665 lines
21 KiB
Python
665 lines
21 KiB
Python
# 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.
|
|
import copy
|
|
import random
|
|
import unittest
|
|
|
|
from transformers import is_torch_available
|
|
from transformers.testing_utils import require_multigpu, require_torch, slow, torch_device
|
|
|
|
from .test_configuration_common import ConfigTester
|
|
from .test_modeling_common import ModelTesterMixin, ids_tensor
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, TransfoXLModel
|
|
from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST
|
|
|
|
|
|
class TransfoXLModelTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = 14
|
|
self.seq_length = 7
|
|
self.mem_len = 30
|
|
self.key_length = self.seq_length + self.mem_len
|
|
self.clamp_len = 15
|
|
self.is_training = True
|
|
self.use_labels = True
|
|
self.vocab_size = 99
|
|
self.cutoffs = [10, 50, 80]
|
|
self.hidden_size = 32
|
|
self.d_embed = 32
|
|
self.num_attention_heads = 4
|
|
self.d_head = 8
|
|
self.d_inner = 128
|
|
self.div_val = 2
|
|
self.num_hidden_layers = 5
|
|
self.scope = None
|
|
self.seed = 1
|
|
self.eos_token_id = 0
|
|
|
|
def prepare_config_and_inputs(self):
|
|
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
|
|
lm_labels = None
|
|
if self.use_labels:
|
|
lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
|
|
|
config = TransfoXLConfig(
|
|
vocab_size=self.vocab_size,
|
|
mem_len=self.mem_len,
|
|
clamp_len=self.clamp_len,
|
|
cutoffs=self.cutoffs,
|
|
d_model=self.hidden_size,
|
|
d_embed=self.d_embed,
|
|
n_head=self.num_attention_heads,
|
|
d_head=self.d_head,
|
|
d_inner=self.d_inner,
|
|
div_val=self.div_val,
|
|
n_layer=self.num_hidden_layers,
|
|
eos_token_id=self.eos_token_id,
|
|
return_dict=True,
|
|
)
|
|
|
|
return (config, input_ids_1, input_ids_2, lm_labels)
|
|
|
|
def set_seed(self):
|
|
random.seed(self.seed)
|
|
torch.manual_seed(self.seed)
|
|
|
|
def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
|
|
model = TransfoXLModel(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
outputs1 = model(input_ids_1)
|
|
outputs2 = model(input_ids_2, outputs1["mems"])
|
|
outputs = {
|
|
"hidden_states_1": outputs1["last_hidden_state"],
|
|
"mems_1": outputs1["mems"],
|
|
"hidden_states_2": outputs2["last_hidden_state"],
|
|
"mems_2": outputs2["mems"],
|
|
}
|
|
return outputs
|
|
|
|
def check_transfo_xl_model_output(self, result):
|
|
self.parent.assertEqual(result["hidden_states_1"].shape, (self.batch_size, self.seq_length, self.hidden_size))
|
|
self.parent.assertEqual(result["hidden_states_2"].shape, (self.batch_size, self.seq_length, self.hidden_size))
|
|
self.parent.assertListEqual(
|
|
[mem.shape for mem in result["mems_1"]],
|
|
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
|
|
)
|
|
self.parent.assertListEqual(
|
|
[mem.shape for mem in result["mems_2"]],
|
|
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
|
|
)
|
|
|
|
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 = model(input_ids_1)["prediction_scores"]
|
|
outputs1 = model(input_ids_1, labels=lm_labels)
|
|
lm_logits_2 = model(input_ids_2, mems=outputs1["mems"])["prediction_scores"]
|
|
outputs2 = model(input_ids_2, labels=lm_labels, mems=outputs1["mems"])
|
|
|
|
outputs = {
|
|
"loss_1": outputs1["losses"],
|
|
"mems_1": outputs1["mems"],
|
|
"lm_logits_1": lm_logits_1,
|
|
