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Commit 40ed7172 authored by erenup's avatar erenup
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Merge remote-tracking branch 'refs/remotes/huggingface/master'

parents 86a63070 7296f101
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Showing with 605 additions and 260 deletions
transformers/tests/modeling_tf_bert_test.py
View file @ 40ed7172
......@@ -18,11 +18,11 @@ from __future__ import print_function
import unittest
import shutil
import pytest
import sys
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow
from transformers import BertConfig, is_tf_available
......@@ -36,10 +36,9 @@ if is_tf_available():
TFBertForTokenClassification,
TFBertForQuestionAnswering,
TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
@require_tf
class TFBertModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFBertModel, TFBertForMaskedLM, TFBertForNextSentencePrediction,
......@@ -131,10 +130,6 @@ class TFBertModelTest(TFCommonTestCases.TFCommonModelTester):
def create_and_check_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFBertModel(config=config)
# inputs = {'input_ids': input_ids,
# 'attention_mask': input_mask,
# 'token_type_ids': token_type_ids}
# sequence_output, pooled_output = model(**inputs)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
......@@ -313,7 +308,7 @@ class TFBertModelTest(TFCommonTestCases.TFCommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_token_classification(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_tf_common_test.py
View file @ 40ed7172
......@@ -25,18 +25,17 @@ import unittest
import uuid
import tempfile
import pytest
import sys
from transformers import is_tf_available, is_torch_available
from .utils import require_tf, slow
if is_tf_available():
import tensorflow as tf
import numpy as np
from transformers import TFPreTrainedModel
# from transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
if sys.version_info[0] == 2:
import cPickle as pickle
......@@ -62,6 +61,7 @@ def _config_zero_init(config):
class TFCommonTestCases:
@require_tf
class TFCommonModelTester(unittest.TestCase):
model_tester = None
......@@ -233,80 +233,6 @@ class TFCommonTestCases:
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
def test_headmasking(self):
pass
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# config.output_attentions = True
# config.output_hidden_states = True
# configs_no_init = _config_zero_init(config) # To be sure we have no Nan
# for model_class in self.all_model_classes:
# model = model_class(config=configs_no_init)
# model.eval()
# # Prepare head_mask
# # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
# head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads)
# head_mask[0, 0] = 0
# head_mask[-1, :-1] = 0
# head_mask.requires_grad_(requires_grad=True)
# inputs = inputs_dict.copy()
# inputs['head_mask'] = head_mask
# outputs = model(**inputs)
# # Test that we can get a gradient back for importance score computation
# output = sum(t.sum() for t in outputs[0])
# output = output.sum()
# output.backward()
# multihead_outputs = head_mask.grad
# attentions = outputs[-1]
# hidden_states = outputs[-2]
# # Remove Nan
# self.assertIsNotNone(multihead_outputs)
# self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
# self.assertAlmostEqual(
# attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
# self.assertNotEqual(
# attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
# self.assertNotEqual(
# attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
# self.assertAlmostEqual(
# attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
# self.assertNotEqual(
# attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
def test_head_pruning(self):
pass
# if not self.test_pruning:
# return
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# for model_class in self.all_model_classes:
# config.output_attentions = True
# config.output_hidden_states = False
# model = model_class(config=config)
# model.eval()
# heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
# -1: [0]}
# model.prune_heads(heads_to_prune)
# outputs = model(**inputs_dict)
# attentions = outputs[-1]
# self.assertEqual(
# attentions[0].shape[-3], 1)
# self.assertEqual(
# attentions[1].shape[-3], self.model_tester.num_attention_heads)
# self.assertEqual(
# attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
......@@ -323,75 +249,14 @@ class TFCommonTestCases:
list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size])
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def test_resize_tokens_embeddings(self):
pass
# original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# if not self.test_resize_embeddings:
# return
# for model_class in self.all_model_classes:
# config = copy.deepcopy(original_config)
# model = model_class(config)
# model_vocab_size = config.vocab_size
# # Retrieve the embeddings and clone theme
# model_embed = model.resize_token_embeddings(model_vocab_size)
# cloned_embeddings = model_embed.weight.clone()
# # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
# model_embed = model.resize_token_embeddings(model_vocab_size + 10)
# self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# # Check that it actually resizes the embeddings matrix
# self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
# model_embed = model.resize_token_embeddings(model_vocab_size - 15)
# self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# # Check that it actually resizes the embeddings matrix
# self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# # Check that adding and removing tokens has not modified the first part of the embedding matrix.
