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Remove static pretrained maps from the library's internals (#29112)

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* [test_all] Remove static pretrained maps from the library's internals

* Deprecate archive maps instead of removing them

* Revert init changes

* [test_all] Deprecate instead of removing

* [test_all] PVT v2 support

* [test_all] Tests should all pass

* [test_all] Style

* Address review comments

* Update src/transformers/models/deprecated/_archive_maps.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/deprecated/_archive_maps.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* [test_all] trigger tests

* [test_all] LLAVA

* [test_all] Bad rebase

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
Lysandre Debut
2024-03-25 10:33:38 +01:00
committed by GitHub
parent 76a33a1092
commit 39114c0383
842 changed files with 4608 additions and 8613 deletions
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188
tests/models/auto/test_modeling_auto.py
View File

@@ -85,10 +85,6 @@ if is_torch_available():
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
)
from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpt2.modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.t5.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tapas import TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
@require_torch
@@ -98,138 +94,134 @@ class AutoModelTest(unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModel.from_pretrained(model_name)
model, loading_info = AutoModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertModel)
model = AutoModel.from_pretrained(model_name)
model, loading_info = AutoModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertModel)
self.assertEqual(len(loading_info["missing_keys"]), 0)
# When using PyTorch checkpoint, the expected value is `8`. With `safetensors` checkpoint (if it is
# installed), the expected value becomes `7`.
EXPECTED_NUM_OF_UNEXPECTED_KEYS = 7 if is_safetensors_available() else 8
self.assertEqual(len(loading_info["unexpected_keys"]), EXPECTED_NUM_OF_UNEXPECTED_KEYS)
self.assertEqual(len(loading_info["mismatched_keys"]), 0)
self.assertEqual(len(loading_info["error_msgs"]), 0)
self.assertEqual(len(loading_info["missing_keys"]), 0)
# When using PyTorch checkpoint, the expected value is `8`. With `safetensors` checkpoint (if it is
# installed), the expected value becomes `7`.
EXPECTED_NUM_OF_UNEXPECTED_KEYS = 7 if is_safetensors_available() else 8
self.assertEqual(len(loading_info["unexpected_keys"]), EXPECTED_NUM_OF_UNEXPECTED_KEYS)
self.assertEqual(len(loading_info["mismatched_keys"]), 0)
self.assertEqual(len(loading_info["error_msgs"]), 0)
@slow
def test_model_for_pretraining_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForPreTraining.from_pretrained(model_name)
model, loading_info = AutoModelForPreTraining.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForPreTraining)
# Only one value should not be initialized and in the missing keys.
for key, value in loading_info.items():
self.assertEqual(len(value), 0)
model = AutoModelForPreTraining.from_pretrained(model_name)
model, loading_info = AutoModelForPreTraining.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForPreTraining)
# Only one value should not be initialized and in the missing keys.
for key, value in loading_info.items():
self.assertEqual(len(value), 0)
@slow
def test_lmhead_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelWithLMHead.from_pretrained(model_name)
model, loading_info = AutoModelWithLMHead.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
model = AutoModelWithLMHead.from_pretrained(model_name)
model, loading_info = AutoModelWithLMHead.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@slow
def test_model_for_causal_lm(self):
for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model = AutoModelForCausalLM.from_pretrained(model_name)
model, loading_info = AutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, GPT2LMHeadModel)
model = AutoModelForCausalLM.from_pretrained(model_name)
model, loading_info = AutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, GPT2LMHeadModel)
@slow
def test_model_for_masked_lm(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForMaskedLM.from_pretrained(model_name)
model, loading_info = AutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
model = AutoModelForMaskedLM.from_pretrained(model_name)
model, loading_info = AutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@slow
def test_model_for_encoder_decoder_lm(self):
for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, T5ForConditionalGeneration)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, T5ForConditionalGeneration)
@slow
def test_sequence_classification_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model, loading_info = AutoModelForSequenceClassification.from_pretrained(
model_name, output_loading_info=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForSequenceClassification)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model, loading_info = AutoModelForSequenceClassification.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForSequenceClassification)
@slow
def test_question_answering_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
model, loading_info = AutoModelForQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForQuestionAnswering)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
model, loading_info = AutoModelForQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForQuestionAnswering)
@slow
def test_table_question_answering_model_from_pretrained(self):
for model_name in TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, TapasConfig)
model_name = "google/tapas-base"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, TapasConfig)
model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
model, loading_info = AutoModelForTableQuestionAnswering.from_pretrained(
model_name, output_loading_info=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TapasForQuestionAnswering)
model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
model, loading_info = AutoModelForTableQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TapasForQuestionAnswering)
@slow
def test_token_classification_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForTokenClassification.from_pretrained(model_name)
model, loading_info = AutoModelForTokenClassification.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForTokenClassification)
model = AutoModelForTokenClassification.from_pretrained(model_name)
model, loading_info = AutoModelForTokenClassification.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForTokenClassification)
@slow
def test_auto_backbone_timm_model_from_pretrained(self):

