions[i](
[tensor, attn_mask, None, cache, head_mask[i], output_attentions], training=training
)
attn = attn_outputs[0]
attentions = attentions + (attn_outputs[1],)
if cast_bool_to_primitive(output_attentions, self.output_attentions) is True:
attentions = attentions + (attn_outputs[1],)
attn = self.dropout(attn, training=training)
tensor = tensor + attn
tensor = self.layer_norm1[i](tensor)
@@ -269,7 +282,7 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
[tensor_normalized, attn_mask, None, cache, head_mask[i]], training=training
)
attn = attn_outputs[0]
if output_attentions:
if cast_bool_to_primitive(output_attentions, self.output_attentions) is True:
attentions = attentions + (attn_outputs[1],)
attn = self.dropout(attn, training=training)
tensor = tensor + attn
@@ -292,7 +305,7 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
tensor = tensor * mask[..., tf.newaxis]
# Add last hidden state
if output_hidden_states:
if cast_bool_to_primitive(output_hidden_states, self.output_hidden_states) is True:
hidden_states = hidden_states + (tensor,)
# update cache length
@@ -303,9 +316,9 @@ class TFFlaubertMainLayer(TFXLMMainLayer):
# tensor = tensor.transpose(0, 1)
outputs = (tensor,)
if output_hidden_states:
if cast_bool_to_primitive(output_hidden_states, self.output_hidden_states) is True:
outputs = outputs + (hidden_states,)
if output_attentions:
if cast_bool_to_primitive(output_attentions, self.output_attentions) is True:
outputs = outputs + (attentions,)
return outputs # outputs, (hidden_states), (attentions)
@@ -321,6 +334,7 @@ class TFFlaubertWithLMHeadModel(TFXLMWithLMHeadModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFFlaubertMainLayer(config, name="transformer")
self.pred_layer = TFXLMPredLayer(config, self.transformer.embeddings, name="pred_layer_._proj")
@add_start_docstrings(

30
src/transformers/modeling_tf_xlm.py
View File

@@ -19,6 +19,7 @@
import itertools
import logging
import math
import warnings
import numpy as np
import tensorflow as tf
@@ -827,6 +828,9 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
self.transformer = TFXLMMainLayer(config, name="transformer")
self.sequence_summary = TFSequenceSummary(config, initializer_range=config.init_std, name="sequence_summary")
self.logits_proj = tf.keras.layers.Dense(
1, kernel_initializer=get_initializer(config.initializer_range), name="logits_proj"
)
@property
def dummy_inputs(self):
@@ -835,7 +839,10 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
return {
"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS),
"langs": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS),
}
@add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="xlm-mlm-en-2048")
@@ -892,7 +899,7 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
output_attentions = inputs[9] if len(inputs) > 9 else output_attentions
output_hidden_states = inputs[10] if len(inputs) > 10 else output_hidden_states
labels = inputs[11] if len(inputs) > 11 else labels
assert len(inputs) <= 11, "Too many inputs."
assert len(inputs) <= 12, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask)
@@ -921,17 +928,31 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
flat_langs = tf.reshape(langs, (-1, seq_length)) if langs is not None else None
flat_inputs_embeds = (
tf.reshape(inputs_embeds, (-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1]))
if inputs_embeds is not None
else None
)
if lengths is not None:
warnings.warn(
"The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the "
"attention mask instead.",
FutureWarning,
)
lengths = None
flat_inputs = [
flat_input_ids,
flat_attention_mask,
langs,
flat_langs,
flat_token_type_ids,
flat_position_ids,
lengths,
cache,
head_mask,
inputs_embeds,
flat_inputs_embeds,
output_attentions,
output_hidden_states,
]
@@ -939,6 +960,7 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
transformer_outputs = self.transformer(flat_inputs, training=training)
output = transformer_outputs[0]
logits = self.sequence_summary(output)
logits = self.logits_proj(logits)
reshaped_logits = tf.reshape(logits, (-1, num_choices))
outputs = (reshaped_logits,) + transformer_outputs[1:] # add hidden states and attention if they are here

104
src/transformers/modeling_xlm.py
View File

@@ -19,6 +19,7 @@
import itertools
import logging
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
@@ -40,6 +41,7 @@ from .file_utils import (
from .modeling_outputs import (
BaseModelOutput,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
@@ -1122,3 +1124,105 @@ class XLMForTokenClassification(XLMPreTrainedModel):
return TokenClassifierOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
)
@add_start_docstrings(
"""XLM Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
XLM_START_DOCSTRING,
)
class XLMForMultipleChoice(XLMPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = XLMModel(config)
self.sequence_summary = SequenceSummary(config)
self.logits_proj = nn.Linear(config.num_labels, 1)
self.init_weights()
@add_start_docstrings_to_callable(XLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="xlm-mlm-en-2048",
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
langs=None,
token_type_ids=None,
position_ids=None,
lengths=None,
cache=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_tuple=None,
):
r"""
labels (:obj:`torch.Tensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
Labels for computing the multiple choice classification loss.
Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
of the input tensors. (see `input_ids` above)
"""
return_tuple = return_tuple if return_tuple is not None else self.config.use_return_tuple
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
langs = langs.view(-1, langs.size(-1)) if langs is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
if lengths is not None:
warnings.warn(
"The `lengths` parameter cannot be used with the XLM multiple choice models. Please use the "
"attention mask instead.",
FutureWarning,
)
lengths = None
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_tuple=return_tuple,
)
output = transformer_outputs[0]
logits = self.sequence_summary(output)
logits = self.logits_proj(logits)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if return_tuple:
output = (reshaped_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)

