461 lines
19 KiB
Python
461 lines
19 KiB
Python
# coding=utf-8
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# Copyright 2020 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 unittest
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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, torch_device
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from .test_modeling_common import ids_tensor
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if is_torch_available():
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import torch
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import torch.nn.functional as F
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from transformers.generation_logits_process import (
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EncoderNoRepeatNGramLogitsProcessor,
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ForcedBOSTokenLogitsProcessor,
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ForcedEOSTokenLogitsProcessor,
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HammingDiversityLogitsProcessor,
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InfNanRemoveLogitsProcessor,
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LogitsProcessorList,
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MinLengthLogitsProcessor,
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NoBadWordsLogitsProcessor,
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NoRepeatNGramLogitsProcessor,
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PrefixConstrainedLogitsProcessor,
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RepetitionPenaltyLogitsProcessor,
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TemperatureLogitsWarper,
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TopKLogitsWarper,
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TopPLogitsWarper,
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)
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@require_torch
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class LogitsProcessorTest(unittest.TestCase):
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def _get_uniform_logits(self, batch_size: int, length: int):
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scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length
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return scores
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def test_min_lenght_dist_processor(self):
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vocab_size = 20
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batch_size = 4
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eos_token_id = 0
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min_dist_processor = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id)
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# check that min length is applied at length 5
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input_ids = ids_tensor((batch_size, 5), vocab_size=20)
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scores = self._get_uniform_logits(batch_size, vocab_size)
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scores_before_min_length = min_dist_processor(input_ids, scores)
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self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(), 4 * [-float("inf")])
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# check that min length is not applied anymore at length 15
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input_ids = ids_tensor((batch_size, 15), vocab_size=20)
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scores = self._get_uniform_logits(batch_size, vocab_size)
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scores_before_min_length = min_dist_processor(input_ids, scores)
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self.assertFalse(torch.isinf(scores_before_min_length).any())
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def test_temperature_dist_warper(self):
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input_ids = None
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length = 20
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scores = self._get_uniform_logits(batch_size=2, length=length)
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# tweak scores to not be uniform anymore
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scores[1, 5] = (1 / length) + 0.1 # peak, 1st batch
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scores[1, 10] = (1 / length) - 0.4 # valley, 1st batch
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# compute softmax
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probs = F.softmax(scores, dim=-1)
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temp_dist_warper_sharper = TemperatureLogitsWarper(temperature=0.5)
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temp_dist_warper_smoother = TemperatureLogitsWarper(temperature=1.3)
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warped_prob_sharp = F.softmax(temp_dist_warper_sharper(input_ids, scores.clone()), dim=-1)
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warped_prob_smooth = F.softmax(temp_dist_warper_smoother(input_ids, scores.clone()), dim=-1)
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# uniform distribution stays uniform
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self.assertTrue(torch.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3))
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self.assertTrue(torch.allclose(probs[0, :], warped_prob_smooth[0, :], atol=1e-3))
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# sharp peaks get higher, valleys get lower
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self.assertLess(probs[1, :].max(), warped_prob_sharp[1, :].max())
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self.assertGreater(probs[1, :].min(), warped_prob_sharp[1, :].min())
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# smooth peaks get lower, valleys get higher
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self.assertGreater(probs[1, :].max(), warped_prob_smooth[1, :].max())
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self.assertLess(probs[1, :].min(), warped_prob_smooth[1, :].min())
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def test_repetition_penalty_dist_process(self):
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input_ids = torch.tensor([[0, 1], [5, 0]], device=torch_device, dtype=torch.long)
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vocab_size = 10
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scores = self._get_uniform_logits(batch_size=2, length=vocab_size)
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# give values special values
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scores[0, 0] = -(1 / vocab_size)
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scores[1, 5] = 4 / vocab_size
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rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0)
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scores = rep_penalty_proc(input_ids, scores.clone())
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# check that values were correctly changed
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self.assertAlmostEqual(scores[0, 0].item(), -(1 / vocab_size) * 2)
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self.assertAlmostEqual(scores[0, 1].item(), (1 / vocab_size) / 2)
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self.assertAlmostEqual(scores[1, 0].item(), (1 / vocab_size) / 2)
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self.assertAlmostEqual(scores[1, 5].item(), (4 / vocab_size) / 2)
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def test_top_k_dist_warper(self):
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input_ids = None
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vocab_size = 10
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batch_size = 2