90
tests/models/auto/test_modeling_tf_auto.py
View File

@@ -65,10 +65,6 @@ if is_tf_available():
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class NewModelConfig(BertConfig):
@@ -107,54 +103,54 @@ class TFAutoModelTest(unittest.TestCase):
@slow
def test_model_for_causal_lm(self):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model_name = "openai-community/gpt2"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model = TFAutoModelForCausalLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFGPT2LMHeadModel)
model = TFAutoModelForCausalLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFGPT2LMHeadModel)
@slow
def test_lmhead_model_from_pretrained(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "openai-community/gpt2"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = TFAutoModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
@slow
def test_model_for_masked_lm(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "openai-community/gpt2"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForMaskedLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = TFAutoModelForMaskedLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
@slow
def test_model_for_encoder_decoder_lm(self):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model_name = "openai-community/gpt2"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFT5ForConditionalGeneration)
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFT5ForConditionalGeneration)
@slow
def test_sequence_classification_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
# model_name = 'openai-community/gpt2'
for model_name in ["google-bert/bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
@@ -166,7 +162,7 @@ class TFAutoModelTest(unittest.TestCase):
@slow
def test_question_answering_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
# model_name = 'openai-community/gpt2'
for model_name in ["google-bert/bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
@@ -179,17 +175,17 @@ class TFAutoModelTest(unittest.TestCase):
@slow
@require_tensorflow_probability
def test_table_question_answering_model_from_pretrained(self):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, TapasConfig)
model_name = "google/tapas-base"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, TapasConfig)
model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name)
model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained(
model_name, output_loading_info=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFTapasForQuestionAnswering)
model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name)
model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained(
model_name, output_loading_info=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFTapasForQuestionAnswering)
def test_from_pretrained_identifier(self):
model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)

121
tests/models/auto/test_modeling_tf_pytorch.py
View File

@@ -45,9 +45,6 @@ if is_tf_available():
TFRobertaForMaskedLM,
TFT5ForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
@@ -74,7 +71,7 @@ if is_torch_available():
class TFPTAutoModelTest(unittest.TestCase):
@slow
def test_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
# model_name = 'google-bert/bert-base-uncased'
for model_name in ["google-bert/bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
@@ -90,7 +87,7 @@ class TFPTAutoModelTest(unittest.TestCase):
@slow
def test_model_for_pretraining_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
# model_name = 'google-bert/bert-base-uncased'
for model_name in ["google-bert/bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
@@ -106,85 +103,79 @@ class TFPTAutoModelTest(unittest.TestCase):
@slow
def test_model_for_causal_lm(self):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model = TFAutoModelForCausalLM.from_pretrained(model_name, from_pt=True)
model, loading_info = TFAutoModelForCausalLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFGPT2LMHeadModel)
model = TFAutoModelForCausalLM.from_pretrained(model_name, from_pt=True)
model, loading_info = TFAutoModelForCausalLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFGPT2LMHeadModel)
model = AutoModelForCausalLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForCausalLM.from_pretrained(
model_name, output_loading_info=True, from_tf=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, GPT2LMHeadModel)
model = AutoModelForCausalLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, GPT2LMHeadModel)
@slow
def test_lmhead_model_from_pretrained(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelWithLMHead.from_pretrained(model_name, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = TFAutoModelWithLMHead.from_pretrained(model_name, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = AutoModelWithLMHead.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
model = AutoModelWithLMHead.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@slow
def test_model_for_masked_lm(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForMaskedLM.from_pretrained(model_name, from_pt=True)
model, loading_info = TFAutoModelForMaskedLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = TFAutoModelForMaskedLM.from_pretrained(model_name, from_pt=True)
model, loading_info = TFAutoModelForMaskedLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = AutoModelForMaskedLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForMaskedLM.from_pretrained(
model_name, output_loading_info=True, from_tf=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
model = AutoModelForMaskedLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@slow
def test_model_for_encoder_decoder_lm(self):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model_name = "google-bert/bert-base-uncased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, from_pt=True)
model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFT5ForConditionalGeneration)
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, from_pt=True)
model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFT5ForConditionalGeneration)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(
model_name, output_loading_info=True, from_tf=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, T5ForConditionalGeneration)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, T5ForConditionalGeneration)
@slow
def test_sequence_classification_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
# model_name = 'google-bert/bert-base-uncased'
for model_name in ["google-bert/bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
@@ -200,7 +191,7 @@ class TFPTAutoModelTest(unittest.TestCase):
@slow
def test_question_answering_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
# model_name = 'google-bert/bert-base-uncased'
for model_name in ["google-bert/bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)

6
tests/models/auto/test_tokenization_auto.py
View File

@@ -25,8 +25,6 @@ import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
@@ -72,13 +70,13 @@ class AutoTokenizerTest(unittest.TestCase):
@slow
def test_tokenizer_from_pretrained(self):
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
for model_name in {"google-bert/bert-base-uncased", "google-bert/bert-base-cased"}:
tokenizer = AutoTokenizer.from_pretrained(model_name)
self.assertIsNotNone(tokenizer)
self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast))
self.assertGreater(len(tokenizer), 0)
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
for model_name in ["openai-community/gpt2", "openai-community/gpt2-medium"]:
tokenizer = AutoTokenizer.from_pretrained(model_name)
self.assertIsNotNone(tokenizer)
self.assertIsInstance(tokenizer, (GPT2Tokenizer, GPT2TokenizerFast))