Lysandre Debut
215
transformers/tests/modeling_ctrl_test.py
Normal file
215
transformers/tests/modeling_ctrl_test.py
Normal file
@@ -0,0 +1,215 @@
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# coding=utf-8
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# Copyright 2018 Salesforce and HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import pytest
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import shutil
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import pdb
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from transformers import is_torch_available
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if is_torch_available():
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from transformers import (CTRLConfig, CTRLModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
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CTRLLMHeadModel)
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else:
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pytestmark = pytest.mark.skip("Require Torch")
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from .modeling_common_test import (CommonTestCases, ids_tensor)
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from .configuration_common_test import ConfigTester
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class CTRLModelTest(CommonTestCases.CommonModelTester):
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all_model_classes = (CTRLModel, CTRLLMHeadModel) if is_torch_available() else ()
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test_pruning = False
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test_torchscript = False
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test_resize_embeddings = False
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test_head_masking = False
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class CTRLModelTester(object):
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def __init__(self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_token_type_ids=True,
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use_input_mask=True,
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use_labels=True,
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use_mc_token_ids=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_token_type_ids = use_token_type_ids
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.use_mc_token_ids = use_mc_token_ids
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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mc_token_ids = None
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if self.use_mc_token_ids:
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mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = CTRLConfig(
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vocab_size_or_config_json_file=self.vocab_size,
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n_embd=self.hidden_size,
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n_layer=self.num_hidden_layers,
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n_head=self.num_attention_heads,
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# intermediate_size=self.intermediate_size,
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# hidden_act=self.hidden_act,
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# hidden_dropout_prob=self.hidden_dropout_prob,
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# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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n_positions=self.max_position_embeddings,
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n_ctx=self.max_position_embeddings
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# type_vocab_size=self.type_vocab_size,
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# initializer_range=self.initializer_range
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)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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return config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels
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def check_loss_output(self, result):
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = CTRLModel(config=config)
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model.eval()
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model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
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model(input_ids, token_type_ids=token_type_ids)
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sequence_output, presents = model(input_ids)
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result = {
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"sequence_output": sequence_output,
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"presents": presents,
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].size()),
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[self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(len(result["presents"]), config.n_layer)
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def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = CTRLLMHeadModel(config)
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model.eval()
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loss, lm_logits, _ = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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result = {
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"loss": loss,
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"lm_logits": lm_logits
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}
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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self.parent.assertListEqual(
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list(result["lm_logits"].size()),
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[self.batch_size, self.seq_length, self.vocab_size])
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(config, input_ids, input_mask, head_mask, token_type_ids,
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mc_token_ids, sequence_labels, token_labels, choice_labels) = config_and_inputs
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inputs_dict = {
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'input_ids': input_ids,
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'token_type_ids': token_type_ids,
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'head_mask': head_mask
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}
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return config, inputs_dict
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def setUp(self):
