297 lines
10 KiB
Python
297 lines
10 KiB
Python
# 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
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# 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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import gc
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import unittest
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from transformers import CTRLConfig, is_torch_available
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from transformers.testing_utils import backend_empty_cache, require_torch, slow, torch_device
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
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CTRLForSequenceClassification,
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CTRLLMHeadModel,
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CTRLModel,
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)
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class CTRLModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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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=2,
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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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self.pad_token_id = self.vocab_size - 1
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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 = random_attention_mask([self.batch_size, self.seq_length])
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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 = self.get_config()
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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def get_config(self):
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return CTRLConfig(
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vocab_size=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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dff=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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# type_vocab_size=self.type_vocab_size,
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# initializer_range=self.initializer_range,
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pad_token_id=self.pad_token_id,
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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.to(torch_device)
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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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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(len(result.past_key_values), 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.to(torch_device)
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model.eval()
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result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (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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(
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}
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return config, inputs_dict
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def create_and_check_ctrl_for_sequence_classification(self, config, input_ids, head_mask, token_type_ids, *args):
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config.num_labels = self.num_labels
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model = CTRLForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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@require_torch
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class CTRLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
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all_generative_model_classes = (CTRLLMHeadModel,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": CTRLModel,
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"text-classification": CTRLForSequenceClassification,
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"text-generation": CTRLLMHeadModel,
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"zero-shot": CTRLForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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test_pruning = True
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test_resize_embeddings = False
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test_head_masking = False
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# TODO: Fix the failed tests
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
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# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
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# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
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# config could not be created.
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return True
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return False
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def setUp(self):
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self.model_tester = CTRLModelTester(self)
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self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37)
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def tearDown(self):
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super().tearDown()
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# clean-up as much as possible GPU memory occupied by PyTorch
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gc.collect()
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backend_empty_cache(torch_device)
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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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@slow
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def test_model_from_pretrained(self):
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for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = CTRLModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
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def test_left_padding_compatibility(self):
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pass
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@require_torch
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class CTRLModelLanguageGenerationTest(unittest.TestCase):
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def tearDown(self):
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super().tearDown()
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# clean-up as much as possible GPU memory occupied by PyTorch
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gc.collect()
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backend_empty_cache(torch_device)
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@slow
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def test_lm_generate_ctrl(self):
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model = CTRLLMHeadModel.from_pretrained("Salesforce/ctrl")
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model.to(torch_device)
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input_ids = torch.tensor(
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[[11859, 0, 1611, 8]], dtype=torch.long, device=torch_device
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) # Legal the president is
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expected_output_ids = [
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11859,
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0,
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1611,
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8,
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5,
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150,
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26449,
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2,
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19,
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348,
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469,
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3,
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2595,
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48,
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20740,
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246533,
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246533,
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19,
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30,
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5,
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] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
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output_ids = model.generate(input_ids, do_sample=False)
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self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
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