# models_equal = True
# for p1, p2 in zip(cloned_embeddings, model_embed.weight):
# if p1.data.ne(p2.data).sum() > 0:
# models_equal = False
# self.assertTrue(models_equal)
def test_tie_model_weights(self):
pass
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# def check_same_values(layer_1, layer_2):
# equal = True
# for p1, p2 in zip(layer_1.weight, layer_2.weight):
# if p1.data.ne(p2.data).sum() > 0:
# equal = False
# return equal
# for model_class in self.all_model_classes:
# if not hasattr(model_class, 'tie_weights'):
# continue
# config.torchscript = True
# model_not_tied = model_class(config)
# params_not_tied = list(model_not_tied.parameters())
# config_tied = copy.deepcopy(config)
# config_tied.torchscript = False
# model_tied = model_class(config_tied)
# params_tied = list(model_tied.parameters())
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertGreater(len(params_not_tied), len(params_tied))
# # Check that after resize they remain tied.
# model_tied.resize_token_embeddings(config.vocab_size + 10)
# params_tied_2 = list(model_tied.parameters())
# self.assertGreater(len(params_not_tied), len(params_tied))
# self.assertEqual(len(params_tied_2), len(params_tied))
for model_class in self.all_model_classes:
model = model_class(config)
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
x = model.get_output_embeddings()
assert x is None or isinstance(x, tf.keras.layers.Layer)
def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
......@@ -401,6 +266,35 @@ class TFCommonTestCases:
first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0]
self.assertTrue(tf.math.equal(first, second).numpy().all())
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"]
del inputs_dict["input_ids"]
for model_class in self.all_model_classes:
model = model_class(config)
wte = model.get_input_embeddings()
try:
x = wte(input_ids, mode="embedding")
except:
try:
x = wte([input_ids], mode="embedding")
except:
try:
x = wte([input_ids, None, None, None], mode="embedding")
except:
if hasattr(self.model_tester, "embedding_size"):
x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32)
else:
x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
# ^^ In our TF models, the input_embeddings can take slightly different forms,
# so we try a few of them.
# We used to fall back to just synthetically creating a dummy tensor of ones:
#
inputs_dict["inputs_embeds"] = x
outputs = model(inputs_dict)
def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
......@@ -422,29 +316,5 @@ def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
return output
class TFModelUtilsTest(unittest.TestCase):
@pytest.mark.skipif('tensorflow' not in sys.modules, reason="requires TensorFlow")
def test_model_from_pretrained(self):
pass
# logging.basicConfig(level=logging.INFO)
# for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# config = BertConfig.from_pretrained(model_name)
# self.assertIsNotNone(config)
# self.assertIsInstance(config, PretrainedConfig)
# model = BertModel.from_pretrained(model_name)
# model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
# self.assertIsNotNone(model)
# self.assertIsInstance(model, PreTrainedModel)
# for value in loading_info.values():
# self.assertEqual(len(value), 0)
# config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# self.assertEqual(model.config.output_attentions, True)
# self.assertEqual(model.config.output_hidden_states, True)
# self.assertEqual(model.config, config)
if __name__ == "__main__":
unittest.main()
transformers/tests/modeling_tf_ctrl_test.py
View file @ 40ed7172
......@@ -18,11 +18,11 @@ from __future__ import print_function
import unittest
import shutil
import pytest
import sys
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow
from transformers import CTRLConfig, is_tf_available
......@@ -30,10 +30,9 @@ if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_ctrl import (TFCTRLModel, TFCTRLLMHeadModel,
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
@require_tf
class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel) if is_tf_available() else ()
......@@ -188,7 +187,7 @@ class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_lm_head(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_tf_distilbert_test.py
View file @ 40ed7172
......@@ -17,10 +17,10 @@ from __future__ import division
from __future__ import print_function
import unittest
import pytest
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow
from transformers import DistilBertConfig, is_tf_available
......@@ -30,10 +30,9 @@ if is_tf_available():
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
@require_tf
class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFDistilBertModel, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering,
......@@ -210,7 +209,7 @@ class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*config_and_inputs)
# @pytest.mark.slow
# @slow
# def test_model_from_pretrained(self):
# cache_dir = "/tmp/transformers_test/"
# for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_tf_gpt2_test.py
View file @ 40ed7172
......@@ -18,11 +18,11 @@ from __future__ import print_function
import unittest
import shutil
import pytest
import sys
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow
from transformers import GPT2Config, is_tf_available
......@@ -31,10 +31,9 @@ if is_tf_available():
from transformers.modeling_tf_gpt2 import (TFGPT2Model, TFGPT2LMHeadModel,
TFGPT2DoubleHeadsModel,
TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
@require_tf
class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFGPT2Model, TFGPT2LMHeadModel,