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self.model_tester = CTRLModelTest.CTRLModelTester(self)
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self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_ctrl_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_ctrl_model(*config_and_inputs)
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def test_ctrl_lm_head_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
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@pytest.mark.slow
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def test_model_from_pretrained(self):
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cache_dir = "/tmp/transformers_test/"
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for model_name in list(CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = CTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
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shutil.rmtree(cache_dir)
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self.assertIsNotNone(model)
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if __name__ == "__main__":
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unittest.main()
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@@ -71,6 +71,8 @@ class TFCommonTestCases:
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if not is_torch_available():
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return
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import torch
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import numpy as np
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import transformers
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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@@ -79,12 +81,23 @@ class TFCommonTestCases:
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pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beggining
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pt_model_class = getattr(transformers, pt_model_class_name)
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config.output_hidden_states = True
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tf_model = model_class(config)
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pt_model = pt_model_class(config)
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# Check we can load pt model in tf and vice-versa (architecture similar)
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tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)
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pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
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# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
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pt_model.eval()
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pt_inputs_dict = dict((name, torch.from_numpy(key.numpy()).to(torch.long))
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for name, key in inputs_dict.items())
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with torch.no_grad():
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pto = pt_model(**pt_inputs_dict)
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tfo = tf_model(inputs_dict)
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max_diff = np.amax(np.abs(tfo[0].numpy() - pto[0].numpy()))
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self.assertLessEqual(max_diff, 2e-2)
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def test_keyword_and_dict_args(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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201
transformers/tests/modeling_tf_ctrl_test.py
Normal file
201
transformers/tests/modeling_tf_ctrl_test.py
Normal file
@@ -0,0 +1,201 @@
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# 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
|
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|
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import unittest
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import shutil
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import pytest
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import sys
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from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
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from .configuration_common_test import ConfigTester
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from transformers import CTRLConfig, is_tf_available
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if is_tf_available():
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import tensorflow as tf
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from transformers.modeling_tf_ctrl import (TFCTRLModel, TFCTRLLMHeadModel,
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TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
else:
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||||
pytestmark = pytest.mark.skip("Require TensorFlow")
|
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|
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class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
|
||||
|
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all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel) if is_tf_available() else ()
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class TFCTRLModelTester(object):
|
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def __init__(self,
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parent,
|
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batch_size=13,
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seq_length=7,
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is_training=True,
|
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use_token_type_ids=True,
|
||||
use_input_mask=True,
|
||||
use_labels=True,
|
||||
use_mc_token_ids=True,
|
||||
vocab_size=99,
|
||||
hidden_size=32,
|
||||
num_hidden_layers=5,
|
||||
num_attention_heads=4,
|
||||
intermediate_size=37,
|
||||
hidden_act="gelu",
|
||||
hidden_dropout_prob=0.1,
|
||||
attention_probs_dropout_prob=0.1,
|
||||
max_position_embeddings=512,
|
||||
type_vocab_size=16,
|
||||
type_sequence_label_size=2,
|
||||
initializer_range=0.02,
|
||||
num_labels=3,
|
||||
num_choices=4,
|
||||
scope=None,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.seq_length = seq_length
|
||||
self.is_training = is_training
|
||||
self.use_token_type_ids = use_token_type_ids
|
||||
self.use_input_mask = use_input_mask
|
||||
self.use_labels = use_labels
|
||||
self.use_mc_token_ids = use_mc_token_ids
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.intermediate_size = intermediate_size
|
||||
self.hidden_act = hidden_act
