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* Result of black 23.1 * Update target to Python 3.7 * Switch flake8 to ruff * Configure isort * Configure isort * Apply isort with line limit * Put the right black version * adapt black in check copies * Fix copies
230 lines
9.1 KiB
Python
230 lines
9.1 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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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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""" Testing suite for the PyTorch GPTNeoX model. """
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import unittest
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from transformers import GPTNeoXConfig, is_torch_available
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from transformers.testing_utils import require_torch, torch_device
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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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if is_torch_available():
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import torch
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from transformers import GPTNeoXForCausalLM, GPTNeoXModel
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class GPTNeoXModelTester:
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def __init__(
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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_input_mask=True,
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use_token_type_ids=True,
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use_labels=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_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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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 = random_attention_mask([self.batch_size, self.seq_length])
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token_labels = None
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if self.use_labels:
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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config = self.get_config()
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return config, input_ids, input_mask, token_labels
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def get_config(self):
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return GPTNeoXConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=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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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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)
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def prepare_config_and_inputs_for_decoder(self):
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config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs()
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config.is_decoder = True
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return config, input_ids, input_mask, token_labels
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def create_and_check_model(self, config, input_ids, input_mask):
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model = GPTNeoXModel(config=config)
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model.to(torch_device)
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model.eval()
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_ = model(input_ids, attention_mask=input_mask)
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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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def create_and_check_model_as_decoder(self, config, input_ids, input_mask):
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config.add_cross_attention = True
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model = GPTNeoXModel(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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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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def create_and_check_for_causal_lm(self, config, input_ids, input_mask, token_labels):
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model = GPTNeoXForCausalLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_decoder_model_past_large_inputs(self, config, input_ids, input_mask):
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config.is_decoder = True
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model = GPTNeoXForCausalLM(config=config)
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model.to(torch_device)
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model.eval()
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# first forward pass
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outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
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past_key_values = outputs.past_key_values
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
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output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True)
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output_from_no_past = output_from_no_past["hidden_states"][0]
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output_from_past = model(
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next_tokens,
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attention_mask=next_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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)["hidden_states"][0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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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, token_labels = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class GPTNeoXModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (GPTNeoXModel, GPTNeoXForCausalLM) if is_torch_available() else ()
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all_generative_model_classes = (GPTNeoXForCausalLM,) if is_torch_available() else ()
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test_pruning = False
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test_missing_keys = False
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test_model_parallel = False
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test_head_masking = False
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def setUp(self):
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self.model_tester = GPTNeoXModelTester(self)
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self.config_tester = ConfigTester(self, config_class=GPTNeoXConfig, hidden_size=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_model(self):
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config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(config, input_ids, input_mask)
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def test_model_as_decoder(self):
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config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
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self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
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def test_model_as_decoder_with_default_input_mask(self):
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# This regression test was failing with PyTorch < 1.3
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config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
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input_mask = None
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self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
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def test_decoder_model_past_large_inputs(self):
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config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_decoder_model_past_large_inputs(config, input_ids, input_mask)
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def test_model_for_causal_lm(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_for_causal_lm(*config_and_inputs)
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@unittest.skip(reason="Feed forward chunking is not implemented")
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def test_feed_forward_chunking(self):
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pass
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