Mark big downloads slow (#7325)
* Make big downloads as slow * Add import * Right order for slow decorator * More slow tests
This commit is contained in:
Sylvain Gugger
GitHub
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585217c87f
commit
1ee2194fb6
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@ -1,7 +1,7 @@
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import unittest
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from transformers import AutoTokenizer, is_torch_available
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from transformers.testing_utils import require_torch
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from transformers.testing_utils import require_torch, slow
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if is_torch_available():
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@ -69,6 +69,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
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self.assertTrue("labels" not in batch)
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self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
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@slow
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def test_default_classification(self):
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MODEL_ID = "bert-base-cased-finetuned-mrpc"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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@ -80,6 +81,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
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batch = data_collator(dataset.features)
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self.assertEqual(batch["labels"].dtype, torch.long)
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@slow
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def test_default_regression(self):
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MODEL_ID = "distilroberta-base"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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@ -91,6 +93,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
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batch = data_collator(dataset.features)
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self.assertEqual(batch["labels"].dtype, torch.float)
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@slow
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def test_lm_tokenizer_without_padding(self):
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tokenizer = AutoTokenizer.from_pretrained("gpt2")
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data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
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@ -109,6 +112,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 512)))
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@slow
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def test_lm_tokenizer_with_padding(self):
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tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
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data_collator = DataCollatorForLanguageModeling(tokenizer)
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@ -128,6 +132,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 512)))
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@slow
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def test_plm(self):
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tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
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data_collator = DataCollatorForPermutationLanguageModeling(tokenizer)
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@ -156,6 +161,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
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# Expect error due to odd sequence length
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data_collator(example)
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@slow
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def test_nsp(self):
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tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
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data_collator = DataCollatorForNextSentencePrediction(tokenizer)
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@ -172,6 +178,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
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self.assertEqual(batch["masked_lm_labels"].shape, torch.Size((total_samples, 512)))
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self.assertEqual(batch["next_sentence_label"].shape, torch.Size((total_samples,)))
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@slow
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def test_sop(self):
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tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
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data_collator = DataCollatorForSOP(tokenizer)
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@ -31,7 +31,7 @@ TF_TRANSLATION_FINETUNED_MODELS = [("patrickvonplaten/t5-tiny-random", "translat
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TEXT2TEXT_FINETUNED_MODELS = ["patrickvonplaten/t5-tiny-random"]
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TF_TEXT2TEXT_FINETUNED_MODELS = ["patrickvonplaten/t5-tiny-random"]
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DIALOGUE_FINETUNED_MODELS = ["microsoft/DialoGPT-medium"]
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DIALOGUE_FINETUNED_MODELS = ["microsoft/DialoGPT-medium"] # @slow
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expected_fill_mask_result = [
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[
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@ -350,8 +350,8 @@ class MonoColumnInputTestCase(unittest.TestCase):
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nlp, VALID_INPUTS, mandatory_keys, invalid_inputs=invalid_inputs, **SUMMARIZATION_KWARGS
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)
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@slow
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@require_torch
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@slow
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def test_integration_torch_summarization(self):
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nlp = pipeline(task="summarization", device=DEFAULT_DEVICE_NUM)
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cnn_article = ' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based. The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the situation in Palestinian territories, paving the way for possible war crimes investigations against Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the world is also a step closer to ending a long era of impunity and injustice," he said, according to an ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine acquires all the rights as well as responsibilities that come with being a State Party to the Statute. These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should immediately end their pressure, and countries that support universal acceptance of the court\'s treaty should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the group. "What\'s objectionable is the attempts to undermine international justice, not Palestine\'s decision to join a treaty to which over 100 countries around the world are members." In January, when the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an outrage, saying the court was overstepping its boundaries. The United States also said it "strongly" disagreed with the court\'s decision. "As we have said repeatedly, we do not believe that Palestine is a state and therefore we do not believe that it is eligible to join the ICC," the State Department said in a statement. It urged the warring sides to resolve their differences through direct negotiations. "We will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace," it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou Bensouda said her office would "conduct its analysis in full independence and impartiality." The war between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry will include alleged war crimes committed since June. The International Criminal Court was set up in 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder and Faith Karimi contributed to this report.'