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# create ramp distribution
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ramp_logits = (
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torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1)
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)
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ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size
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top_k_warp = TopKLogitsWarper(3)
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scores = top_k_warp(input_ids, ramp_logits)
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# check that correct tokens are filtered
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self.assertListEqual(torch.isinf(scores[0]).tolist(), 7 * [True] + 3 * [False])
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self.assertListEqual(torch.isinf(scores[1]).tolist(), 2 * [True] + 3 * [False] + 5 * [True])
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# check special cases
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length = 5
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logits = self._get_uniform_logits(batch_size=batch_size, length=length)
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top_k_warp_safety_check = TopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3)
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scores = top_k_warp_safety_check(input_ids, logits)
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# uniform dist is not changed
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self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [0, 0])
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ramp_logits = torch.arange(length, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(batch_size, 1)
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scores = top_k_warp_safety_check(input_ids, ramp_logits)
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# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
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self.assertListEqual((scores == 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2])
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def test_top_p_dist_warper(self):
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input_ids = None
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vocab_size = 10
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batch_size = 2
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# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
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dist = torch.log(
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torch.tensor([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float)
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)
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top_p_warp = TopPLogitsWarper(0.7)
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filtered_dist = torch.exp(top_p_warp(input_ids, dist))
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# dist should be filtered to keep min num values so that sum is >= 0.7
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# exp (-inf) => 0
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EXPECTED_FILTERED_DIST = torch.tensor(
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[[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]], device=torch_device, dtype=torch.float
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)
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self.assertTrue(torch.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3))
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# check edge cases with negative and extreme logits
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ramp_logits = torch.arange(vocab_size, device=torch_device, dtype=torch.float).unsqueeze(0).repeat(
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batch_size, 1
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) - (vocab_size // 2)
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# make ramp_logits more extreme
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ramp_logits[1] = ramp_logits[1] * 100.0
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# make sure at least 2 tokens are kept
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top_p_warp = TopPLogitsWarper(0.9, min_tokens_to_keep=2, filter_value=0.0)
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filtered_dist = top_p_warp(input_ids, ramp_logits)
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# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
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self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [3, 2])
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def test_no_repeat_ngram_dist_processor(self):
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vocab_size = 3
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batch_size = 2
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input_ids = torch.tensor([[1, 1, 2, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
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scores = self._get_uniform_logits(batch_size, vocab_size)
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no_repeat_proc_2_gram = NoRepeatNGramLogitsProcessor(2)
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no_repeat_proc_3_gram = NoRepeatNGramLogitsProcessor(3)
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filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone())
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filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone())
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# 2-gram would forbid 2nd and 3rd token (1,2) at 1st batch and 1st token (0) at 2nd batch
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self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [True, False, False]])
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# 3-gram would forbid no token at 1st batch and 1st token (0) at 2nd batch
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self.assertListEqual(
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torch.isinf(filtered_scores_3_gram).tolist(), [[False, False, False], [True, False, False]]
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)
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def test_encoder_no_repeat_ngram_dist_processor(self):
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vocab_size = 3
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num_beams = 2
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batch_size = 1
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encoder_input_ids = torch.tensor([1, 2, 1, 1], device=torch_device, dtype=torch.long)
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input_ids = torch.tensor([[1, 2, 1], [8, 0, 2]], device=torch_device, dtype=torch.long)
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scores = self._get_uniform_logits(batch_size * num_beams, vocab_size)
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no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids)
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no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids)
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filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone())
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filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone())
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# 2-gram would forbid 1st and 2nd token at 1st beam and 1st token (0) at 2nd beam
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self.assertListEqual(torch.isinf(filtered_scores_2_gram).tolist(), [[False, True, True], [False, True, False]])
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# 3-gram would forbid 1st token at 1st beam and no token at 2nd beam
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self.assertListEqual(
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torch.isinf(filtered_scores_3_gram).tolist(), [[False, True, False], [False, False, False]]
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)
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# Batched input
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vocab_size = 3
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num_beams = 2
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batch_size = 2
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encoder_input_ids = torch.tensor([[1, 2, 1, 1], [0, 0, 2, 1]], device=torch_device, dtype=torch.long)
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input_ids = torch.tensor([[1, 2, 1], [1, 0, 2], [0, 0, 0], [0, 2, 2]], device=torch_device, dtype=torch.long)
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scores = self._get_uniform_logits(batch_size * num_beams, vocab_size)
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no_repeat_proc_2_gram = EncoderNoRepeatNGramLogitsProcessor(2, encoder_input_ids=encoder_input_ids)
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no_repeat_proc_3_gram = EncoderNoRepeatNGramLogitsProcessor(3, encoder_input_ids=encoder_input_ids)
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filtered_scores_2_gram = no_repeat_proc_2_gram(input_ids, scores.clone())
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filtered_scores_3_gram = no_repeat_proc_3_gram(input_ids, scores.clone())
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# 2gram
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# Batch 1
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# - Beam 1: tokens (1, 2) forbidden
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# - Beam 2: tokens (1) forbidden
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# Batch 2
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# - Beam 1: tokens (0, 2) forbidden
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# - Beam 2: tokens (1) forbidden
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self.assertListEqual(
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torch.isinf(filtered_scores_2_gram).tolist(),
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[[False, True, True], [False, True, False], [True, False, True], [False, True, False]],
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)
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# Batch 1
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# - Beam 1: tokens (1) forbidden
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# - Beam 2: tokens () forbidden
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# Batch 2
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# - Beam 1: tokens (2) forbidden
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# - Beam 2: tokens () forbidden
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self.assertListEqual(
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torch.isinf(filtered_scores_3_gram).tolist(),
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[[False, True, False], [False, False, False], [False, False, True], [False, False, False]],
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)
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def test_no_bad_words_dist_processor(self):
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vocab_size = 5
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batch_size = 2
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eos_token_id = 4
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input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
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bad_word_tokens = [[1], [4], [1, 0], [0, 1, 2], [1, 3, 1, 3]]
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scores = self._get_uniform_logits(batch_size, vocab_size)
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no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=bad_word_tokens, eos_token_id=eos_token_id)
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filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone())
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# batch 1: 1st, 2nd, and 4th (0, 1, 3) token are forbidden
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# batch 2: 1st, 2nd, and 3rd (0, 1, 2) token are forbidden
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# Note that 5th element cannot be forbidden as it is EOS token
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self.assertListEqual(
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torch.isinf(filtered_scores).tolist(), [[True, True, False, True, False], [True, True, True, False, False]]
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)
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# check edge case
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no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[4]], eos_token_id=eos_token_id)
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filtered_scores = no_bad_words_dist_proc(input_ids, scores.clone())
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self.assertTrue(torch.allclose(scores, filtered_scores, atol=1e-3))
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def test_processor_list(self):
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batch_size = 4
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sequence_length = 10
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vocab_size = 15
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eos_token_id = 0
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# dummy input_ids and scores
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input_ids = ids_tensor((batch_size, sequence_length), vocab_size)
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input_ids_comp = input_ids.clone()
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scores = self._get_uniform_logits(batch_size, vocab_size)
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scores_comp = scores.clone()
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# instantiate all dist processors
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min_dist_proc = MinLengthLogitsProcessor(min_length=10, eos_token_id=eos_token_id)
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temp_dist_warp = TemperatureLogitsWarper(temperature=0.5)
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rep_penalty_proc = RepetitionPenaltyLogitsProcessor(penalty=2.0)
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top_k_warp = TopKLogitsWarper(3)
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top_p_warp = TopPLogitsWarper(0.8)
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no_repeat_proc = NoRepeatNGramLogitsProcessor(2)
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no_bad_words_dist_proc = NoBadWordsLogitsProcessor(bad_words_ids=[[1]], eos_token_id=eos_token_id)
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# no processor list
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scores = min_dist_proc(input_ids, scores)
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scores = temp_dist_warp(input_ids, scores)
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scores = rep_penalty_proc(input_ids, scores)
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scores = top_k_warp(input_ids, scores)
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scores = top_p_warp(input_ids, scores)
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scores = no_repeat_proc(input_ids, scores)
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scores = no_bad_words_dist_proc(input_ids, scores)
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# with processor list
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processor = LogitsProcessorList(
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[
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min_dist_proc,
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temp_dist_warp,
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rep_penalty_proc,
