Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
File size: 8,239 Bytes
75466df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
# coding=utf-8
# Copyright 2018 Google T5 Authors and HuggingFace Inc. team.
#
# 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 unittest
from transformers import is_torch_available
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_torch, slow
if is_torch_available():
from transformers import T5Config, T5Model, T5WithLMHeadModel
from transformers.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP
@require_torch
class T5ModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (T5Model, T5WithLMHeadModel) if is_torch_available() else ()
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
is_encoder_decoder = True
class T5ModelTester(object):
def __init__(
self,
parent,
batch_size=13,
encoder_seq_length=7,
decoder_seq_length=9,
is_training=True,
use_attention_mask=True,
use_labels=True,
vocab_size=99,
n_positions=14,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
d_ff=37,
relative_attention_num_buckets=8,
dropout_rate=0.1,
initializer_factor=0.002,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_positions = n_positions
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.d_ff = d_ff
self.relative_attention_num_buckets = relative_attention_num_buckets
self.dropout_rate = dropout_rate
self.initializer_factor = initializer_factor
self.scope = scope
def prepare_config_and_inputs(self):
encoder_input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
encoder_attention_mask = None
decoder_attention_mask = None
if self.use_attention_mask:
encoder_attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
decoder_lm_labels = None
if self.use_labels:
decoder_lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = T5Config(
vocab_size=self.vocab_size,
n_positions=self.n_positions,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor,
)
return (
config,
encoder_input_ids,
decoder_input_ids,
encoder_attention_mask,
decoder_attention_mask,
decoder_lm_labels,
)
def check_loss_output(self, result):
self.parent.assertListEqual(list(result["loss"].size()), [])
def create_and_check_t5_model(
self,
config,
encoder_input_ids,
decoder_input_ids,
encoder_attention_mask,
decoder_attention_mask,
decoder_lm_labels,
):
model = T5Model(config=config)
model.eval()
decoder_output, encoder_output = model(
encoder_input_ids=encoder_input_ids,
decoder_input_ids=decoder_input_ids,
encoder_attention_mask=encoder_attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
decoder_output, encoder_output = model(
encoder_input_ids=encoder_input_ids, decoder_input_ids=decoder_input_ids
)
result = {
"encoder_output": encoder_output,
"decoder_output": decoder_output,
}
self.parent.assertListEqual(
list(result["encoder_output"].size()), [self.batch_size, self.encoder_seq_length, self.hidden_size]
)
self.parent.assertListEqual(
list(result["decoder_output"].size()), [self.batch_size, self.decoder_seq_length, self.hidden_size]
)
def create_and_check_t5_with_lm_head(
self,
config,
encoder_input_ids,
decoder_input_ids,
encoder_attention_mask,
decoder_attention_mask,
decoder_lm_labels,
):
model = T5WithLMHeadModel(config=config)
model.eval()
outputs = model(
encoder_input_ids=encoder_input_ids,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_lm_labels=decoder_lm_labels,
)
loss, prediction_scores = outputs[0], outputs[1]
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()), [self.batch_size, self.decoder_seq_length, self.vocab_size]
)
self.check_loss_output(result)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
encoder_input_ids,
decoder_input_ids,
encoder_attention_mask,
decoder_attention_mask,
decoder_lm_labels,
) = config_and_inputs
inputs_dict = {
"encoder_input_ids": encoder_input_ids,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"encoder_attention_mask": encoder_attention_mask,
}
return config, inputs_dict
def setUp(self):
self.model_tester = T5ModelTest.T5ModelTester(self)
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_t5_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_t5_model(*config_and_inputs)
def test_with_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_t5_with_lm_head(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in list(T5_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = T5Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
self.assertIsNotNone(model)
|