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@ -359,8 +359,8 @@ class MonoColumnInputTestCase(unittest.TestCase):
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result = nlp(cnn_article)
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self.assertEqual(result[0]["summary_text"], expected_cnn_summary)
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@slow
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@require_tf
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@slow
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def test_tf_summarization(self):
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invalid_inputs = [4, "<mask>"]
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mandatory_keys = ["summary_text"]
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@ -433,8 +433,8 @@ class MonoColumnInputTestCase(unittest.TestCase):
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self._test_mono_column_pipeline(nlp, VALID_INPUTS, {})
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self._test_mono_column_pipeline(nlp, VALID_INPUTS, {}, prefix="This is ")
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@slow
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@require_torch
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@slow
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def test_integration_torch_conversation(self):
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# When
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nlp = pipeline(task="conversational", device=DEFAULT_DEVICE_NUM)
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@ -465,8 +465,8 @@ class MonoColumnInputTestCase(unittest.TestCase):
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self.assertEqual(result.past_user_inputs[1], "Why do you recommend it?")
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self.assertEqual(result.generated_responses[1], "It's a good book.")
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@slow
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@require_torch
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@slow
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def test_integration_torch_conversation_truncated_history(self):
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# When
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nlp = pipeline(task="conversational", min_length_for_response=24, device=DEFAULT_DEVICE_NUM)
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@ -614,14 +614,14 @@ class ZeroShotClassificationPipelineTests(unittest.TestCase):
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nlp = pipeline(task="zero-shot-classification", model=model_name, tokenizer=model_name, framework="tf")
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self._test_zero_shot_pipeline(nlp)
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@slow
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@require_torch
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@slow
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def test_torch_zero_shot_outputs(self):
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nlp = pipeline(task="zero-shot-classification", model="roberta-large-mnli")
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self._test_zero_shot_pipeline_outputs(nlp)
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@slow
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@require_tf
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@slow
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def test_tf_zero_shot_outputs(self):
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nlp = pipeline(task="zero-shot-classification", model="roberta-large-mnli", framework="tf")
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self._test_zero_shot_pipeline_outputs(nlp)
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@ -647,12 +647,14 @@ class DialoguePipelineTests(unittest.TestCase):
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self.assertRaises(Exception, nlp, invalid_inputs)
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@require_torch
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@slow
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def test_torch_conversation(self):
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for model_name in DIALOGUE_FINETUNED_MODELS:
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nlp = pipeline(task="conversational", model=model_name, tokenizer=model_name)
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self._test_conversation_pipeline(nlp)
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@require_tf
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@slow
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def test_tf_conversation(self):
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for model_name in DIALOGUE_FINETUNED_MODELS:
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nlp = pipeline(task="conversational", model=model_name, tokenizer=model_name, framework="tf")
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@ -806,16 +808,16 @@ class NerPipelineTests(unittest.TestCase):
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class PipelineCommonTests(unittest.TestCase):
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pipelines = SUPPORTED_TASKS.keys()
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@slow
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@require_tf
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@slow
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def test_tf_defaults(self):
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# Test that pipelines can be correctly loaded without any argument
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for task in self.pipelines:
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with self.subTest(msg="Testing TF defaults with TF and {}".format(task)):
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pipeline(task, framework="tf")
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@slow
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@require_torch
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@slow
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def test_pt_defaults(self):
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# Test that pipelines can be correctly loaded without any argument
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for task in self.pipelines:
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@ -4,7 +4,7 @@ import datasets
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import numpy as np
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from transformers import AutoTokenizer, TrainingArguments, is_torch_available
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from transformers.testing_utils import get_tests_dir, require_torch
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from transformers.testing_utils import get_tests_dir, require_torch, slow
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if is_torch_available():
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@ -287,6 +287,7 @@ class TrainerIntegrationTest(unittest.TestCase):
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trainer.train()
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self.check_trained_model(trainer.model, alternate_seed=True)
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@slow
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def test_trainer_eval_mrpc(self):
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MODEL_ID = "bert-base-cased-finetuned-mrpc"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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@ -301,6 +302,7 @@ class TrainerIntegrationTest(unittest.TestCase):
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result = trainer.evaluate()
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self.assertLess(result["eval_loss"], 0.2)
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@slow
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def test_trainer_eval_lm(self):
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MODEL_ID = "distilroberta-base"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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