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1073a2bde5
* Use the CI to identify failing tests * Remove from all examples and tests * More default switch * Fixes * More test fixes * More fixes * Last fixes hopefully * Use the CI to identify failing tests * Remove from all examples and tests * More default switch * Fixes * More test fixes * More fixes * Last fixes hopefully * Run on the real suite * Fix slow tests
43 lines
1.7 KiB
Python
43 lines
1.7 KiB
Python
import unittest
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from numpy import ndarray
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from transformers import TensorType, is_flax_available, is_torch_available
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from transformers.testing_utils import require_flax, require_torch
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from transformers.tokenization_roberta_fast import RobertaTokenizerFast
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if is_flax_available():
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from transformers.modeling_flax_roberta import FlaxRobertaModel
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if is_torch_available():
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import torch
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from transformers.modeling_roberta import RobertaModel
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@require_flax
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@require_torch
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class FlaxRobertaModelTest(unittest.TestCase):
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def test_from_pytorch(self):
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with torch.no_grad():
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with self.subTest("roberta-base"):
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tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
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fx_model = FlaxRobertaModel.from_pretrained("roberta-base")
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pt_model = RobertaModel.from_pretrained("roberta-base")
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# Check for simple input
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pt_inputs = tokenizer.encode_plus("This is a simple input", return_tensors=TensorType.PYTORCH)
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fx_inputs = tokenizer.encode_plus("This is a simple input", return_tensors=TensorType.JAX)
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pt_outputs = pt_model(**pt_inputs)
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fx_outputs = fx_model(**fx_inputs)
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self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
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for fx_output, pt_output in zip(fx_outputs, pt_outputs.to_tuple()):
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self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-4)
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def assert_almost_equals(self, a: ndarray, b: ndarray, tol: float):
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diff = (a - b).sum()
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self.assertLessEqual(diff, tol, "Difference between torch and flax is {} (>= {})".format(diff, tol))
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