2
tests/test_modeling_common.py
View File

@@ -66,7 +66,7 @@ class ModelTesterMixin:
if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
return {
k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
if isinstance(v, torch.Tensor) and v.ndim != 0
if isinstance(v, torch.Tensor) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}

47
tests/test_modeling_flaubert.py
View File

@@ -32,6 +32,7 @@ if is_torch_available():
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
from transformers.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -90,6 +91,7 @@ class FlaubertModelTester(object):
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)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = FlaubertConfig(
vocab_size=self.vocab_size,
@@ -118,6 +120,7 @@ class FlaubertModelTester(object):
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
@@ -133,6 +136,7 @@ class FlaubertModelTester(object):
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = FlaubertModel(config=config)
@@ -158,6 +162,7 @@ class FlaubertModelTester(object):
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = FlaubertWithLMHeadModel(config)
@@ -183,6 +188,7 @@ class FlaubertModelTester(object):
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = FlaubertForQuestionAnsweringSimple(config)
@@ -212,6 +218,7 @@ class FlaubertModelTester(object):
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = FlaubertForQuestionAnswering(config)
@@ -278,6 +285,7 @@ class FlaubertModelTester(object):
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = FlaubertForSequenceClassification(config)
@@ -304,6 +312,7 @@ class FlaubertModelTester(object):
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_labels = self.num_labels
@@ -319,6 +328,38 @@ class FlaubertModelTester(object):
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
def create_and_check_flaubert_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_choices = self.num_choices
model = FlaubertForMultipleChoice(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()
loss, logits = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices])
self.check_loss_output(result)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
@@ -329,6 +370,7 @@ class FlaubertModelTester(object):
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
@@ -346,6 +388,7 @@ class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
@@ -382,6 +425,10 @@ class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*config_and_inputs)
def test_flaubert_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

4
tests/test_modeling_tf_common.py
View File

@@ -80,8 +80,8 @@ class TFModelTesterMixin:
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
if model_class in TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
inputs_dict = {
k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices, 1))
if isinstance(v, tf.Tensor) and v.ndim != 0
k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
if isinstance(v, tf.Tensor) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}

331
tests/test_modeling_tf_flaubert.py
View File

@@ -18,11 +18,340 @@ 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 tensorflow as tf
import numpy as np
from transformers import TFFlaubertModel
from transformers import (
FlaubertConfig,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class TFFlaubertModelTester:
def __init__(
self, parent,
):
self.parent = parent
self.batch_size = 13
self.seq_length = 7
self.is_training = True
self.use_input_lengths = True
self.use_token_type_ids = True
self.use_labels = True
self.gelu_activation = True
self.sinusoidal_embeddings = False
self.causal = False
self.asm = False
self.n_langs = 2
self.vocab_size = 99
self.n_special = 0
self.hidden_size = 32
self.num_hidden_layers = 5
self.num_attention_heads = 4
self.hidden_dropout_prob = 0.1
self.attention_probs_dropout_prob = 0.1
self.max_position_embeddings = 512
self.type_vocab_size = 16
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_choices = 4
self.summary_type = "last"
self.use_proj = True
self.scope = None
self.bos_token_id = 0
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = ids_tensor([self.batch_size, self.seq_length], 2, dtype=tf.float32)
input_lengths = None
if self.use_input_lengths:
input_lengths = (
ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
) # small variation of seq_length
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)
sequence_labels = None
token_labels = None
is_impossible_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)
is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = FlaubertConfig(
vocab_size=self.vocab_size,
n_special=self.n_special,
emb_dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
gelu_activation=self.gelu_activation,
sinusoidal_embeddings=self.sinusoidal_embeddings,
asm=self.asm,
causal=self.causal,
n_langs=self.n_langs,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
summary_type=self.summary_type,
use_proj=self.use_proj,
bos_token_id=self.bos_token_id,
)
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def create_and_check_flaubert_model(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = TFFlaubertModel(config=config)
inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
outputs = model(inputs)
inputs = [input_ids, input_mask]
outputs = model(inputs)
sequence_output = outputs[0]
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size]
)
def create_and_check_flaubert_lm_head(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = TFFlaubertWithLMHeadModel(config)
inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
outputs = model(inputs)
logits = outputs[0]
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_flaubert_qa(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = TFFlaubertForQuestionAnsweringSimple(config)
inputs = {"input_ids": input_ids, "lengths": input_lengths}
start_logits, end_logits = model(inputs)
result = {
"start_logits": start_logits.numpy(),
"end_logits": end_logits.numpy(),
}
self.parent.assertListEqual(list(result["start_logits"].shape), [self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].shape), [self.batch_size, self.seq_length])
def create_and_check_flaubert_sequence_classif(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = TFFlaubertForSequenceClassification(config)
inputs = {"input_ids": input_ids, "lengths": input_lengths}
(logits,) = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.type_sequence_label_size])
def create_and_check_flaubert_for_token_classification(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_labels = self.num_labels
model = TFFlaubertForTokenClassification(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
(logits,) = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels])
def create_and_check_flaubert_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_choices = self.num_choices
model = TFFlaubertForMultipleChoice(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,
}
(logits,) = model(inputs)
result = {"logits": logits.numpy()}
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"langs": token_type_ids,
"lengths": input_lengths,
}
return config, inputs_dict
@require_tf
class TFFlaubertModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
)
if is_tf_available()
else ()
)
all_generative_model_classes = (
(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
def setUp(self):
self.model_tester = TFFlaubertModelTester(self)
self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_flaubert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*config_and_inputs)
def test_flaubert_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*config_and_inputs)
def test_flaubert_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*config_and_inputs)
def test_flaubert_sequence_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*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_flaubert_for_token_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_flaubert_for_multiple_choice(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFFlaubertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_tf

