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84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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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 numpy import ndarray
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from transformers import BertTokenizerFast, TensorType, is_flax_available, is_torch_available
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from transformers.testing_utils import require_flax, require_torch
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if is_flax_available():
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import os
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os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8
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import jax
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from transformers.models.bert.modeling_flax_bert import FlaxBertModel
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if is_torch_available():
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import torch
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from transformers.models.bert.modeling_bert import BertModel
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@require_flax
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@require_torch
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class FlaxBertModelTest(unittest.TestCase):
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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, f"Difference between torch and flax is {diff} (>= {tol})")
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def test_from_pytorch(self):
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with torch.no_grad():
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with self.subTest("bert-base-cased"):
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tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased")
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fx_model = FlaxBertModel.from_pretrained("bert-base-cased")
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pt_model = BertModel.from_pretrained("bert-base-cased")
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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).to_tuple()
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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):
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self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-3)
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def test_multiple_sequences(self):
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tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased")
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model = FlaxBertModel.from_pretrained("bert-base-cased")
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sequences = ["this is an example sentence", "this is another", "and a third one"]
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encodings = tokenizer(sequences, return_tensors=TensorType.JAX, padding=True, truncation=True)
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@jax.jit
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def model_jitted(input_ids, attention_mask=None, token_type_ids=None):
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return model(input_ids, attention_mask, token_type_ids)
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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tokens, pooled = model_jitted(**encodings)
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self.assertEqual(tokens.shape, (3, 7, 768))
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self.assertEqual(pooled.shape, (3, 768))
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with self.subTest("JIT Enabled"):
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jitted_tokens, jitted_pooled = model_jitted(**encodings)
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self.assertEqual(jitted_tokens.shape, (3, 7, 768))
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self.assertEqual(jitted_pooled.shape, (3, 768))
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