......@@ -219,7 +218,7 @@ class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gpt2_double_head(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_tf_openai_gpt_test.py
View file @ 40ed7172
......@@ -18,11 +18,11 @@ from __future__ import print_function
import unittest
import shutil
import pytest
import sys
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow
from transformers import OpenAIGPTConfig, is_tf_available
......@@ -31,10 +31,9 @@ if is_tf_available():
from transformers.modeling_tf_openai import (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel,
TFOpenAIGPTDoubleHeadsModel,
TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
@require_tf
class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel,
......@@ -218,7 +217,7 @@ class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_double_head(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_tf_roberta_test.py
View file @ 40ed7172
......@@ -18,10 +18,10 @@ from __future__ import print_function
import unittest
import shutil
import pytest
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow
from transformers import RobertaConfig, is_tf_available
......@@ -30,11 +30,11 @@ if is_tf_available():
import numpy
from transformers.modeling_tf_roberta import (TFRobertaModel, TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
@require_tf
class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFRobertaModel,TFRobertaForMaskedLM,
......@@ -154,6 +154,20 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def create_and_check_roberta_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = TFRobertaForTokenClassification(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 prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_mask,
......@@ -176,7 +190,7 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_for_masked_lm(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......@@ -188,7 +202,7 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
class TFRobertaModelIntegrationTest(unittest.TestCase):
@pytest.mark.slow
@slow
def test_inference_masked_lm(self):
model = TFRobertaForMaskedLM.from_pretrained('roberta-base')
......@@ -209,7 +223,7 @@ class TFRobertaModelIntegrationTest(unittest.TestCase):
numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)
)
@pytest.mark.slow
@slow
def test_inference_no_head(self):
model = TFRobertaModel.from_pretrained('roberta-base')
......@@ -225,7 +239,7 @@ class TFRobertaModelIntegrationTest(unittest.TestCase):
numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)
)
@pytest.mark.slow
@slow
def test_inference_classification_head(self):
model = TFRobertaForSequenceClassification.from_pretrained('roberta-large-mnli')
......
transformers/tests/modeling_tf_transfo_xl_test.py
View file @ 40ed7172
......@@ -19,10 +19,10 @@ from __future__ import print_function
import unittest
import random
import shutil
import pytest
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow
from transformers import TransfoXLConfig, is_tf_available
......@@ -31,10 +31,9 @@ if is_tf_available():
from transformers.modeling_tf_transfo_xl import (TFTransfoXLModel,
TFTransfoXLLMHeadModel,
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
@require_tf
class TFTransfoXLModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFTransfoXLModel, TFTransfoXLLMHeadModel) if is_tf_available() else ()
......@@ -204,7 +203,7 @@ class TFTransfoXLModelTest(TFCommonTestCases.TFCommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_tf_xlm_test.py
View file @ 40ed7172
......@@ -18,7 +18,6 @@ from __future__ import print_function
import unittest
import shutil
import pytest
from transformers import is_tf_available
......@@ -29,13 +28,13 @@ if is_tf_available():
TFXLMForSequenceClassification,
TFXLMForQuestionAnsweringSimple,
TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow
@require_tf
class TFXLMModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFXLMModel, TFXLMWithLMHeadModel,
......@@ -251,7 +250,7 @@ class TFXLMModelTest(TFCommonTestCases.TFCommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_tf_xlnet_test.py
View file @ 40ed7172
......@@ -21,7 +21,6 @@ import unittest
import json
import random
import shutil
import pytest
from transformers import XLNetConfig, is_tf_available
......@@ -30,18 +29,21 @@ if is_tf_available():
from transformers.modeling_tf_xlnet import (TFXLNetModel, TFXLNetLMHeadModel,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetForQuestionAnsweringSimple,
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_tf, slow
@require_tf
class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes=(TFXLNetModel, TFXLNetLMHeadModel,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetForQuestionAnsweringSimple) if is_tf_available() else ()
test_pruning = False
......@@ -258,6 +260,26 @@ class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester):
list(list(mem.shape) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_for_token_classification(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
config.num_labels = input_ids_1.shape[1]
model = TFXLNetForTokenClassification(config)
inputs = {'input_ids': input_ids_1,
'attention_mask': input_mask,
# 'token_type_ids': token_type_ids
}
logits, mems_1 = model(inputs)
result = {
"mems_1": [mem.numpy() for mem in mems_1],
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(result["logits"].shape),
[self.batch_size, self.seq_length, config.num_labels])
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
......@@ -289,12 +311,16 @@ class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
def test_xlnet_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_for_token_classification(*config_and_inputs)
