532 lines
23 KiB
Python
532 lines
23 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace 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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import copy
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import sys
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import tempfile
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import unittest
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from collections import OrderedDict
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from pathlib import Path
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import pytest
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import transformers
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from transformers import BertConfig, GPT2Model, is_safetensors_available, is_torch_available
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from transformers.models.auto.configuration_auto import CONFIG_MAPPING
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from transformers.testing_utils import (
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DUMMY_UNKNOWN_IDENTIFIER,
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SMALL_MODEL_IDENTIFIER,
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RequestCounter,
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require_torch,
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slow,
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)
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from ..bert.test_modeling_bert import BertModelTester
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sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
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from test_module.custom_configuration import CustomConfig # noqa E402
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if is_torch_available():
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import torch
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from test_module.custom_modeling import CustomModel
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from transformers import (
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AutoBackbone,
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AutoConfig,
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AutoModel,
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AutoModelForCausalLM,
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AutoModelForMaskedLM,
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AutoModelForPreTraining,
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AutoModelForQuestionAnswering,
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AutoModelForSeq2SeqLM,
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AutoModelForSequenceClassification,
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AutoModelForTableQuestionAnswering,
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AutoModelForTokenClassification,
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AutoModelWithLMHead,
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BertForMaskedLM,
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BertForPreTraining,
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BertForQuestionAnswering,
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BertForSequenceClassification,
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BertForTokenClassification,
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BertModel,
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FunnelBaseModel,
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FunnelModel,
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GPT2Config,
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GPT2LMHeadModel,
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ResNetBackbone,
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RobertaForMaskedLM,
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T5Config,
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T5ForConditionalGeneration,
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TapasConfig,
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TapasForQuestionAnswering,
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TimmBackbone,
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)
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from transformers.models.auto.modeling_auto import (
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MODEL_FOR_CAUSAL_LM_MAPPING,
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MODEL_FOR_MASKED_LM_MAPPING,
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MODEL_FOR_PRETRAINING_MAPPING,
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MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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MODEL_MAPPING,
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)
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@require_torch
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class AutoModelTest(unittest.TestCase):
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def setUp(self):
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transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
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@slow
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def test_model_from_pretrained(self):
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model_name = "google-bert/bert-base-uncased"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModel.from_pretrained(model_name)
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model, loading_info = AutoModel.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertModel)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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# When using PyTorch checkpoint, the expected value is `8`. With `safetensors` checkpoint (if it is
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# installed), the expected value becomes `7`.
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EXPECTED_NUM_OF_UNEXPECTED_KEYS = 7 if is_safetensors_available() else 8
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self.assertEqual(len(loading_info["unexpected_keys"]), EXPECTED_NUM_OF_UNEXPECTED_KEYS)
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self.assertEqual(len(loading_info["mismatched_keys"]), 0)
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self.assertEqual(len(loading_info["error_msgs"]), 0)
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@slow
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def test_model_for_pretraining_from_pretrained(self):
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model_name = "google-bert/bert-base-uncased"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForPreTraining.from_pretrained(model_name)
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model, loading_info = AutoModelForPreTraining.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForPreTraining)
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# Only one value should not be initialized and in the missing keys.
