[tests|tokenizers] Refactoring pipelines test backbone - Small tokenizers improvements - General tests speedups (#7970)
* WIP refactoring pipeline tests - switching to fast tokenizers * fix dialog pipeline and fill-mask * refactoring pipeline tests backbone * make large tests slow * fix tests (tf Bart inactive for now) * fix doc... * clean up for merge * fixing tests - remove bart from summarization until there is TF * fix quality and RAG * Add new translation pipeline tests - fix JAX tests * only slow for dialog * Fixing the missing TF-BART imports in modeling_tf_auto * spin out pipeline tests in separate CI job * adding pipeline test to CI YAML * add slow pipeline tests * speed up tf and pt join test to avoid redoing all the standalone pt and tf tests * Update src/transformers/tokenization_utils_base.py Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * Update src/transformers/pipelines.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/testing_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * add require_torch and require_tf in is_pt_tf_cross_test Co-authored-by: Sam Shleifer <sshleifer@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Thomas Wolf
243
tests/test_modeling_tf_pytorch.py
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243
tests/test_modeling_tf_pytorch.py
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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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 unittest
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from transformers import is_tf_available, is_torch_available
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from transformers.testing_utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
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if is_tf_available():
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from transformers import (
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AutoConfig,
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BertConfig,
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GPT2Config,
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T5Config,
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TFAutoModel,
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TFAutoModelForCausalLM,
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TFAutoModelForMaskedLM,
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TFAutoModelForPreTraining,
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TFAutoModelForQuestionAnswering,
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TFAutoModelForSeq2SeqLM,
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TFAutoModelForSequenceClassification,
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TFAutoModelWithLMHead,
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TFBertForMaskedLM,
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TFBertForPreTraining,
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TFBertForQuestionAnswering,
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TFBertForSequenceClassification,
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TFBertModel,
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TFGPT2LMHeadModel,
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TFRobertaForMaskedLM,
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TFT5ForConditionalGeneration,
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)
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from transformers.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
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if is_torch_available():
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from transformers import (
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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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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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BertModel,
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GPT2LMHeadModel,
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RobertaForMaskedLM,
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T5ForConditionalGeneration,
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)
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@is_pt_tf_cross_test
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class TFPTAutoModelTest(unittest.TestCase):
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@slow
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def test_model_from_pretrained(self):
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import h5py
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self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["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 = TFAutoModel.from_pretrained(model_name, from_pt=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertModel)
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model = AutoModel.from_pretrained(model_name, from_tf=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertModel)
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@slow
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def test_model_for_pretraining_from_pretrained(self):
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import h5py
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self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["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 = TFAutoModelForPreTraining.from_pretrained(model_name, from_pt=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForPreTraining)
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model = AutoModelForPreTraining.from_pretrained(model_name, from_tf=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForPreTraining)
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@slow
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def test_model_for_causal_lm(self):
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for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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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 = TFAutoModelForCausalLM.from_pretrained(model_name, from_pt=True)
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model, loading_info = TFAutoModelForCausalLM.from_pretrained(
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model_name, output_loading_info=True, from_pt=True
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)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFGPT2LMHeadModel)
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model = AutoModelForCausalLM.from_pretrained(model_name, from_tf=True)
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model, loading_info = AutoModelForCausalLM.from_pretrained(
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model_name, output_loading_info=True, from_tf=True
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)
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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_lmhead_model_from_pretrained(self):
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for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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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 = TFAutoModelWithLMHead.from_pretrained(model_name, from_pt=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForMaskedLM)
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model = AutoModelWithLMHead.from_pretrained(model_name, from_tf=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_masked_lm(self):
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for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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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 = TFAutoModelForMaskedLM.from_pretrained(model_name, from_pt=True)
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model, loading_info = TFAutoModelForMaskedLM.from_pretrained(
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model_name, output_loading_info=True, from_pt=True
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)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForMaskedLM)
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model = AutoModelForMaskedLM.from_pretrained(model_name, from_tf=True)
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model, loading_info = AutoModelForMaskedLM.from_pretrained(
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model_name, output_loading_info=True, from_tf=True
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)
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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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for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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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 = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, from_pt=True)
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model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(
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model_name, output_loading_info=True, from_pt=True
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)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFT5ForConditionalGeneration)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name, from_tf=True)
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model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(
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model_name, output_loading_info=True, from_tf=True
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)
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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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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["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 = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForSequenceClassification)
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model = AutoModelForSequenceClassification.from_pretrained(model_name, from_tf=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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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["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 = TFAutoModelForQuestionAnswering.from_pretrained(model_name, from_pt=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForQuestionAnswering)
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model = AutoModelForQuestionAnswering.from_pretrained(model_name, from_tf=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, BertForQuestionAnswering)
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def test_from_pretrained_identifier(self):
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model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER, from_pt=True)
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self.assertIsInstance(model, TFBertForMaskedLM)
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self.assertEqual(model.num_parameters(), 14830)
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self.assertEqual(model.num_parameters(only_trainable=True), 14830)
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model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER, from_tf=True)
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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 = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKWOWN_IDENTIFIER, from_pt=True)
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self.assertIsInstance(model, TFRobertaForMaskedLM)
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self.assertEqual(model.num_parameters(), 14830)
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self.assertEqual(model.num_parameters(only_trainable=True), 14830)
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model = AutoModelWithLMHead.from_pretrained(DUMMY_UNKWOWN_IDENTIFIER, from_tf=True)
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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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