def test_xlnet_qa(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_transfo_xl_test.py
View file @ 40ed7172
......@@ -19,7 +19,6 @@ from __future__ import print_function
import unittest
import random
import shutil
import pytest
from transformers import is_torch_available
......@@ -27,12 +26,13 @@ if is_torch_available():
import torch
from transformers import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel)
from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_torch, slow, torch_device
@require_torch
class TransfoXLModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else ()
......@@ -111,6 +111,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester):
def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
model = TransfoXLModel(config)
model.to(torch_device)
model.eval()
hidden_states_1, mems_1 = model(input_ids_1)
......@@ -140,6 +141,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester):
def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
model = TransfoXLLMHeadModel(config)
model.to(torch_device)
model.eval()
lm_logits_1, mems_1 = model(input_ids_1)
......@@ -204,7 +206,7 @@ class TransfoXLModelTest(CommonTestCases.CommonModelTester):
output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
self.model_tester.check_transfo_xl_lm_head_output(output_result)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_xlm_test.py
View file @ 40ed7172
......@@ -18,7 +18,6 @@ from __future__ import print_function
import unittest
import shutil
import pytest
from transformers import is_torch_available
......@@ -26,13 +25,13 @@ if is_torch_available():
from transformers import (XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering,
XLMForSequenceClassification, XLMForQuestionAnsweringSimple)
from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_torch, slow, torch_device
@require_torch
class XLMModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering,
......@@ -148,6 +147,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
def create_and_check_xlm_model(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMModel(config=config)
model.to(torch_device)
model.eval()
outputs = model(input_ids, lengths=input_lengths, langs=token_type_ids)
outputs = model(input_ids, langs=token_type_ids)
......@@ -163,6 +163,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
def create_and_check_xlm_lm_head(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMWithLMHeadModel(config)
model.to(torch_device)
model.eval()
loss, logits = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
......@@ -182,6 +183,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
def create_and_check_xlm_simple_qa(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMForQuestionAnsweringSimple(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
......@@ -206,6 +208,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
def create_and_check_xlm_qa(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMForQuestionAnswering(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
......@@ -260,6 +263,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
def create_and_check_xlm_sequence_classif(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
model = XLMForSequenceClassification(config)
model.to(torch_device)
model.eval()
(logits,) = model(input_ids)
......@@ -312,7 +316,7 @@ class XLMModelTest(CommonTestCases.CommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/modeling_xlnet_test.py
View file @ 40ed7172
......@@ -21,24 +21,25 @@ import unittest
import json
import random
import shutil
import pytest
from transformers import is_torch_available
if is_torch_available():
import torch
from transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering)
from transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification,
XLNetForTokenClassification, XLNetForQuestionAnswering)
from transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
else:
pytestmark = pytest.mark.skip("Require Torch")
from .modeling_common_test import (CommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from .utils import require_torch, slow, torch_device
@require_torch
class XLNetModelTest(CommonTestCases.CommonModelTester):
all_model_classes=(XLNetModel, XLNetLMHeadModel,
all_model_classes=(XLNetModel, XLNetLMHeadModel, XLNetForTokenClassification,
XLNetForSequenceClassification, XLNetForQuestionAnswering) if is_torch_available() else ()
test_pruning = False
......@@ -99,18 +100,20 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
input_mask = ids_tensor([self.batch_size, self.seq_length], 2).float()
input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
perm_mask = torch.zeros(self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float)
perm_mask = torch.zeros(self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float, device=torch_device)
perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float)
target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float, device=torch_device)
target_mapping[:, 0, -1] = 1.0 # predict last token
sequence_labels = None
lm_labels = None
is_impossible_labels = None
token_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = XLNetConfig(
vocab_size_or_config_json_file=self.vocab_size,
......@@ -129,15 +132,16 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
num_labels=self.type_sequence_label_size)
return (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels)
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels)
def set_seed(self):
random.seed(self.seed)
torch.manual_seed(self.seed)
def create_and_check_xlnet_base_model(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
model = XLNetModel(config)
model.to(torch_device)
