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Cleanup pytorch tests (#8033)
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@ -37,7 +37,6 @@ if is_torch_available():
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from transformers.pipelines import TranslationPipeline
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@require_torch
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class ModelTester:
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def __init__(self, parent):
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self.config = MarianConfig(
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@ -4,7 +4,6 @@ from transformers import is_torch_available
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from transformers.file_utils import cached_property
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from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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from .test_modeling_bart import TOLERANCE, _long_tensor, assert_tensors_close
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from .test_modeling_common import ModelTesterMixin
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@ -91,32 +90,6 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
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]
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expected_src_tokens = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
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@slow
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@unittest.skip("This has been failing since June 20th at least.")
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def test_enro_forward(self):
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model = self.model
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net_input = {
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"input_ids": _long_tensor(
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[
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[3493, 3060, 621, 104064, 1810, 100, 142, 566, 13158, 6889, 5, 2, 250004],
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[64511, 7, 765, 2837, 45188, 297, 4049, 237, 10, 122122, 5, 2, 250004],
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]
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),
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"decoder_input_ids": _long_tensor(
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[
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[250020, 31952, 144, 9019, 242307, 21980, 55749, 11, 5, 2, 1, 1],
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[250020, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2],
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]
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),
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}
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net_input["attention_mask"] = net_input["input_ids"].ne(1)
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with torch.no_grad():
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logits, *other_stuff = model(**net_input)
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expected_slice = torch.tensor([9.0078, 10.1113, 14.4787], device=logits.device, dtype=logits.dtype)
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result_slice = logits[0, 0, :3]
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assert_tensors_close(expected_slice, result_slice, atol=TOLERANCE)
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@slow
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def test_enro_generate_one(self):
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batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(
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@ -128,7 +101,7 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
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# self.assertEqual(self.tgt_text[1], decoded[1])
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@slow
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def test_enro_generate(self):
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def test_enro_generate_batch(self):
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batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(self.src_text).to(torch_device)
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translated_tokens = self.model.generate(**batch)
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decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
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@ -58,7 +58,7 @@ class PegasusXSUMIntegrationTest(AbstractSeq2SeqIntegrationTest):
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src_text = [PGE_ARTICLE, XSUM_ENTRY_LONGER]
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tgt_text = [
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"California's largest electricity provider has turned off power to hundreds of thousands of customers.",
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"N-Dubz have said they were surprised to get four nominations for this year's Mobo Awards.",
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"Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
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]
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@cached_property
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@ -72,7 +72,7 @@ class PegasusXSUMIntegrationTest(AbstractSeq2SeqIntegrationTest):
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torch_device
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)
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assert inputs.input_ids.shape == (2, 421)
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translated_tokens = self.model.generate(**inputs)
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translated_tokens = self.model.generate(**inputs, num_beams=2)
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decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
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assert self.tgt_text == decoded
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