63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
# Copyright 2021 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 transformers import is_flax_available
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from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
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if is_flax_available():
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import optax
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from flax.training.common_utils import onehot
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from transformers import AutoTokenizer, FlaxMT5ForConditionalGeneration
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from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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@require_flax
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class MT5IntegrationTest(unittest.TestCase):
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@slow
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def test_small_integration_test(self):
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"""
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For comparision run:
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>>> import t5 # pip install t5==0.7.1
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>>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
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>>> path_to_mtf_small_mt5_checkpoint = '<fill_in>'
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>>> path_to_mtf_small_mt5_spm_model_path = '<fill_in>'
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>>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_mt5_checkpoint, batch_size=1, tpu=None)
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>>> vocab = SentencePieceVocabulary(path_to_mtf_small_mt5_spm_model_path)
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>>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
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"""
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model = FlaxMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
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tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
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input_ids = tokenizer("Hello there", return_tensors="np").input_ids
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labels = tokenizer("Hi I am", return_tensors="np").input_ids
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decoder_input_ids = shift_tokens_right(labels, model.config.pad_token_id, model.config.decoder_start_token_id)
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logits = model(input_ids, decoder_input_ids=decoder_input_ids).logits
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loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])).mean()
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mtf_score = -(labels.shape[-1] * loss.item())
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EXPECTED_SCORE = -84.9127
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self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)
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