model.eval()
_, _ = model(input_ids_1, input_mask=input_mask)
......@@ -152,6 +156,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
config.mem_len = 0
model = XLNetModel(config)
model.to(torch_device)
model.eval()
no_mems_outputs = model(input_ids_1)
self.parent.assertEqual(len(no_mems_outputs), 1)
......@@ -163,9 +168,23 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
list(list(mem.size()) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_base_model_with_att_output(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
model = XLNetModel(config)
model.to(torch_device)
model.eval()
_, _, attentions = model(input_ids_1, target_mapping=target_mapping)
self.parent.assertEqual(len(attentions), config.n_layer)
self.parent.assertIsInstance(attentions[0], tuple)
self.parent.assertEqual(len(attentions[0]), 2)
self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape)
def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
model = XLNetLMHeadModel(config)
model.to(torch_device)
model.eval()
loss_1, all_logits_1, mems_1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
......@@ -204,8 +223,9 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_qa(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
model = XLNetForQuestionAnswering(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids_1)
......@@ -261,9 +281,43 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
list(list(mem.size()) for mem in result["mems"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_token_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
model = XLNetForTokenClassification(config)
model.to(torch_device)
model.eval()
logits, mems_1 = model(input_ids_1)
loss, logits, mems_1 = model(input_ids_1, labels=token_labels)
result = {
"loss": loss,
"mems_1": mems_1,
"logits": logits,
}
self.parent.assertListEqual(
list(result["loss"].size()),
[])
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.type_sequence_label_size])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels,
sequence_labels, is_impossible_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids_1}
return config, inputs_dict
def create_and_check_xlnet_sequence_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
target_mapping, segment_ids, lm_labels, sequence_labels, is_impossible_labels, token_labels):
model = XLNetForSequenceClassification(config)
model.to(torch_device)
model.eval()
logits, mems_1 = model(input_ids_1)
......@@ -289,7 +343,7 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, segment_ids, lm_labels,
sequence_labels, is_impossible_labels) = config_and_inputs
sequence_labels, is_impossible_labels, token_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids_1}
return config, inputs_dict
......@@ -306,6 +360,12 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
def test_xlnet_base_model_with_att_output(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
config_and_inputs[0].output_attentions = True
self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs)
def test_xlnet_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
......@@ -316,12 +376,17 @@ class XLNetModelTest(CommonTestCases.CommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
def test_xlnet_token_classif(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs)
def test_xlnet_qa(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
@pytest.mark.slow
@slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in list(XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
......
transformers/tests/optimization_test.py
View file @ 40ed7172
......@@ -18,19 +18,21 @@ from __future__ import print_function
import unittest
import os
import pytest
from transformers import is_torch_available
if is_torch_available():
import torch
from transformers import (AdamW, ConstantLRSchedule, WarmupConstantSchedule,
WarmupCosineSchedule, WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
else:
pytestmark = pytest.mark.skip("Require Torch")
from transformers import (AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup)
from .tokenization_tests_commons import TemporaryDirectory
from .utils import require_torch
def unwrap_schedule(scheduler, num_steps=10):
......@@ -54,6 +56,7 @@ def unwrap_and_save_reload_schedule(scheduler, num_steps=10):
scheduler.load_state_dict(state_dict)
return lrs
@require_torch
class OptimizationTest(unittest.TestCase):
def assertListAlmostEqual(self, list1, list2, tol):
......@@ -76,6 +79,7 @@ class OptimizationTest(unittest.TestCase):
self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
@require_torch
class ScheduleInitTest(unittest.TestCase):
m = torch.nn.Linear(50, 50) if is_torch_available() else None
optimizer = AdamW(m.parameters(), lr=10.) if is_torch_available() else None
......@@ -87,59 +91,60 @@ class ScheduleInitTest(unittest.TestCase):
self.assertAlmostEqual(a, b, delta=tol)
def test_constant_scheduler(self):
scheduler = ConstantLRSchedule(self.optimizer)
scheduler = get_constant_schedule(self.optimizer)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [10.] * self.num_steps
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = ConstantLRSchedule(self.optimizer)
scheduler = get_constant_schedule(self.optimizer)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_constant_scheduler(self):
scheduler = WarmupConstantSchedule(self.optimizer, warmup_steps=4)
scheduler = get_constant_schedule_with_warmup(self.optimizer, num_warmup_steps=4)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = WarmupConstantSchedule(self.optimizer, warmup_steps=4)
scheduler = get_constant_schedule_with_warmup(self.optimizer, num_warmup_steps=4)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_linear_scheduler(self):
