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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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47
tests/test_modeling_flaubert.py
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@@ -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]: