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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import random
import unittest
import timeout_decorator
from ..file_utils import cached_property, is_torch_available
from ..testing_utils import require_torch
if is_torch_available():
import torch
from ..models.marian import MarianConfig, MarianMTModel
@require_torch
class GenerationUtilsTest(unittest.TestCase):
@cached_property
def config(self):
config = MarianConfig.from_pretrained("sshleifer/tiny-marian-en-de")
return config
@cached_property
def model(self):
return MarianMTModel(self.config)
def test_postprocess_next_token_scores(self):
config = self.config
model = self.model
# Initialize an input id tensor with batch size 8 and sequence length 12
input_ids = torch.arange(0, 96, 1).view((8, 12))
eos = config.eos_token_id
bad_words_ids_test_cases = [[[299]], [[23, 24], [54]], [[config.eos_token_id]], []]
masked_scores = [
[(0, 299), (1, 299), (2, 299), (3, 299), (4, 299), (5, 299), (6, 299), (7, 299)],
[(1, 24), (0, 54), (1, 54), (2, 54), (3, 54), (4, 54), (5, 54), (6, 54), (7, 54)],
[(0, eos), (1, eos), (2, eos), (3, eos), (4, eos), (5, eos), (6, eos), (7, eos)],
[],
]
for test_case_index, bad_words_ids in enumerate(bad_words_ids_test_cases):
# Initialize a scores tensor with batch size 8 and vocabulary size 300
scores = torch.rand((8, 300))
output = model.postprocess_next_token_scores(
scores,
input_ids,
0,
bad_words_ids,
13,
15,
config.max_length,
config.eos_token_id,
config.repetition_penalty,
32,
5,
)
for masked_score in masked_scores[test_case_index]:
self.assertTrue(output[masked_score[0], masked_score[1]] == -float("inf"))
@timeout_decorator.timeout(10)
def test_postprocess_next_token_scores_large_bad_words_list(self):
config = self.config
model = self.model
# Initialize an input id tensor with batch size 8 and sequence length 12
input_ids = torch.arange(0, 96, 1).view((8, 12))
bad_words_ids = []
for _ in range(100):
length_bad_word = random.randint(1, 4)
bad_words_ids.append(random.sample(range(1, 300), length_bad_word))
scores = torch.rand((8, 300))
_ = model.postprocess_next_token_scores(
scores,
input_ids,
0,
bad_words_ids,
13,
15,
config.max_length,
config.eos_token_id,
config.repetition_penalty,
32,
5,
)
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