scheduler = WarmupLinearSchedule(self.optimizer, warmup_steps=2, t_total=10)
scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25, 0.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = WarmupLinearSchedule(self.optimizer, warmup_steps=2, t_total=10)
scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_cosine_scheduler(self):
scheduler = WarmupCosineSchedule(self.optimizer, warmup_steps=2, t_total=10)
scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38, 0.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
scheduler = WarmupCosineSchedule(self.optimizer, warmup_steps=2, t_total=10)
scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_cosine_hard_restart_scheduler(self):
scheduler = WarmupCosineWithHardRestartsSchedule(self.optimizer, warmup_steps=2, cycles=2, t_total=10)
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46, 0.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
scheduler = WarmupCosineWithHardRestartsSchedule(self.optimizer, warmup_steps=2, cycles=2, t_total=10)
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
if __name__ == "__main__":
unittest.main()
transformers/tests/optimization_tf_test.py 0 → 100644
View file @ 40ed7172
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
from transformers import is_tf_available
from .utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import (create_optimizer, GradientAccumulator)
@require_tf
class OptimizationFTest(unittest.TestCase):
def assertListAlmostEqual(self, list1, list2, tol):
self.assertEqual(len(list1), len(list2))
for a, b in zip(list1, list2):
self.assertAlmostEqual(a, b, delta=tol)
def testGradientAccumulator(self):
accumulator = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0])])
accumulator([tf.constant([-2.0, 1.0])])
accumulator([tf.constant([-1.0, 2.0])])
with self.assertRaises(ValueError):
accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])])
self.assertEqual(accumulator.step, 3)
self.assertEqual(len(accumulator.gradients), 1)
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [-2.0, 5.0], tol=1e-2)
accumulator.reset()
self.assertEqual(accumulator.step, 0)
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist(), [0.0, 0.0], tol=1e-2)
def testGradientAccumulatorDistributionStrategy(self):
context._context = None
ops.enable_eager_execution_internal()
physical_devices = tf.config.experimental.list_physical_devices("CPU")
tf.config.experimental.set_virtual_device_configuration(
physical_devices[0],
[tf.config.experimental.VirtualDeviceConfiguration(),
tf.config.experimental.VirtualDeviceConfiguration()])
devices = tf.config.experimental.list_logical_devices(device_type="CPU")
strategy = tf.distribute.MirroredStrategy(devices=[device.name for device in devices])
with strategy.scope():
accumulator = GradientAccumulator()
variable = tf.Variable([4.0, 3.0])
optimizer = create_optimizer(5e-5, 10, 5)
gradient_placeholder = tf.Variable([0.0, 0.0], trainable=False)
def accumulate_on_replica(gradient):
accumulator([gradient])
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients, [variable])), 1.0)
@tf.function
def accumulate(grad1, grad2):
with strategy.scope():
gradient_placeholder.values[0].assign(grad1)
gradient_placeholder.values[1].assign(grad2)
strategy.experimental_run_v2(accumulate_on_replica, args=(gradient_placeholder,))
@tf.function
def apply_grad():
with strategy.scope():
strategy.experimental_run_v2(apply_on_replica)
accumulate([1.0, 2.0], [-1.0, 1.0])
accumulate([3.0, -1.0], [-1.0, -1.0])
accumulate([-2.0, 2.0], [3.0, -2.0])
self.assertEqual(accumulator.step, 3)
self.assertListAlmostEqual(accumulator._gradients[0].values[0].value().numpy().tolist(), [2.0, 3.0], tol=1e-2)
self.assertListAlmostEqual(accumulator._gradients[0].values[1].value().numpy().tolist(), [1.0, -2.0], tol=1e-2)
apply_grad()
self.assertListAlmostEqual(variable.value().numpy().tolist(), [4.0, 3.0], tol=1e-2)
accumulator.reset()
self.assertEqual(accumulator.step, 0)
self.assertListAlmostEqual(accumulator._gradients[0].values[0].value().numpy().tolist(), [0.0, 0.0], tol=1e-2)
self.assertListAlmostEqual(accumulator._gradients[0].values[1].value().numpy().tolist(), [0.0, 0.0], tol=1e-2)
if __name__ == "__main__":
unittest.main()
\ No newline at end of file
transformers/tests/tokenization_albert_test.py 0 → 100644
View file @ 40ed7172
# coding=utf-8
# Copyright 2019 Hugging Face inc.
#
# 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.
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import unittest
from transformers.tokenization_albert import (AlbertTokenizer, SPIECE_UNDERLINE)
from .tokenization_tests_commons import CommonTestCases
SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'fixtures/spiece.model')
class AlbertTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = AlbertTokenizer
def setUp(self):
super(AlbertTokenizationTest, self).setUp()
# We have a SentencePiece fixture for testing
tokenizer = AlbertTokenizer(SAMPLE_VOCAB)
tokenizer.save_pretrained(self.tmpdirname)
def get_tokenizer(self, **kwargs):
return AlbertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"this is a test"
output_text = u"this is a test"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = AlbertTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize(u'This is a test')
self.assertListEqual(tokens, [u'▁this', u'▁is', u'▁a', u'▁test'])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens), [48, 25, 21, 1289])
tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.")
self.assertListEqual(tokens, [u'▁i', u'▁was', u'▁born', u'▁in', u'▁9', u'2000', u',', u'▁and', u'▁this', u'▁is', u'▁fal', u's', u'é', u'.'])