"loss_2": outputs2["losses"],
|
|
"mems_2": outputs2["mems"],
|
|
"lm_logits_2": lm_logits_2,
|
|
}
|
|
return outputs
|
|
|
|
def check_transfo_xl_lm_head_output(self, result):
|
|
self.parent.assertEqual(result["loss_1"].shape, (self.batch_size, self.seq_length - 1))
|
|
self.parent.assertEqual(result["lm_logits_1"].shape, (self.batch_size, self.seq_length, self.vocab_size))
|
|
self.parent.assertListEqual(
|
|
[mem.shape for mem in result["mems_1"]],
|
|
[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
|
|
)
|
|
|
|
self.parent.assertEqual(result["loss_2"].shape, (self.batch_size, self.seq_length - 1))
|
|
self.parent.assertEqual(result["lm_logits_2"].shape, (self.batch_size, self.seq_length, self.vocab_size))
|
|
self.parent.assertListEqual(
|
|
[mem.shape for mem in result["mems_2"]],
|
|
[(self.mem_len, 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, lm_labels) = config_and_inputs
|
|
inputs_dict = {"input_ids": input_ids_1}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class TransfoXLModelTest(ModelTesterMixin, unittest.TestCase):
|
|
all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else ()
|
|
all_generative_model_classes = (TransfoXLLMHeadModel,) if is_torch_available() else ()
|
|
test_pruning = False
|
|
test_torchscript = False
|
|
test_resize_embeddings = True
|
|
|
|
def check_cutoffs_and_n_token(
|
|
self, copied_cutoffs, layer, model_embed, model, model_class, resized_value, vocab_size
|
|
):
|
|
# Check that the cutoffs were modified accordingly
|
|
for i in range(len(copied_cutoffs)):
|
|
if i < layer:
|
|
self.assertEqual(model_embed.cutoffs[i], copied_cutoffs[i])
|
|
if model_class == TransfoXLLMHeadModel:
|
|
self.assertEqual(model.crit.cutoffs[i], copied_cutoffs[i])
|
|
if i < len(model.config.cutoffs):
|
|
self.assertEqual(model.config.cutoffs[i], copied_cutoffs[i])
|
|
else:
|
|
self.assertEqual(model_embed.cutoffs[i], copied_cutoffs[i] + resized_value)
|
|
if model_class == TransfoXLLMHeadModel:
|
|
self.assertEqual(model.crit.cutoffs[i], copied_cutoffs[i] + resized_value)
|
|
if i < len(model.config.cutoffs):
|
|
self.assertEqual(model.config.cutoffs[i], copied_cutoffs[i] + resized_value)
|
|
|
|
self.assertEqual(model_embed.n_token, vocab_size + resized_value)
|
|
if model_class == TransfoXLLMHeadModel:
|
|
self.assertEqual(model.crit.n_token, vocab_size + resized_value)
|
|
|
|
def setUp(self):
|
|
self.model_tester = TransfoXLModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_transfo_xl_model(self):
|
|
self.model_tester.set_seed()
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
output_result = self.model_tester.create_transfo_xl_model(*config_and_inputs)
|
|
self.model_tester.check_transfo_xl_model_output(output_result)
|
|
|
|
def test_transfo_xl_lm_head(self):
|
|
self.model_tester.set_seed()
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
|
|
self.model_tester.check_transfo_xl_lm_head_output(output_result)
|
|
|
|
@require_multigpu
|
|
def test_multigpu_data_parallel_forward(self):
|
|
# Opt-out of this test.
|
|
pass
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
model = TransfoXLModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
def test_resize_tokens_embeddings(self):
|
|
(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.to(torch_device)
|
|
|
|
if self.model_tester.is_training is False:
|
|
model.eval()
|
|
|
|
model_vocab_size = config.vocab_size
|
|
# Retrieve the embeddings and clone theme
|
|
model_embed = model.resize_token_embeddings(model_vocab_size)
|
|
cloned_embeddings = [emb.weight.clone() for emb in model_embed.emb_layers]
|
|
# Retrieve the cutoffs and copy them
|
|
copied_cutoffs = copy.copy(model_embed.cutoffs)
|
|
|
|
test_layers = [x for x in range(config.div_val)]
|
|
for layer in test_layers:
|
|
# 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, layer)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0] + 10)
|
|
# Check that the cutoffs were modified accordingly
|
|
self.check_cutoffs_and_n_token(
|
|
copied_cutoffs, layer, model_embed, model, model_class, 10, model_vocab_size
|
|
)
|
|
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**inputs_dict)
|
|
|
|
# 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 - 5, layer)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size - 5)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0] - 5)
|
|
# Check that the cutoffs were modified accordingly
|
|
self.check_cutoffs_and_n_token(
|
|
copied_cutoffs, layer, model_embed, model, model_class, -5, model_vocab_size
|
|
)
|
|
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
# Input ids should be clamped to the maximum size of the vocabulary
|
|
inputs_dict["input_ids"].clamp_(max=model_vocab_size - 5 - 1)
|
|
model(**inputs_dict)
|
|
|
|
# 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[layer], model_embed.emb_layers[layer].weight):
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
# Reset model embeddings to original size
|
|
model.resize_token_embeddings(model_vocab_size, layer)
|
|
self.assertEqual(model_vocab_size, model.config.vocab_size)
|
|
self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0])
|
|
|
|
|
|
class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
|
|
@slow
|
|
def test_lm_generate_transfo_xl_wt103(self):
|
|
model = TransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
|
|
model.to(torch_device)
|
|
input_ids = torch.tensor(
|
|
[
|
|
[
|
|
33,
|
|
1297,
|
|
2,
|
|
1,
|
|
1009,
|
|
4,
|
|
1109,
|
|
11739,
|
|
4762,
|
|
358,
|
|
5,
|
|
25,
|
|
245,
|
|
22,
|
|
1706,
|
|
17,
|
|
20098,
|
|
5,
|
|
3215,
|
|
21,
|
|
37,
|
|
1110,
|
|
3,
|
|
13,
|
|
1041,
|
|
4,
|
|
24,
|
|
603,
|
|
490,
|
|
2,
|
|
71477,
|
|
20098,
|
|
104447,
|
|
2,
|
|
20961,
|
|
1,
|
|
2604,
|
|
4,
|
|
1,
|
|
329,
|
|
3,
|
|
6224,
|
|
831,
|
|
16002,
|
|
2,
|
|
8,
|
|
603,
|
|
78967,
|
|
29546,
|
|
23,
|
|
803,
|
|
20,
|
|
25,
|
|
416,
|
|
5,
|
|
8,
|
|
232,
|
|
4,
|
|
277,
|
|
6,
|
|
1855,
|
|
4601,
|
|
3,
|
|
29546,
|
|
54,
|
|
8,
|
|
3609,
|
|
5,
|
|
57211,
|
|
49,
|
|
4,
|
|
1,
|
|
277,
|
|
18,
|
|
8,
|
|
1755,
|
|
15691,
|
|
3,
|
|
341,
|
|
25,
|
|
416,
|
|
693,
|
|
42573,
|
|
71,
|
|
17,
|
|
401,
|
|
94,
|
|
31,
|
|
17919,
|
|
2,
|
|
29546,
|
|
7873,
|
|
18,
|
|
1,
|
|
435,
|
|
23,
|
|
11011,
|
|
755,
|
|
5,
|
|
5167,
|
|
3,
|
|
7983,
|
|
98,
|
|
84,
|
|
2,
|
|
29546,
|
|
3267,
|
|
8,
|
|
3609,
|
|
4,
|
|
1,
|
|
4865,
|
|
1075,
|
|
2,
|
|
6087,
|
|
71,
|
|
6,
|
|
346,
|
|
8,
|
|
5854,
|
|
3,
|
|
29546,
|
|
824,
|
|
1400,
|
|
1868,
|
|
2,
|
|
19,
|
|
160,
|
|
2,
|
|
311,
|
|
8,
|
|
5496,
|
|
2,
|
|
20920,
|
|
17,
|
|
25,
|
|
15097,
|
|
3,
|
|
24,
|
|
24,
|
|
0,
|
|
]
|
|
],
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
)
|
|
# In 1991 , the remains of Russian Tsar Nicholas II and his family
|
|
# ( except for Alexei and Maria ) are discovered .
|
|
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
|
|
# remainder of the story . 1883 Western Siberia ,
|
|
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
|
|
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
|
|
# father initially slaps him for making such an accusation , Rasputin watches as the
|
|
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
|
|
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
|
|
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
|
|
|
|
expected_output_ids = [
|
|
33,
|
|
1297,
|
|
2,
|
|
1,
|
|
1009,
|
|
4,
|
|
1109,
|
|
11739,
|
|
4762,
|
|
358,
|
|
5,
|
|
25,
|
|
245,
|
|
22,
|
|
1706,
|
|
17,
|
|
20098,
|
|
5,
|
|
3215,
|
|
21,
|
|
37,
|
|
1110,
|
|
3,
|
|
13,
|
|
1041,
|
|
4,
|
|
24,
|
|
603,
|
|
490,
|
|
2,
|
|
71477,
|
|
20098,
|
|
104447,
|
|
2,
|
|
20961,
|
|
1,
|
|
2604,
|
|
4,
|
|
1,
|
|
329,
|
|
3,
|
|
6224,
|
|
831,
|
|
16002,
|
|
2,
|
|
8,
|
|
603,
|
|
78967,
|
|
29546,
|
|
23,
|
|
803,
|
|
20,
|
|
25,
|
|
416,
|
|
5,
|
|
8,
|
|
232,
|
|
4,
|
|
277,
|
|
6,
|
|
1855,
|
|
4601,
|
|
3,
|
|
29546,
|
|
54,
|
|
8,
|
|
3609,
|
|
5,
|
|
57211,
|
|
49,
|
|
4,
|
|
1,
|
|
277,
|
|
18,
|
|
8,
|
|
1755,
|
|
15691,
|
|
3,
|
|
341,
|
|
25,
|
|
416,
|
|
693,
|
|
42573,
|
|
71,
|
|
17,
|
|
401,
|
|
94,
|
|
31,
|
|
17919,
|
|
2,
|
|
29546,
|
|
7873,
|
|
18,
|
|
1,
|
|
435,
|
|
23,
|
|
11011,
|
|
755,
|
|
5,
|
|
5167,
|
|
3,
|
|
7983,
|
|
98,
|
|
84,
|
|
2,
|
|
29546,
|
|
3267,
|
|
8,
|
|
3609,
|
|
4,
|
|
1,
|
|
4865,
|
|
1075,
|
|
2,
|
|
6087,
|
|
71,
|
|
6,
|
|
346,
|
|
8,
|
|
5854,
|
|
3,
|
|
29546,
|
|
824,
|
|
1400,
|
|
1868,
|
|
2,
|
|
19,
|
|
160,
|
|
2,
|
|
311,
|
|
8,
|
|
5496,
|
|
2,
|
|
20920,
|
|
17,
|
|
25,
|
|
15097,
|
|
3,
|
|
24,
|
|
24,
|
|
0,
|
|
33,
|
|
1,
|
|
142,
|
|
1298,
|
|
188,
|
|
2,
|
|
29546,
|
|
113,
|
|
8,
|
|
3654,
|
|
4,
|
|
1,
|
|
1109,
|
|
7136,
|
|
833,
|
|
3,
|
|
13,
|
|
1645,
|
|
4,
|
|
29546,
|
|
11,
|
|
104,
|
|
7,
|
|
1,
|
|
1109,
|
|
532,
|
|
7129,
|
|
2,
|
|
10,
|
|
83507,
|
|
2,
|
|
1162,
|
|
1123,
|
|
2,
|
|
6,
|
|
7245,
|
|
10,
|
|
2,
|
|
5,
|
|
11,
|
|
104,
|
|
7,
|
|
1,
|
|
1109,
|
|
532,
|
|
7129,
|
|
2,
|
|
10,
|
|
24,
|
|
24,
|
|
10,
|
|
22,
|
|
10,
|
|
13,
|
|
770,
|
|
5863,
|
|
4,
|
|
7245,
|
|
10,
|
|
]
|
|
# In 1991, the remains of Russian Tsar Nicholas II and his family ( except for
|
|
# Alexei and Maria ) are discovered. The voice of young son, Tsarevich Alexei
|
|
# Nikolaevich, narrates the remainder of the story. 1883 Western Siberia, a young
|
|
# Grigori Rasputin is asked by his father and a group of men to perform magic.
|
|
# Rasputin has a vision and denounces one of the men as a horse thief. Although
|
|
# his father initially slaps him for making such an accusation, Rasputin watches
|
|
# as the man is chased outside and beaten. Twenty years later, Rasputin sees a
|
|
# vision of the Virgin Mary, prompting him to become a priest. Rasputin quickly
|
|
# becomes famous, with people, even a bishop, begging for his blessing. In the
|
|
# early 20th century, Rasputin became a symbol of the Russian Orthodox Church.
|
|
# The image of Rasputin was used in the Russian national anthem, " Nearer, My God,
|
|
# to Heaven ", and was used in the Russian national anthem, " " ( " The Great Spirit
|
|
# of Heaven "
|
|
|
|
output_ids = model.generate(input_ids, max_length=200, do_sample=False)
|
|
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
|