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for key, value in loading_info.items():
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self.assertEqual(len(value), 0)
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@slow
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def test_lmhead_model_from_pretrained(self):
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model_name = "google-bert/bert-base-uncased"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelWithLMHead.from_pretrained(model_name)
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model, loading_info = AutoModelWithLMHead.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForMaskedLM)
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@slow
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def test_model_for_causal_lm(self):
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model_name = "openai-community/gpt2"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, GPT2Config)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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model, loading_info = AutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, GPT2LMHeadModel)
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@slow
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def test_model_for_masked_lm(self):
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model_name = "google-bert/bert-base-uncased"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForMaskedLM.from_pretrained(model_name)
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model, loading_info = AutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForMaskedLM)
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@slow
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def test_model_for_encoder_decoder_lm(self):
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model_name = "google-t5/t5-base"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, T5Config)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, T5ForConditionalGeneration)
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@slow
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def test_sequence_classification_model_from_pretrained(self):
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model_name = "google-bert/bert-base-uncased"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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model, loading_info = AutoModelForSequenceClassification.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForSequenceClassification)
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@slow
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def test_question_answering_model_from_pretrained(self):
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model_name = "google-bert/bert-base-uncased"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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model, loading_info = AutoModelForQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForQuestionAnswering)
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@slow
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def test_table_question_answering_model_from_pretrained(self):
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model_name = "google/tapas-base"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, TapasConfig)
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model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
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model, loading_info = AutoModelForTableQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TapasForQuestionAnswering)
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@slow
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def test_token_classification_model_from_pretrained(self):
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model_name = "google-bert/bert-base-uncased"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = AutoModelForTokenClassification.from_pretrained(model_name)
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model, loading_info = AutoModelForTokenClassification.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForTokenClassification)
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@slow
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def test_auto_backbone_timm_model_from_pretrained(self):
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# Configs can't be loaded for timm models
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model = AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True)
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with pytest.raises(ValueError):
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# We can't pass output_loading_info=True as we're loading from timm
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AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TimmBackbone)
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# Check kwargs are correctly passed to the backbone
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model = AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True, out_indices=(-2, -1))
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self.assertEqual(model.out_indices, (-2, -1))
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# Check out_features cannot be passed to Timm backbones
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with self.assertRaises(ValueError):
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_ = AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True, out_features=["stage1"])
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@slow
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def test_auto_backbone_from_pretrained(self):
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model = AutoBackbone.from_pretrained("microsoft/resnet-18")
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model, loading_info = AutoBackbone.from_pretrained("microsoft/resnet-18", output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, ResNetBackbone)
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# Check kwargs are correctly passed to the backbone
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model = AutoBackbone.from_pretrained("microsoft/resnet-18", out_indices=[-2, -1])
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self.assertEqual(model.out_indices, [-2, -1])
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self.assertEqual(model.out_features, ["stage3", "stage4"])
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model = AutoBackbone.from_pretrained("microsoft/resnet-18", out_features=["stage2", "stage4"])
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self.assertEqual(model.out_indices, [2, 4])
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self.assertEqual(model.out_features, ["stage2", "stage4"])
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def test_from_pretrained_identifier(self):
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model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
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self.assertIsInstance(model, BertForMaskedLM)
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self.assertEqual(model.num_parameters(), 14410)
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self.assertEqual(model.num_parameters(only_trainable=True), 14410)
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def test_from_identifier_from_model_type(self):
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model = AutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER)
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self.assertIsInstance(model, RobertaForMaskedLM)
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self.assertEqual(model.num_parameters(), 14410)
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self.assertEqual(model.num_parameters(only_trainable=True), 14410)
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def test_from_pretrained_with_tuple_values(self):
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# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
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model = AutoModel.from_pretrained("sgugger/funnel-random-tiny")
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self.assertIsInstance(model, FunnelModel)
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config = copy.deepcopy(model.config)
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config.architectures = ["FunnelBaseModel"]
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model = AutoModel.from_config(config)
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self.assertIsInstance(model, FunnelBaseModel)
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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model = AutoModel.from_pretrained(tmp_dir)
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self.assertIsInstance(model, FunnelBaseModel)
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def test_from_pretrained_dynamic_model_local(self):
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try:
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AutoConfig.register("custom", CustomConfig)
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AutoModel.register(CustomConfig, CustomModel)
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config = CustomConfig(hidden_size=32)
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model = CustomModel(config)
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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new_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
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for p1, p2 in zip(model.parameters(), new_model.parameters()):
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self.assertTrue(torch.equal(p1, p2))
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finally:
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if "custom" in CONFIG_MAPPING._extra_content:
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del CONFIG_MAPPING._extra_content["custom"]
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if CustomConfig in MODEL_MAPPING._extra_content:
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del MODEL_MAPPING._extra_content[CustomConfig]
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def test_from_pretrained_dynamic_model_distant(self):
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# If remote code is not set, we will time out when asking whether to load the model.
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with self.assertRaises(ValueError):
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model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model")
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# If remote code is disabled, we can't load this config.
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with self.assertRaises(ValueError):
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model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
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model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
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self.assertEqual(model.__class__.__name__, "NewModel")
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# Test model can be reloaded.
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
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self.assertEqual(reloaded_model.__class__.__name__, "NewModel")
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for p1, p2 in zip(model.parameters(), reloaded_model.parameters()):
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self.assertTrue(torch.equal(p1, p2))
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# This one uses a relative import to a util file, this checks it is downloaded and used properly.