41
tests/test_modeling_tf_xlm.py
View File

@@ -32,6 +32,7 @@ if is_tf_available():
TFXLMForSequenceClassification,
TFXLMForQuestionAnsweringSimple,
TFXLMForTokenClassification,
TFXLMForMultipleChoice,
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
)
@@ -91,6 +92,7 @@ class TFXLMModelTester:
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)
is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = XLMConfig(
vocab_size=self.vocab_size,
@@ -120,6 +122,7 @@ class TFXLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
@@ -132,6 +135,7 @@ class TFXLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = TFXLMModel(config=config)
@@ -157,6 +161,7 @@ class TFXLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = TFXLMWithLMHeadModel(config)
@@ -181,6 +186,7 @@ class TFXLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = TFXLMForQuestionAnsweringSimple(config)
@@ -206,6 +212,7 @@ class TFXLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = TFXLMForSequenceClassification(config)
@@ -229,6 +236,7 @@ class TFXLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_labels = self.num_labels
@@ -240,6 +248,32 @@ class TFXLMModelTester:
}
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels])
def create_and_check_xlm_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_choices = self.num_choices
model = TFXLMForMultipleChoice(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,
}
(logits,) = model(inputs)
result = {"logits": logits.numpy()}
self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
@@ -250,6 +284,7 @@ class TFXLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
) = config_and_inputs
inputs_dict = {
@@ -265,13 +300,13 @@ class TFXLMModelTester:
class TFXLMModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
# TODO The multiple choice model is missing and should be added.
(
TFXLMModel,
TFXLMWithLMHeadModel,
TFXLMForSequenceClassification,
TFXLMForQuestionAnsweringSimple,
TFXLMForTokenClassification,
TFXLMForMultipleChoice,
)
if is_tf_available()
else ()
@@ -307,6 +342,10 @@ class TFXLMModelTest(TFModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_token_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_xlm_for_multiple_choice(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

50
tests/test_modeling_xlm.py
View File

@@ -33,6 +33,7 @@ if is_torch_available():
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForMultipleChoice,
)
from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -63,7 +64,7 @@ class XLMModelTester:
self.max_position_embeddings = 512
self.type_sequence_label_size = 2
self.initializer_range = 0.02
self.num_labels = 3
self.num_labels = 2
self.num_choices = 4
self.summary_type = "last"
self.use_proj = True
@@ -91,6 +92,7 @@ class XLMModelTester:
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)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = XLMConfig(
vocab_size=self.vocab_size,
@@ -109,6 +111,7 @@ class XLMModelTester:
initializer_range=self.initializer_range,
summary_type=self.summary_type,
use_proj=self.use_proj,
num_labels=self.num_labels,
bos_token_id=self.bos_token_id,
)
@@ -120,6 +123,7 @@ class XLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
@@ -135,6 +139,7 @@ class XLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMModel(config=config)
@@ -160,6 +165,7 @@ class XLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMWithLMHeadModel(config)
@@ -185,6 +191,7 @@ class XLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForQuestionAnsweringSimple(config)
@@ -214,6 +221,7 @@ class XLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForQuestionAnswering(config)
@@ -280,6 +288,7 @@ class XLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForSequenceClassification(config)
@@ -306,6 +315,7 @@ class XLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_labels = self.num_labels
@@ -321,6 +331,38 @@ class XLMModelTester:
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
def create_and_check_xlm_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_choices = self.num_choices
model = XLMForMultipleChoice(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()
loss, logits = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices])
self.check_loss_output(result)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
@@ -331,6 +373,7 @@ class XLMModelTester:
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
@@ -348,6 +391,7 @@ class XLMModelTest(ModelTesterMixin, unittest.TestCase):
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
@@ -387,6 +431,10 @@ class XLMModelTest(ModelTesterMixin, unittest.TestCase):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*config_and_inputs)
def test_xlm_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: