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adding gpt2

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thomwolf
2019-02-17 23:38:51 +01:00
parent 3a2f97db6f
commit ffd623823d
7 changed files with 1246 additions and 4 deletions
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tests/modeling_gpt2_test.py Normal file
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# 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
from __future__ import division
from __future__ import print_function
import unittest
import json
import random
import torch
from pytorch_pretrained_bert import (GPT2Config, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel)
class GPT2ModelTest(unittest.TestCase):
class GPT2ModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_position_ids=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
n_special=1,
n_positions=33,
n_embd=32,
n_layer=5,
n_head=4,
n_choices=3,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_position_ids = use_position_ids
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_special = n_special
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_choices = n_choices
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.vocab_size)
position_ids = None
if self.use_position_ids:
position_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
token_type_ids = None
if self.use_token_type_ids:
total_voc = self.vocab_size + self.n_special
token_type_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
mc_labels = None
lm_labels = None
mc_token_ids = None
if self.use_labels:
mc_labels = GPT2ModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size)
lm_labels = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
mc_token_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices], self.seq_length)
config = GPT2Config(
vocab_size_or_config_json_file=self.vocab_size,
n_positions=self.n_positions,
n_special=self.n_special,
n_embd=self.n_embd,
n_layer=self.n_layer,
n_head=self.n_head,
initializer_range=self.initializer_range)
return (config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids)
def create_gpt2_model(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = GPT2Model(config)
model.eval()
hidden_states, presents = model(input_ids, position_ids, token_type_ids)
outputs = {
"hidden_states": hidden_states,
"presents": presents,
}
return outputs
def check_gpt2_model_output(self, result):
self.parent.assertListEqual(
list(result["hidden_states"].size()),
[self.batch_size, self.n_choices, self.seq_length, self.n_embd])
def create_gpt2_lm_head(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = GPT2LMHeadModel(config)
model.eval()
loss = model(input_ids, position_ids, token_type_ids, lm_labels)
lm_logits, presents = model(input_ids, position_ids, token_type_ids)
outputs = {
"loss": loss,
"lm_logits": lm_logits,
"presents": presents,
}
return outputs
def check_gpt2_lm_head_output(self, result):
total_voc = self.n_special + self.vocab_size
self.parent.assertListEqual(
list(result["lm_logits"].size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
def check_gpt2_lm_head_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
def create_gpt2_double_heads(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = GPT2DoubleHeadsModel(config)
model.eval()
loss = model(input_ids, mc_token_ids,
lm_labels=lm_labels, mc_labels=mc_labels,
token_type_ids=token_type_ids, position_ids=position_ids)
lm_logits, mc_logits, presents = model(input_ids, mc_token_ids, position_ids=position_ids, token_type_ids=token_type_ids)
outputs = {
"loss": loss,
"lm_logits": lm_logits,
"mc_logits": mc_logits,
"presents": presents,
}
return outputs
def check_gpt2_double_heads_output(self, result):
total_voc = self.n_special + self.vocab_size
self.parent.assertListEqual(
list(result["lm_logits"].size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
self.parent.assertListEqual(
list(result["mc_logits"].size()),
[self.batch_size, self.n_choices])
def check_gpt2_double_heads_loss_output(self, result):
self.parent.assertListEqual(
[list(l.size()) for l in result["loss"]],
[[], []])
def test_default(self):
self.run_tester(GPT2ModelTest.GPT2ModelTester(self))
def test_config_to_json_string(self):
config = GPT2Config(vocab_size_or_config_json_file=99, n_embd=37)
obj = json.loads(config.to_json_string())
self.assertEqual(obj["vocab_size"], 99)
self.assertEqual(obj["n_embd"], 37)
def run_tester(self, tester):
config_and_inputs = tester.prepare_config_and_inputs()
output_result = tester.create_gpt2_model(*config_and_inputs)
tester.check_gpt2_model_output(output_result)
output_result = tester.create_gpt2_lm_head(*config_and_inputs)
tester.check_gpt2_lm_head_output(output_result)
tester.check_gpt2_lm_head_loss_output(output_result)
output_result = tester.create_gpt2_double_heads(*config_and_inputs)
tester.check_gpt2_double_heads_output(output_result)
tester.check_gpt2_double_heads_loss_output(output_result)
@classmethod
def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
if __name__ == "__main__":
unittest.main()

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tests/tokenization_gpt2_test.py Normal file
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# 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
import json
from pytorch_pretrained_bert.tokenization_gpt2 import GPT2Tokenizer
class GPT2TokenizationTest(unittest.TestCase):
def test_full_tokenizer(self):
""" Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
"w</w>", "r</w>", "t</w>",
"lo", "low", "er</w>",
"low</w>", "lowest</w>", "newer</w>", "wider</w>"]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
with open("/tmp/openai_tokenizer_vocab_test.json", "w") as fp:
json.dump(vocab_tokens, fp)
vocab_file = fp.name
with open("/tmp/openai_tokenizer_merges_test.txt", "w") as fp:
fp.write("\n".join(merges))
merges_file = fp.name
tokenizer = GPT2Tokenizer(vocab_file, merges_file)
os.remove(vocab_file)
os.remove(merges_file)
text = "lower"
bpe_tokens = ["low", "er</w>"]
tokens = tokenizer.tokenize(text)
self.assertListEqual(tokens, bpe_tokens)
input_tokens = tokens
input_bpe_tokens = [14, 15, 20]
self.assertListEqual(
tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
if __name__ == '__main__':
unittest.main()