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(ids, [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(back_tokens, ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.'])
def test_sequence_builders(self):
tokenizer = AlbertTokenizer(SAMPLE_VOCAB)
text = tokenizer.encode("sequence builders")
text_2 = tokenizer.encode("multi-sequence build")
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [tokenizer.sep_token_id]
if __name__ == '__main__':
unittest.main()
transformers/tests/tokenization_auto_test.py
View file @ 40ed7172
......@@ -18,14 +18,16 @@ from __future__ import print_function
import unittest
import shutil
import pytest
import logging
from transformers import AutoTokenizer, BertTokenizer, AutoTokenizer, GPT2Tokenizer
from transformers import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
from .utils import slow, SMALL_MODEL_IDENTIFIER
class AutoTokenizerTest(unittest.TestCase):
@slow
def test_tokenizer_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys())[:1]:
......@@ -40,6 +42,11 @@ class AutoTokenizerTest(unittest.TestCase):
self.assertIsInstance(tokenizer, GPT2Tokenizer)
self.assertGreater(len(tokenizer), 0)
def test_tokenizer_from_pretrained_identifier(self):
logging.basicConfig(level=logging.INFO)
tokenizer = AutoTokenizer.from_pretrained(SMALL_MODEL_IDENTIFIER)
self.assertIsInstance(tokenizer, BertTokenizer)
self.assertEqual(len(tokenizer), 12)
if __name__ == "__main__":
unittest.main()
transformers/tests/tokenization_bert_japanese_test.py 0 → 100644
View file @ 40ed7172
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import unittest
from io import open
from transformers.tokenization_bert import WordpieceTokenizer
from transformers.tokenization_bert_japanese import (BertJapaneseTokenizer,
MecabTokenizer, CharacterTokenizer,
VOCAB_FILES_NAMES)
from .tokenization_tests_commons import CommonTestCases
from .utils import slow, custom_tokenizers
@custom_tokenizers
class BertJapaneseTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = BertJapaneseTokenizer
def setUp(self):
super(BertJapaneseTokenizationTest, self).setUp()
vocab_tokens = [u"[UNK]", u"[CLS]", u"[SEP]",
u"こんにちは", u"こん", u"にちは", u"ばんは", u"##こん", u"##にちは", u"##ばんは",
u"世界", u"##世界", u"、", u"##、", u"。", u"##。"]
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 BertJapaneseTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"こんにちは、世界。 \nこんばんは、世界。"
output_text = u"こんにちは 、 世界 。 こんばんは 、 世界 。"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file)
tokens = tokenizer.tokenize(u"こんにちは、世界。\nこんばんは、世界。")
self.assertListEqual(tokens,
[u"こんにちは", u"、", u"世界", u"。",
u"こん", u"##ばんは", u"、", u"世界", "。"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens),
[3, 12, 10, 14, 4, 9, 12, 10, 14])
def test_mecab_tokenizer(self):
tokenizer = MecabTokenizer()
self.assertListEqual(
tokenizer.tokenize(u" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "),
[u"アップルストア", u"で", u"iPhone", u"8", u"が",
u"発売", u"さ", u"れ", u"た", u"。"])
def test_mecab_tokenizer_lower(self):
tokenizer = MecabTokenizer(do_lower_case=True)
self.assertListEqual(
tokenizer.tokenize(u" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "),
[u"アップルストア", u"で", u"iphone", u"8", u"が",
u"発売", u"さ", u"れ", u"た", u"。"])
def test_mecab_tokenizer_no_normalize(self):
tokenizer = MecabTokenizer(normalize_text=False)
self.assertListEqual(
tokenizer.tokenize(u" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された　。 "),
[u"ｱｯﾌﾟﾙストア", u"で", u"iPhone", u"８", u"が",
u"発売", u"さ", u"れ", u"た", u"　", u"。"])
def test_wordpiece_tokenizer(self):
vocab_tokens = [u"[UNK]", u"[CLS]", u"[SEP]",
u"こんにちは", u"こん", u"にちは" u"ばんは", u"##こん", u"##にちは", u"##ばんは"]
vocab = {}
for (i, token) in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = WordpieceTokenizer(vocab=vocab, unk_token=u"[UNK]")
self.assertListEqual(tokenizer.tokenize(u""), [])
