211 lines
8.1 KiB
Python
211 lines
8.1 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 random
|
|
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, torch_device
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
from transformers import TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel
|
|
from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
|
|
|
|
|
|
@require_torch
|
|
class TransfoXLModelTest(ModelTesterMixin, unittest.TestCase):
|
|
|
|
all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else ()
|
|
test_pruning = False
|
|
test_torchscript = False
|
|
test_resize_embeddings = False
|
|
|
|
class TransfoXLModelTester(object):
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=13,
|
|
seq_length=7,
|
|
mem_len=30,
|
|
clamp_len=15,
|
|
is_training=True,
|
|
use_labels=True,
|
|
vocab_size=99,
|
|
cutoffs=[10, 50, 80],
|
|
hidden_size=32,
|
|
d_embed=32,
|
|
num_attention_heads=4,
|
|
d_head=8,
|
|
d_inner=128,
|
|
div_val=2,
|
|
num_hidden_layers=5,
|
|
scope=None,
|
|
seed=1,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.seq_length = seq_length
|
|
self.mem_len = mem_len
|
|
self.key_length = seq_length + mem_len
|
|
self.clamp_len = clamp_len
|
|
self.is_training = is_training
|
|
self.use_labels = use_labels
|
|
self.vocab_size = vocab_size
|
|
self.cutoffs = cutoffs
|
|
self.hidden_size = hidden_size
|
|
self.d_embed = d_embed
|
|
self.num_attention_heads = num_attention_heads
|
|
self.d_head = d_head
|
|
self.d_inner = d_inner
|
|
self.div_val = div_val
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.scope = scope
|
|
self.seed = seed
|
|
|
|
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,
|
|
)
|
|
|
|
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()
|
|
|
|
hidden_states_1, mems_1 = model(input_ids_1)
|
|
hidden_states_2, mems_2 = model(input_ids_2, mems_1)
|
|
outputs = {
|
|
"hidden_states_1": hidden_states_1,
|
|
"mems_1": mems_1,
|
|
"hidden_states_2": hidden_states_2,
|
|
"mems_2": mems_2,
|
|
}
|
|
return outputs
|
|
|
|
def check_transfo_xl_model_output(self, result):
|
|
self.parent.assertListEqual(
|
|
list(result["hidden_states_1"].size()), [self.batch_size, self.seq_length, self.hidden_size]
|
|
)
|
|
self.parent.assertListEqual(
|
|
list(result["hidden_states_2"].size()), [self.batch_size, self.seq_length, self.hidden_size]
|
|
)
|
|
self.parent.assertListEqual(
|
|
list(list(mem.size()) for mem in result["mems_1"]),
|
|
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
|
|
)
|
|
self.parent.assertListEqual(
|
|
list(list(mem.size()) 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, mems_1 = model(input_ids_1)
|
|
loss_1, _, mems_1 = model(input_ids_1, labels=lm_labels)
|
|
lm_logits_2, mems_2 = model(input_ids_2, mems=mems_1)
|
|
loss_2, _, mems_2 = model(input_ids_2, labels=lm_labels, mems=mems_1)
|
|
|
|
outputs = {
|
|
"loss_1": loss_1,
|
|
"mems_1": mems_1,
|
|
"lm_logits_1": lm_logits_1,
|
|
"loss_2": loss_2,
|
|
"mems_2": mems_2,
|
|
"lm_logits_2": lm_logits_2,
|
|
}
|
|
return outputs
|
|
|
|
def check_transfo_xl_lm_head_output(self, result):
|
|
self.parent.assertListEqual(list(result["loss_1"].size()), [self.batch_size, self.seq_length])
|
|
self.parent.assertListEqual(
|
|
list(result["lm_logits_1"].size()), [self.batch_size, self.seq_length, self.vocab_size]
|
|
)
|
|
self.parent.assertListEqual(
|
|
list(list(mem.size()) for mem in result["mems_1"]),
|
|
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
|
|
)
|
|
|
|
self.parent.assertListEqual(list(result["loss_2"].size()), [self.batch_size, self.seq_length])
|
|
self.parent.assertListEqual(
|
|
list(result["lm_logits_2"].size()), [self.batch_size, self.seq_length, self.vocab_size]
|
|
)
|
|
self.parent.assertListEqual(
|
|
list(list(mem.size()) 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
|
|
|
|
def setUp(self):
|
|
self.model_tester = TransfoXLModelTest.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)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in list(TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
|
model = TransfoXLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
|
|
self.assertIsNotNone(model)
|