|
||||
self.hidden_dropout_prob = hidden_dropout_prob
|
||||
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.type_vocab_size = type_vocab_size
|
||||
self.type_sequence_label_size = type_sequence_label_size
|
||||
self.initializer_range = initializer_range
|
||||
self.num_labels = num_labels
|
||||
self.num_choices = num_choices
|
||||
self.scope = scope
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
||||
|
||||
input_mask = None
|
||||
if self.use_input_mask:
|
||||
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
|
||||
|
||||
token_type_ids = None
|
||||
if self.use_token_type_ids:
|
||||
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
|
||||
|
||||
mc_token_ids = None
|
||||
if self.use_mc_token_ids:
|
||||
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
|
||||
|
||||
sequence_labels = None
|
||||
token_labels = None
|
||||
choice_labels = None
|
||||
if self.use_labels:
|
||||
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
|
||||
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
|
||||
choice_labels = ids_tensor([self.batch_size], self.num_choices)
|
||||
|
||||
config = CTRLConfig(
|
||||
vocab_size_or_config_json_file=self.vocab_size,
|
||||
n_embd=self.hidden_size,
|
||||
n_layer=self.num_hidden_layers,
|
||||
n_head=self.num_attention_heads,
|
||||
# intermediate_size=self.intermediate_size,
|
||||
# hidden_act=self.hidden_act,
|
||||
# hidden_dropout_prob=self.hidden_dropout_prob,
|
||||
# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
|
||||
n_positions=self.max_position_embeddings,
|
||||
n_ctx=self.max_position_embeddings
|
||||
# type_vocab_size=self.type_vocab_size,
|
||||
# initializer_range=self.initializer_range
|
||||
)
|
||||
|
||||
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
|
||||
|
||||
return config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels
|
||||
|
||||
def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
||||
model = TFCTRLModel(config=config)
|
||||
inputs = {'input_ids': input_ids,
|
||||
'attention_mask': input_mask,
|
||||
'token_type_ids': token_type_ids}
|
||||
sequence_output = model(inputs)[0]
|
||||
|
||||
inputs = [input_ids, None, input_mask] # None is the input for 'past'
|
||||
sequence_output = model(inputs)[0]
|
||||
|
||||
sequence_output = model(input_ids)[0]
|
||||
|
||||
result = {
|
||||
"sequence_output": sequence_output.numpy(),
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["sequence_output"].shape),
|
||||
[self.batch_size, self.seq_length, self.hidden_size])
|
||||
|
||||
|
||||
def create_and_check_ctrl_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
|
||||
model = TFCTRLLMHeadModel(config=config)
|
||||
inputs = {'input_ids': input_ids,
|
||||
'attention_mask': input_mask,
|
||||
'token_type_ids': token_type_ids}
|
||||
prediction_scores = model(inputs)[0]
|
||||
result = {
|
||||
"prediction_scores": prediction_scores.numpy(),
|
||||
}
|
||||
self.parent.assertListEqual(
|
||||
list(result["prediction_scores"].shape),
|
||||
[self.batch_size, self.seq_length, self.vocab_size])
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
|
||||
(config, input_ids, input_mask, head_mask, token_type_ids,
|
||||
mc_token_ids, sequence_labels, token_labels, choice_labels) = config_and_inputs
|
||||
|
||||
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = TFCTRLModelTest.TFCTRLModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_ctrl_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_ctrl_model(*config_and_inputs)
|
||||
|
||||
def test_ctrl_lm_head(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_ctrl_lm_head(*config_and_inputs)
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_model_from_pretrained(self):
|
||||
cache_dir = "/tmp/transformers_test/"
|
||||
for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
model = TFCTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
|
||||
shutil.rmtree(cache_dir)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
@@ -222,7 +222,7 @@ class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
|
||||
@pytest.mark.slow
|
||||
def test_model_from_pretrained(self):
|
||||
cache_dir = "/tmp/transformers_test/"
|
||||
for model_name in list(TF_gpt2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
model = TFGPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
|
||||
shutil.rmtree(cache_dir)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
69
transformers/tests/tokenization_ctrl_test.py
Normal file
69
transformers/tests/tokenization_ctrl_test.py
Normal file
@@ -0,0 +1,69 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2018 Salesforce 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.
|
||||
from __future__ import absolute_import, division, print_function, unicode_literals
|
||||
|
||||
import os
|
||||
import unittest
|
||||
import json
|
||||
from io import open
|
||||
|
||||
from transformers.tokenization_ctrl import CTRLTokenizer, VOCAB_FILES_NAMES
|
||||
|
||||
from .tokenization_tests_commons import CommonTestCases
|
||||
|
||||
class CTRLTokenizationTest(CommonTestCases.CommonTokenizerTester):
|
||||
|
||||
tokenizer_class = CTRLTokenizer
|
||||
|
||||
def setUp(self):
|
||||
super(CTRLTokenizationTest, self).setUp()
|
||||
|
||||
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
|
||||
vocab = ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
|
||||
vocab_tokens = dict(zip(vocab, range(len(vocab))))
|
||||
merges = ["#version: 0.2", 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
|
||||
self.special_tokens_map = {"unk_token": "<unk>"}
|
||||
|
||||
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
|
||||
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'])
|
||||
with open(self.vocab_file, "w", encoding="utf-8") as fp:
|
||||
fp.write(json.dumps(vocab_tokens) + "\n")
|
||||
with open(self.merges_file, "w", encoding="utf-8") as fp:
|
||||
fp.write("\n".join(merges))
|
||||
|
||||
def get_tokenizer(self, **kwargs):
|
||||
kwargs.update(self.special_tokens_map)
|
||||
return CTRLTokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def get_input_output_texts(self):
|
||||
input_text = u"adapt react readapt apt"
|
||||
output_text = u"adapt react readapt apt"
|
||||
return input_text, output_text
|
||||
|
||||
def test_full_tokenizer(self):
|
||||
tokenizer = CTRLTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
|
||||
text = "adapt react readapt apt"
|
||||
bpe_tokens = 'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
|
||||
tokens = tokenizer.tokenize(text)
|
||||
self.assertListEqual(tokens, bpe_tokens)
|
||||
|
||||
input_tokens = tokens + [tokenizer.unk_token]
|
||||
|
||||
input_bpe_tokens = [0, 1, 2, 4, 5, 1, 0, 3, 6]
|
||||
self.assertListEqual(
|
||||
tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user