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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.
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_transfo_xl import (
TFTransfoXLModel,
TFTransfoXLLMHeadModel,
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
)
@require_tf
class TFTransfoXLModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (TFTransfoXLModel, TFTransfoXLLMHeadModel) if is_tf_available() else ()
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
class TFTransfoXLModelTester(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)
tf.random.set_seed(self.seed)
def create_and_check_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
model = TFTransfoXLModel(config)
hidden_states_1, mems_1 = model(input_ids_1)
inputs = {"input_ids": input_ids_2, "mems": mems_1}
hidden_states_2, mems_2 = model(inputs)
result = {
"hidden_states_1": hidden_states_1.numpy(),
"mems_1": [mem.numpy() for mem in mems_1],
"hidden_states_2": hidden_states_2.numpy(),
"mems_2": [mem.numpy() for mem in mems_2],
}
self.parent.assertListEqual(
list(result["hidden_states_1"].shape), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(
list(result["hidden_states_2"].shape), [self.batch_size, self.seq_length, self.hidden_size]
)
self.parent.assertListEqual(
list(list(mem.shape) 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.shape) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
)
def create_and_check_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
model = TFTransfoXLLMHeadModel(config)
lm_logits_1, mems_1 = model(input_ids_1)
inputs = {"input_ids": input_ids_1, "labels": lm_labels}
_, mems_1 = model(inputs)
lm_logits_2, mems_2 = model([input_ids_2, mems_1])
inputs = {"input_ids": input_ids_1, "mems": mems_1, "labels": lm_labels}
_, mems_2 = model(inputs)
result = {
"mems_1": [mem.numpy() for mem in mems_1],
"lm_logits_1": lm_logits_1.numpy(),
"mems_2": [mem.numpy() for mem in mems_2],
"lm_logits_2": lm_logits_2.numpy(),
}
self.parent.assertListEqual(
list(result["lm_logits_1"].shape), [self.batch_size, self.seq_length, self.vocab_size]
)
self.parent.assertListEqual(
list(list(mem.shape) for mem in result["mems_1"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
)
self.parent.assertListEqual(
list(result["lm_logits_2"].shape), [self.batch_size, self.seq_length, self.vocab_size]
)
self.parent.assertListEqual(
list(list(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
def setUp(self):
self.model_tester = TFTransfoXLModelTest.TFTransfoXLModelTester(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()
self.model_tester.create_and_check_transfo_xl_model(*config_and_inputs)
def test_transfo_xl_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFTransfoXLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
self.assertIsNotNone(model)
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