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top_k_warp,
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top_p_warp,
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no_repeat_proc,
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no_bad_words_dist_proc,
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]
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)
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scores_comp = processor(input_ids, scores_comp)
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# scores should be equal
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self.assertTrue(torch.allclose(scores, scores_comp, atol=1e-3))
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# input_ids should never be changed
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self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist())
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def test_prefix_constrained_logits_processor(self):
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vocab_size = 5
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batch_size = 2
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input_ids = torch.tensor([[0, 1, 3, 1], [0, 1, 0, 1]], device=torch_device, dtype=torch.long)
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scores = self._get_uniform_logits(batch_size, vocab_size)
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def prefix_allowed_tokens_fn(batch_id, inputs_ids):
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return [[0, 1], [2, 3]][batch_id]
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prefix_constrained_logits_proc = PrefixConstrainedLogitsProcessor(prefix_allowed_tokens_fn, 1)
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filtered_scores = prefix_constrained_logits_proc(input_ids, scores.clone())
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# batch 1: 1st, 2nd (0, 1) token are allowed
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# batch 2: 3rd, 4th (2, 3) token are allowed
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self.assertListEqual(
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torch.isinf(filtered_scores).tolist(), [[False, False, True, True, True], [True, True, False, False, True]]
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)
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def test_hamming_diversity(self):
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vocab_size = 4
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num_beams = 2
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num_beam_groups = 2
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scores = self._get_uniform_logits(num_beams, vocab_size)
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# batch_idx = 0 -> index batch_idx * num_beam_groups -> idx = 0 * 2 = 0 -> penalises tokens 1
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# batch_idx = 1 -> index batch_idx * num_beam_groups -> idx = 1 * 2 = 2 -> penalises tokens 1
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current_tokens = torch.tensor([0, 3, 1, 2], device=torch_device, dtype=torch.long)
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diversity_logits_processor = HammingDiversityLogitsProcessor(
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diversity_penalty=1.0, num_beams=num_beams, num_beam_groups=num_beam_groups
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)
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processed_scores = diversity_logits_processor(None, scores, current_tokens, 1)
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self.assertTrue(
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torch.allclose(
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processed_scores[0], torch.tensor([-0.7500, 0.2500, 0.2500, 0.2500], device=torch_device), atol=1e-3
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)
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)
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self.assertTrue(
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torch.allclose(
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processed_scores[1], torch.tensor([0.2500, -0.7500, 0.2500, 0.2500], device=torch_device), atol=1e-3
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)
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)
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def test_forced_bos_token_logits_processor(self):
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vocab_size = 20
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batch_size = 4
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bos_token_id = 0
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logits_processor = ForcedBOSTokenLogitsProcessor(bos_token_id=bos_token_id)
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# check that all scores are -inf except the bos_token_id score
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input_ids = ids_tensor((batch_size, 1), vocab_size=20)
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scores = self._get_uniform_logits(batch_size, vocab_size)
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scores = logits_processor(input_ids, scores)
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self.assertTrue(torch.isneginf(scores[:, bos_token_id + 1 :]).all())
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self.assertListEqual(scores[:, bos_token_id].tolist(), 4 * [0]) # score for bos_token_id shold be zero
|
|
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|
# check that bos_token_id is not forced if current length is greater than 1
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input_ids = ids_tensor((batch_size, 4), vocab_size=20)
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|
scores = self._get_uniform_logits(batch_size, vocab_size)
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|
scores = logits_processor(input_ids, scores)
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self.assertFalse(torch.isinf(scores).any())
|
|
|
|
def test_forced_eos_token_logits_processor(self):
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vocab_size = 20
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|
batch_size = 4
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|
eos_token_id = 0
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max_length = 5
|
|
|
|
logits_processor = ForcedEOSTokenLogitsProcessor(max_length=max_length, eos_token_id=eos_token_id)
|
|
|
|
# check that all scores are -inf except the eos_token_id when max_length is reached
|
|
input_ids = ids_tensor((batch_size, 4), vocab_size=20)
|
|
scores = self._get_uniform_logits(batch_size, vocab_size)
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|
scores = logits_processor(input_ids, scores)
|
|
self.assertTrue(torch.isneginf(scores[:, eos_token_id + 1 :]).all())
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|
self.assertListEqual(scores[:, eos_token_id].tolist(), 4 * [0]) # score for eos_token_id should be zero
|
|
|
|
# check that eos_token_id is not forced if max_length is not reached
|
|
input_ids = ids_tensor((batch_size, 3), vocab_size=20)
|
|
scores = self._get_uniform_logits(batch_size, vocab_size)
|
|
scores = logits_processor(input_ids, scores)
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|
self.assertFalse(torch.isinf(scores).any())
|
|
|
|
def test_remove_nan_inf_logits_processor(self):
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|
scores = torch.tensor(
|
|
[[0.0, 0.7, 0.8, float("nan")], [0.1, float("inf"), 0.3, float("-inf")]], device=torch_device
|
|
)
|
|
input_ids = ids_tensor((2, 4), vocab_size=20)
|
|
|
|
logits_processor = InfNanRemoveLogitsProcessor()
|
|
|
|
scores = logits_processor(input_ids, scores)
|
|
|
|
self.assertTrue(
|
|
torch.allclose(
|
|
scores,
|
|
torch.tensor(
|
|
[[0.0, 0.7, 0.8, 0.0], [0.1, torch.finfo(scores.dtype).max, 0.3, float("-inf")]],
|
|
device=torch_device,
|
|
),
|
|
atol=1e-6,
|
|
)
|
|
)
|