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3042c63a95
* fix mess * better documentation * typo * fix doc * update * add test * fix test * more tests * Update src/transformers/modeling_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * move to utils * Apply suggestions from code review Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com> * nit --------- Co-authored-by: younesbelkada <younesbelkada@gmail.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Michael Benayoun <mickbenayoun@gmail.com>
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Team Inc.
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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 clone 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 tempfile
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import unittest
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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from transformers.testing_utils import (
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is_torch_available,
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require_optimum,
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require_torch,
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slow,
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)
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if is_torch_available():
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import torch
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@require_torch
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@require_optimum
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@slow
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class BetterTransformerIntegrationTest(unittest.TestCase):
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# refer to the full test suite in Optimum library:
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# https://github.com/huggingface/optimum/tree/main/tests/bettertransformer
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def test_transform_and_reverse(self):
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r"""
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Classic tests to simply check if the conversion has been successfull.
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"""
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model_id = "hf-internal-testing/tiny-random-t5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
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inp = tokenizer("This is me", return_tensors="pt")
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model = model.to_bettertransformer()
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self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()))
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output = model.generate(**inp)
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model = model.reverse_bettertransformer()
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self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()))
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model_reloaded = AutoModelForSeq2SeqLM.from_pretrained(tmpdirname)
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self.assertFalse(
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any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules())
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)
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output_from_pretrained = model_reloaded.generate(**inp)
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self.assertTrue(torch.allclose(output, output_from_pretrained))
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def test_error_save_pretrained(self):
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r"""
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The save_pretrained method should raise a ValueError if the model is in BetterTransformer mode.
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All should be good if the model is reversed.
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"""
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model_id = "hf-internal-testing/tiny-random-t5"
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model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
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model = model.to_bettertransformer()
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with tempfile.TemporaryDirectory() as tmpdirname:
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with self.assertRaises(ValueError):
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model.save_pretrained(tmpdirname)
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model = model.reverse_bettertransformer()
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model.save_pretrained(tmpdirname)
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