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model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model_with_util", trust_remote_code=True)
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self.assertEqual(model.__class__.__name__, "NewModel")
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# Test model can be reloaded.
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
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self.assertEqual(reloaded_model.__class__.__name__, "NewModel")
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for p1, p2 in zip(model.parameters(), reloaded_model.parameters()):
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self.assertTrue(torch.equal(p1, p2))
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def test_from_pretrained_dynamic_model_distant_with_ref(self):
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model = AutoModel.from_pretrained("hf-internal-testing/ref_to_test_dynamic_model", trust_remote_code=True)
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self.assertEqual(model.__class__.__name__, "NewModel")
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# Test model can be reloaded.
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
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self.assertEqual(reloaded_model.__class__.__name__, "NewModel")
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for p1, p2 in zip(model.parameters(), reloaded_model.parameters()):
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self.assertTrue(torch.equal(p1, p2))
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# This one uses a relative import to a util file, this checks it is downloaded and used properly.
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model = AutoModel.from_pretrained(
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"hf-internal-testing/ref_to_test_dynamic_model_with_util", trust_remote_code=True
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)
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self.assertEqual(model.__class__.__name__, "NewModel")
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# Test model can be reloaded.
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
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self.assertEqual(reloaded_model.__class__.__name__, "NewModel")
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for p1, p2 in zip(model.parameters(), reloaded_model.parameters()):
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self.assertTrue(torch.equal(p1, p2))
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def test_from_pretrained_dynamic_model_with_period(self):
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# We used to have issues where repos with "." in the name would cause issues because the Python
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# import machinery would treat that as a directory separator, so we test that case
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# If remote code is not set, we will time out when asking whether to load the model.
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with self.assertRaises(ValueError):
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model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model_v1.0")
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# If remote code is disabled, we can't load this config.
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with self.assertRaises(ValueError):
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model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model_v1.0", trust_remote_code=False)
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model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model_v1.0", trust_remote_code=True)
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self.assertEqual(model.__class__.__name__, "NewModel")
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# Test that it works with a custom cache dir too
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with tempfile.TemporaryDirectory() as tmp_dir:
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model = AutoModel.from_pretrained(
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"hf-internal-testing/test_dynamic_model_v1.0", trust_remote_code=True, cache_dir=tmp_dir
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)
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self.assertEqual(model.__class__.__name__, "NewModel")
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def test_new_model_registration(self):
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AutoConfig.register("custom", CustomConfig)
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auto_classes = [
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AutoModel,
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AutoModelForCausalLM,
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AutoModelForMaskedLM,
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AutoModelForPreTraining,
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AutoModelForQuestionAnswering,
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AutoModelForSequenceClassification,
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AutoModelForTokenClassification,
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]
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try:
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for auto_class in auto_classes:
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with self.subTest(auto_class.__name__):
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# Wrong config class will raise an error
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with self.assertRaises(ValueError):
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auto_class.register(BertConfig, CustomModel)
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auto_class.register(CustomConfig, CustomModel)
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# Trying to register something existing in the Transformers library will raise an error
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with self.assertRaises(ValueError):
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auto_class.register(BertConfig, BertModel)
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# Now that the config is registered, it can be used as any other config with the auto-API
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tiny_config = BertModelTester(self).get_config()
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config = CustomConfig(**tiny_config.to_dict())
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model = auto_class.from_config(config)
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self.assertIsInstance(model, CustomModel)
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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new_model = auto_class.from_pretrained(tmp_dir)
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# The model is a CustomModel but from the new dynamically imported class.
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self.assertIsInstance(new_model, CustomModel)
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finally:
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if "custom" in CONFIG_MAPPING._extra_content:
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del CONFIG_MAPPING._extra_content["custom"]
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for mapping in (
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MODEL_MAPPING,
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MODEL_FOR_PRETRAINING_MAPPING,
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MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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MODEL_FOR_CAUSAL_LM_MAPPING,
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MODEL_FOR_MASKED_LM_MAPPING,
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):
|
|
if CustomConfig in mapping._extra_content:
|
|
del mapping._extra_content[CustomConfig]
|
|
|
|
def test_from_pretrained_dynamic_model_conflict(self):
|
|
class NewModelConfigLocal(BertConfig):
|
|
model_type = "new-model"
|
|
|
|
class NewModel(BertModel):
|
|
config_class = NewModelConfigLocal
|
|
|
|
try:
|
|
AutoConfig.register("new-model", NewModelConfigLocal)
|
|
AutoModel.register(NewModelConfigLocal, NewModel)
|
|
# If remote code is not set, the default is to use local
|
|
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model")
|
|
self.assertEqual(model.config.__class__.__name__, "NewModelConfigLocal")
