65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
import unittest
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from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
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from transformers.testing_utils import require_flax, slow
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if is_flax_available():
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import jax
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from transformers.modeling_flax_auto import FlaxAutoModel
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from transformers.modeling_flax_bert import FlaxBertModel
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from transformers.modeling_flax_roberta import FlaxRobertaModel
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@require_flax
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class FlaxAutoModelTest(unittest.TestCase):
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@slow
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def test_bert_from_pretrained(self):
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for model_name in ["bert-base-cased", "bert-large-uncased"]:
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with self.subTest(model_name):
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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 = FlaxAutoModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, FlaxBertModel)
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@slow
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def test_roberta_from_pretrained(self):
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for model_name in ["roberta-base-cased", "roberta-large-uncased"]:
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with self.subTest(model_name):
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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 = FlaxAutoModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, FlaxRobertaModel)
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@slow
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def test_bert_jax_jit(self):
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for model_name in ["bert-base-cased", "bert-large-uncased"]:
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = FlaxBertModel.from_pretrained(model_name)
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tokens = tokenizer("Do you support jax jitted function?", return_tensors=TensorType.JAX)
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@jax.jit
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def eval(**kwargs):
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return model(**kwargs)
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eval(**tokens).block_until_ready()
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@slow
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def test_roberta_jax_jit(self):
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for model_name in ["roberta-base-cased", "roberta-large-uncased"]:
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = FlaxRobertaModel.from_pretrained(model_name)
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tokens = tokenizer("Do you support jax jitted function?", return_tensors=TensorType.JAX)
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@jax.jit
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def eval(**kwargs):
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return model(**kwargs)
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eval(**tokens).block_until_ready()
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