self.assertListEqual(tokenizer.tokenize(u"こんにちは"),
[u"こんにちは"])
self.assertListEqual(tokenizer.tokenize(u"こんばんは"),
[u"こん", u"##ばんは"])
self.assertListEqual(tokenizer.tokenize(u"こんばんは こんばんにちは こんにちは"),
[u"こん", u"##ばんは", u"[UNK]", u"こんにちは"])
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("bert-base-japanese")
text = tokenizer.encode(u"ありがとう。", add_special_tokens=False)
text_2 = tokenizer.encode(u"どういたしまして。", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_2 + [3]
class BertJapaneseCharacterTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = BertJapaneseTokenizer
def setUp(self):
super(BertJapaneseCharacterTokenizationTest, self).setUp()
vocab_tokens = [u"[UNK]", u"[CLS]", u"[SEP]",
u"こ", u"ん", u"に", u"ち", u"は", u"ば", u"世", u"界", u"、", u"。"]
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 BertJapaneseTokenizer.from_pretrained(self.tmpdirname,
subword_tokenizer_type="character",
**kwargs)
def get_input_output_texts(self):
input_text = u"こんにちは、世界。 \nこんばんは、世界。"
output_text = u"こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file,
subword_tokenizer_type="character")
tokens = tokenizer.tokenize(u"こんにちは、世界。 \nこんばんは、世界。")
self.assertListEqual(tokens,
[u"こ", u"ん", u"に", u"ち", u"は", u"、", u"世", u"界", u"。",
u"こ", u"ん", u"ば", u"ん", u"は", u"、", u"世", u"界", u"。"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens),
[3, 4, 5, 6, 7, 11, 9, 10, 12,
3, 4, 8, 4, 7, 11, 9, 10, 12])
def test_character_tokenizer(self):
vocab_tokens = [u"[UNK]", u"[CLS]", u"[SEP]",
u"こ", u"ん", u"に", u"ち", u"は", u"ば", u"世", u"界"u"、", u"。"]
vocab = {}
for (i, token) in enumerate(vocab_tokens):
vocab[token] = i
tokenizer = CharacterTokenizer(vocab=vocab, unk_token=u"[UNK]")
self.assertListEqual(tokenizer.tokenize(u""), [])
self.assertListEqual(tokenizer.tokenize(u"こんにちは"),
[u"こ", u"ん", u"に", u"ち", u"は"])
self.assertListEqual(tokenizer.tokenize(u"こんにちほ"),
[u"こ", u"ん", u"に", u"ち", u"[UNK]"])
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("bert-base-japanese-char")
text = tokenizer.encode(u"ありがとう。", add_special_tokens=False)
text_2 = tokenizer.encode(u"どういたしまして。", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_2 + [3]
transformers/tests/tokenization_bert_test.py
View file @ 40ed7172
......@@ -25,6 +25,7 @@ from transformers.tokenization_bert import (BasicTokenizer,
_is_whitespace, VOCAB_FILES_NAMES)
from .tokenization_tests_commons import CommonTestCases
from .utils import slow
class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
......@@ -125,11 +126,12 @@ class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
self.assertFalse(_is_punctuation(u"A"))
self.assertFalse(_is_punctuation(u" "))
@slow
def test_sequence_builders(self):
tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
text = tokenizer.encode("sequence builders")
text_2 = tokenizer.encode("multi-sequence build")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
......
transformers/tests/tokenization_distilbert_test.py
View file @ 40ed7172
......@@ -22,6 +22,7 @@ from transformers.tokenization_distilbert import (DistilBertTokenizer)
from .tokenization_tests_commons import CommonTestCases
from .tokenization_bert_test import BertTokenizationTest
from .utils import slow
class DistilBertTokenizationTest(BertTokenizationTest):
......@@ -30,11 +31,12 @@ class DistilBertTokenizationTest(BertTokenizationTest):
def get_tokenizer(self, **kwargs):
return DistilBertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
@slow
def test_sequence_builders(self):
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
text = tokenizer.encode("sequence builders")
text_2 = tokenizer.encode("multi-sequence build")
text = tokenizer.encode("sequence builders", add_special_tokens=False)
text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
......
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