|
|
|
|
# If remote code is disabled, we load the local one.
|
|
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
|
|
self.assertEqual(model.config.__class__.__name__, "NewModelConfigLocal")
|
|
|
|
# If remote is enabled, we load from the Hub
|
|
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
|
|
self.assertEqual(model.config.__class__.__name__, "NewModelConfig")
|
|
|
|
finally:
|
|
if "new-model" in CONFIG_MAPPING._extra_content:
|
|
del CONFIG_MAPPING._extra_content["new-model"]
|
|
if NewModelConfigLocal in MODEL_MAPPING._extra_content:
|
|
del MODEL_MAPPING._extra_content[NewModelConfigLocal]
|
|
|
|
def test_repo_not_found(self):
|
|
with self.assertRaisesRegex(
|
|
EnvironmentError, "bert-base is not a local folder and is not a valid model identifier"
|
|
):
|
|
_ = AutoModel.from_pretrained("bert-base")
|
|
|
|
def test_revision_not_found(self):
|
|
with self.assertRaisesRegex(
|
|
EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
|
|
):
|
|
_ = AutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
|
|
|
|
def test_model_file_not_found(self):
|
|
with self.assertRaisesRegex(
|
|
EnvironmentError,
|
|
"hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin",
|
|
):
|
|
_ = AutoModel.from_pretrained("hf-internal-testing/config-no-model")
|
|
|
|
def test_model_from_tf_suggestion(self):
|
|
with self.assertRaisesRegex(EnvironmentError, "Use `from_tf=True` to load this model"):
|
|
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-bert-tf-only")
|
|
|
|
def test_model_from_flax_suggestion(self):
|
|
with self.assertRaisesRegex(EnvironmentError, "Use `from_flax=True` to load this model"):
|
|
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
|
|
|
|
def test_cached_model_has_minimum_calls_to_head(self):
|
|
# Make sure we have cached the model.
|
|
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
with RequestCounter() as counter:
|
|
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert")
|
|
self.assertEqual(counter["GET"], 0)
|
|
self.assertEqual(counter["HEAD"], 1)
|
|
self.assertEqual(counter.total_calls, 1)
|
|
|
|
# With a sharded checkpoint
|
|
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
|
|
with RequestCounter() as counter:
|
|
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
|
|
self.assertEqual(counter["GET"], 0)
|
|
self.assertEqual(counter["HEAD"], 1)
|
|
self.assertEqual(counter.total_calls, 1)
|
|
|
|
def test_attr_not_existing(self):
|
|
from transformers.models.auto.auto_factory import _LazyAutoMapping
|
|
|
|
_CONFIG_MAPPING_NAMES = OrderedDict([("bert", "BertConfig")])
|
|
_MODEL_MAPPING_NAMES = OrderedDict([("bert", "GhostModel")])
|
|
_MODEL_MAPPING = _LazyAutoMapping(_CONFIG_MAPPING_NAMES, _MODEL_MAPPING_NAMES)
|
|
|
|
with pytest.raises(ValueError, match=r"Could not find GhostModel neither in .* nor in .*!"):
|
|
_MODEL_MAPPING[BertConfig]
|
|
|
|
_MODEL_MAPPING_NAMES = OrderedDict([("bert", "BertModel")])
|
|
_MODEL_MAPPING = _LazyAutoMapping(_CONFIG_MAPPING_NAMES, _MODEL_MAPPING_NAMES)
|
|
self.assertEqual(_MODEL_MAPPING[BertConfig], BertModel)
|
|
|
|
_MODEL_MAPPING_NAMES = OrderedDict([("bert", "GPT2Model")])
|
|
_MODEL_MAPPING = _LazyAutoMapping(_CONFIG_MAPPING_NAMES, _MODEL_MAPPING_NAMES)
|
|
self.assertEqual(_MODEL_MAPPING[BertConfig], GPT2Model)
|