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Transfoxl seq classification (#8868)

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* Transfoxl sequence classification

* Transfoxl sequence classification
This commit is contained in:
sandip
2020-12-02 20:38:32 +05:30
committed by GitHub
parent 24f0c2fe33
commit f6b44e6190
7 changed files with 179 additions and 3 deletions
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21
tests/test_modeling_transfo_xl.py
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@@ -27,7 +27,7 @@ from .test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, TransfoXLModel
from transformers import TransfoXLConfig, TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel
from transformers.models.transfo_xl.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -56,6 +56,8 @@ class TransfoXLModelTester:
self.scope = None
self.seed = 1
self.eos_token_id = 0
self.num_labels = 3
self.pad_token_id = self.vocab_size - 1
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -78,6 +80,7 @@ class TransfoXLModelTester:
div_val=self.div_val,
n_layer=self.num_hidden_layers,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
)
return (config, input_ids_1, input_ids_2, lm_labels)
@@ -148,6 +151,14 @@ class TransfoXLModelTester:
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
)
def create_and_check_transfo_xl_for_sequence_classification(self, config, input_ids_1, input_ids_2, lm_labels):
config.num_labels = self.num_labels
model = TransfoXLForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
@@ -157,7 +168,9 @@ class TransfoXLModelTester:
@require_torch
class TransfoXLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else ()
all_model_classes = (
(TransfoXLModel, TransfoXLLMHeadModel, TransfoXLForSequenceClassification) if is_torch_available() else ()
)
all_generative_model_classes = (TransfoXLLMHeadModel,) if is_torch_available() else ()
test_pruning = False
test_torchscript = False
@@ -204,6 +217,10 @@ class TransfoXLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestC
output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
self.model_tester.check_transfo_xl_lm_head_output(output_result)
def test_transfo_xl_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# xlnet cannot keep gradients in attentions or hidden states
return